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Resonant Inductive Power Transmission (RIPT) is an advanced Wireless Power Transfer (WPT) technology, emerging as a secure and efficient solution for Electric Vehicle (EV) charging. Although Dynamic Wireless Charging Systems (DWCS) reduce the need for large batteries compared to static charging, they require higher initial investments. This study introduces a novel approach utilizing LCC-S compensation to achieve load-invariant Constant Current (CC) and Constant Voltage (CV) outputs at two distinct Zero Phase Angle (ZPA) frequencies in DWCS, leveraging a half-bridge-based multi-legged inverter configuration. The article presents a new method with fewer iterations to determine the optimal compensation parameters, ensuring that both the CC and CV outputs with ZPA comply with the SAE J2954 frequencies standards. The half-bridge-based DWCS with LCC-S compensation is designed to operate in either CC or CV mode, depending on whether the application involves static or dynamic charging. A 1-kW LCC-S compensated DWCS prototype was developed and built to verify and support the theoretical findings and analysis. This study offers valuable insights for optimizing RIPT technology, making EV charging more efficient and cost-effective.

The optimization of photovoltaic (PV) systems is vital for enhancing efficiency and economic viability, especially under Partial Shading Conditions (PSCs). This study focuses on the development and comparison of traditional and advanced algorithms, including Perturb and Observe (P&O), Incremental Conductance (IC), Fuzzy Logic Control (FLC), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Artificial Neural Networks (ANN), for efficient Maximum Power Point Tracking (MPPT). Simulations conducted in the MATLAB/Simulink software package evaluated these algorithms’ performances under various shading scenarios. The results indicate that, while traditional methods like P&O and IC are effective under uniform conditions, advanced techniques, particularly ANN-based MPPT, exhibit superior efficiency and faster convergence under PSCs. This study concludes that integrating Artificial Intelligence (AI) and Machine Learning (ML) into MPPT algorithms significantly enhances the reliability and efficiency of PV systems, paving the way for a broader adoption of solar energy technologies in diverse environmental conditions. These findings contribute to advancing renewable energy technology and supporting green energy transition.

Asynchronous interconnection is essential for integrating AC networks operating at different frequencies, typically 50 Hz and 60 Hz. This need arises from distributed power generation methods, including offshore renewable sources and diverse regional grid configurations. Advanced strategies are required to overcome these frequency differences and ensure uninterrupted power transfer.High-Voltage Direct Current (HVDC) transmission systems facilitate efficient power exchange, enhancing grid reliability and stability. This study focuses on optimizing the Proportional-plus-Integral (PI) controller parameters within a 20 MVA Voltage Source Converters (VSC)-based HVDC system to enable asynchronous interconnection between offshore and onshore AC networks. The offshore VSC regulates active and reactive power, while the onshore VSC controls DC voltage and reactive power. A vector control approach with symmetric optimum PI tuning is proposed for a comprehensive performance assessment of the VSC-based HVDC transmission system.The effectiveness of the tuned PI controller parameters is evaluated through four test cases using MATLAB/Simulink for offline simulation and Typhoon HIL604 for real-time validation. These cases involve abrupt changes in reference active and reactive power for the offshore VSC; and in reference reactive power and DC voltage for the onshore VSC. Results demonstrate rapid and satisfactory dynamic performance across all test cases, as evidenced by offline simulations and real-time validation.The validation highlights the effectiveness of the proposed control design with symmetric optimum PI tuning, confirming its ability to enhance the overall performance of the HVDC transmission system for efficient asynchronous interconnection.

Electric vehicle chargers are categorized into high-voltage DC (HVDC) systems for charging power batteries and low-voltage DC (LVDC) systems for low-voltage batteries. As next-generation EVs evolve, the demand for chargers with higher power ratings, greater power density, and lower losses, complicates system design. The rising load requirements necessitate a compact, lightweight, and efficient design, highlighting the importance of effective EV design for both industry and academia. This research presents a novel examination of Dual-Input Dual-Output DC-DC Boost, Buck, and Buck-Boost Converters, which are intended to transform a DC voltage input into several DC output voltage levels suitable for EVs. Current multi-port converters often encounter challenges such as limitations on duty ratios, inductor charging currents, output voltage restrictions, and cross-regulation problems during load fluctuations. This study introduces a multi-port DC-DC converter with Multi-Input Multi-Output (MIMO) capabilities that overcome these challenges, allowing for two distinct output voltages without restrictions on duty cycles or inductor currents. The proposed design resolves cross-regulation issues, ensuring that the output voltages V01 and V02 are stable and unaffected by changes in output currents i02 and i01. The outputs operate independently without influencing each other during use, and the loads are kept isolated during control processes. In this paper, the operating principle of the proposed converter is analyzed with the small-signal model, the average model of the converter is derived, and the transfer function of the proposed converter is further obtained. To ensure the response speed and stability of the converter, the voltage and current for closed-loop control are adopted, with which classical PI regulators are utilized and designed. The effectiveness of the proposed converter has been validated through a 1kW and 500W prototype, which successfully provides two output voltages of 150V and 30V, with the potential for additional outputs.

Self-Driving Vehicles (SDVs) are increasingly popular, with companies like Google, Uber, and Tesla investing significantly in self-driving technology. These vehicles could transform commuting, offering safer, and efficient transport. A key SDV aspect is motion planning, generating secure, and efficient routes. This ensures safe navigation and prevents collisions with obstacles, pedestrians, and other vehicles. Deep Learning (DL) could aid SDV motion planning. AI tools and algorithms, like Artificial Neural Networks (ANNs), Machine Learning (ML) and DL can learn from data to create effective driving strategies, enhancing SDV adaptability to changing conditions for improved safety and efficiency. This survey gives a DL-based motion planning overview for SDVs, covering behaviour planning, trajectory planning, and End to End Learning (E2EL). It assesses various DL-based behaviour and trajectory planning methods, comparing and summarizing them. It also reviews diverse E2EL techniques including Imitation Learning (IL) and Reinforcement Learning (RL) gaining traction lately. Additionally, this review emphasizes the significance of two crucial enablers: datasets and simulation deployment frameworks for SDVs. The survey compares strategies using multiple metrics and highlights DL-based SDV implementation challenges, including simulation and real-world use cases. This article also suggests future research directions to address E2EL and DL-based motion planning limitations. The presented article is an excellent reference for scholars, engineers, and decision-makers who have an interest in DL-based SDV motion planning.

In recent times, the increasing demand for energy consumption on a global scale and the prevalent use of power electronics DC-DC converters in various applications, such as hybrid energy systems, hybrid vehicles, aerospace, satellites, and portable electronic devices. To increase the converters’ dependability, effectiveness, adaptability, and cost-effectiveness, they have undergone substantial research and development. Diverse input levels can be merged for diverse output levels using new converter topologies, which increase their possibilities from single input-single output setups to multi-input-multiple configurations. The goal of ongoing research is to lower costs and component counts while raising efficiency and dependability. This review paper focuses on the analysis of non-isolated converters, especially for SI-MIMO topologies produced from different converter types. The research work, also includes control techniques and methods like proportional integral derivative, sliding mode control, model predictive control, state-space modelling, fuzzy logic control, and maximum power point tracking, which are all integrated with non-isolated DC-DC converters while considering things like settling concerns, response time, and complexity.

Demand Side Management (DSM) implies intelligently managing load appliances in a Smart Grid (SG). DSM programs help customers save money by reducing their electricity bills, minimizing the utility’s peak demand, and improving load factor. To achieve these goals, this paper proposes a new load shifting-based optimal DSM model for scheduling residential users’ appliances. The proposed system effectively handles the challenges raised in the literature regarding the absence of using recent, easy, and more robust optimization techniques, a comparison procedure with well-established ones, using Renewable Energy Resources (RERs), Renewable Energy Storage (RES), and adopting consumer comfort. This system uses recent algorithms called Virulence Optimization Algorithm (VOA) and Earth Worm Optimization Algorithm (EWOA) for optimally shifting the time slots of shiftable appliances. The system adopts RERs, RES, as well as utility grid energy for supplying load appliances. This system takes into account user preferences, timing factors for each appliance, and a pricing signal for relocating shiftable appliances to flatten the energy demand profile. In order to figure out how much electricity users will have to pay, a Time Of Use (TOU) dynamic pricing scheme has been used. Using MATLAB simulation environment, we have made effectiveness-based comparisons of the adopted optimization algorithms with the well-established meta-heuristics and evolutionary algorithms (Genetic Algorithm (GA), Cuckoo Search Optimization (CSO), and Binary Particle Swarm Optimization (BPSO) in order to determine the most efficient one. Without adopting RES, the results indicate that VOA outperforms the other algorithms. The VOA enables 59% minimization in Peak-to-Average Ratio (PAR) of consumption energy and is more robust than other competitors. By incorporating RES, the EWOA, alongside the VOA, provides less deviation and a lower PAR. The VOA saves 76.19% of PAR, and the EWOA saves 73.8%, followed by the BPSO, GA, and CSO, respectively. The electricity consumption using VOA and EWOA-based DSM cost 217 and 210 USD cents, respectively, whereas non-scheduled consumption costs 273 USD cents and scheduling based on BPSO, GA, and CSO costs 219, 220, and 222 USD cents.

The climate crisis necessitates a global shift to achieve a secure, sustainable, and affordable energy system toward a green energy transition reaching climate neutrality by 2050. Because of this, renewable energy sources have come to the forefront, and the research interest in microgrids that rely on distributed generation and storage systems has exploded. Furthermore, many new markets for energy trading, ancillary services, and frequency reserve markets have provided attractive investment opportunities in exchange for balancing the supply and demand of electricity. Artificial intelligence can be utilized to locally optimize energy consumption, trade energy with the main grid, and participate in these markets. Reinforcement learning (RL) is one of the most promising approaches to achieve this goal because it enables an agent to learn optimal behavior in a microgrid by executing specific actions that maximize the long-term reward signal/function. The study focuses on testing two optimization algorithms: logic-based optimization and reinforcement learning. This paper builds on the existing research framework by combining PPO with machine learning-based load forecasting to produce an optimal solution for an industrial microgrid in Norway under different pricing schemes, including day-ahead pricing and peak pricing. It addresses the peak shaving and price arbitrage challenges by taking the historical data into the algorithm and making the decisions according to the energy consumption pattern, battery characteristics, PV production, and energy price. The RL-based approach is implemented in Python based on real data from the site and in combination with MATLAB-Simulink to validate its results. The application of the RL algorithm achieved an average monthly cost saving of 20% compared with logic-based optimization. These findings contribute to digitalization and decarbonization of energy technology, and support the fundamental goals and policies of the European Green Deal.

Solar photovoltaic (PV) systems are susceptible to power loss caused by environmental factors such as partial shading and temperature changes. To address this issue, PV modules are connected in array configurations. However, these configurations can lead to mismatch losses between the PV rows, which reduce power output. While there are many solutions to mitigate these losses, the performance of each solution can vary depending on the environmental conditions and the array configuration logic. This research paper evaluates the performance of fifteen existing static PV array configuration techniques under various shading patterns. We analyze the mathematical formulation and logic used behind each configuration, as well as the shade dispersion rate, power generation, power losses, advantages, and disadvantages. Our analysis includes a MATLAB/SimulinkⓇ model of a $5\times 5$ array for each configuration under different shading patterns. The performance of consistent and best configurations is also evaluated in a real-time environment. The results categorize each configuration as consistent, best, average, or poor. This paper provides a detailed analysis of the different PV array configurations and their performance, which can help in selecting the optimal configuration for specific environmental conditions.

This paper presents a novel high-gain non-isolated Four Port DC-DC converter (FPC) topology for hybrid energy applications. The proposed four-port dc-dc converter interfaces four power ports of three input ports and one output port. High output voltage gain, minimized component count, and high efficiency are the advantages of the proposed converter, which renders it very useful for hybrid energy applications such as fast-charging electric vehicles. The high output voltage gain of the proposed FPC is achieved by the Actively Switched Inductor Capacitor (ALC) network. Moreover, the reduction in component count is achieved by minimizing the number of switches per input port. This enhances the converter efficiency is 96.77 % with a voltage gain 4.75. In this study, the FPC topology and modes of operation are described along with complete, steady state analysis, loss analysis, and switch voltage stress (VS) analysis of the converter. The converter operates in five modes, where all three sources can either be interfaced with the load by simply altering the switching patterns. Circuit topology, modes of operation, circuit analysis simulation, and real-time validations have been described in this paper.

The second-generation hybrid and Electric Vehicles are currently leading the paradigm shift in the automobile industry, replacing conventional diesel and gasoline-powered vehicles. The Battery Management System is crucial in these electric vehicles and also essential for renewable energy storage systems. This review paper focuses on batteries and addresses concerns, difficulties, and solutions associated with them. It explores key technologies of Battery Management System, including battery modeling, state estimation, and battery charging. A thorough analysis of numerous battery models, including electric, thermal, and electro-thermal models, is provided in the article. Additionally, it surveys battery state estimations for a charge and health. Furthermore, the different battery charging approaches and optimization methods are discussed. The Battery Management System performs a wide range of tasks, including as monitoring voltage and current, estimating charge and discharge, equalizing and protecting the battery, managing temperature conditions, and managing battery data. It also looks at various cell balancing circuit types, current and voltage stressors, control reliability, power loss, efficiency, as well as their advantages and disadvantages. The paper also discusses research gaps in battery management systems.

The agriculture sector has witnessed a transformation with the advent of smart sensing devices, leading to improved crop yield and quality. However, the management of data collection from numerous sensors across vast agricultural areas, as well as the associated charging requirements, presents significant challenges. This paper addresses the major research problem by proposing an innovative solution for charging agricultural sensors. The introduction of an energy-constrained device (ECD) enables wireless charging and transmission of soil data to a centralized server. The proposed ECDs will enable enhanced data collection, precision agriculture, optimized resource allocation, timely decision-making, and remote monitoring and control. A bidirectional wireless charging drone is employed to efficiently charge the ECDs. To optimize energy usage, a prioritized Dijkstra algorithm determines the ECDs to be charged and plans the shortest route for the drone. The wireless charging drone landing-charging station achieves an efficiency of 91.3%, delivering 72 W of power within a 5 mm range. Furthermore, the ECD possesses a data transmission range of 100 m and incorporates deep sleep functionality, allowing for a remarkable 30-day battery life.

The development of EV technology and EV migration is limited by various factors such as sizing of batteries, short driving ranges, optimal operations, and so on. The EV charging faces many difficulties such as waiting time, charging time, uneven charge scheduling, and uneven distributed charging stations. In Charging Station (CS), EVs usually spend much time in queues, mainly during peak hours of charging. Therefore, building a well-established charging station network should be derived from charging demand and proper charge scheduling to assist EVs for getting charged with less cost, less waiting time. Also, it should reduce the number of vehicles scheduling during the peak load time. This work aims to design a scheduling system for EV charging using an optimization strategy of Chaotic Harris Hawks optimization (CHHO) which reduces the total time spent on charging station and the distance of EV origin to destination. CHHO is authenticated using Vehicular Ad-hoc Network (VANET) simulation, and the performances are compared with algorithms Exponential Harris Hawk Optimization, Grey Wolf Optimizer, First in First Out and Random Allocation to demonstrate the efficacy of our technique. The proposed CHHO-based scheduling system yields better performance with the maximum remaining energy and significantly cuts the average travel time, and improves the utilization rate of EVs in charging stations compared to other algorithms. A detailed result and discussions on different case studies by varying number of vehicles and number of charging stations and the corresponding average waiting time were obtained and presented in this paper.

Integrating nanotechnology in dielectric fluid significantly inhibits losses and boosts overall dielectric fluid performance. There has been research done on the effects of introducing various nanoparticles, such as titania, alumina, silica nanodiamonds, etc. In this paper, a novel nanoparticle, Ceria (CeO2), has been used, and its properties were examined using the FTIR (Fourier Transform Infrared) spectrum, the XRD (X-ray Diffraction) spectrum, the SEM (Scanning Electron Microscopy), and the TEM (Transmission Electron Microscopy). This paper illustrates an efficient dielectric fluid prepared by the successful dispersion of Cerium Oxide (CeO2) nanoparticles in various concentrations into four commercial oils, namely mineral oil, rapeseed oil, synthetic ester oil, and soybean oil, to enhance and improve their dielectric characteristics. The performance investigation emphasises breakdown strength enhancement and other dielectric properties of the colloidal solution comprising different nanoparticle (NP) concentrations. Various commercial oils are used as a base in nano-oil to diversify their applicability as dielectric fluids by measuring the correlation in dielectric parameters and statistically assessing their applicability with normal and Weibull distributions. The obtained experimental data sets were analyzed using the Statistics and Machine Learning Toolbox in MATLAB. The aging measurement has been done only on mineral oil, and results were matched using a predictive model of statistics and the Machine Learning Toolbox in MATLAB. Well-dispersed CeO2 NPs in the insulating oils lead to a significant increase in AC breakdown strength. The effect of ageing on the dielectric properties of nano oils yields better results than conventionally aged oil. It has been observed that the breakdown voltage is enhanced by up to 30% for mineral oil at an optimal concentration of 0.01 g/L, 9% for synthetic ester oil at 0.03 g/L, 18% for rapeseed oil at 0.02 g/L, and 19% for soybean oil at 0.03 g/L nanoparticle concentration. Following the dispersion of CeO2 nanoparticles, the dielectric constant of all insulating oils has also significantly improved.The overall experimental results are promising and show the potential of the CeO2 NPs-based nano oil as an efficient and highly performing dielectric oil for different power applications.

Electric vehicles (EVs) are playing a vital role in sustainable transportation. It is estimated that by 2030, Battery EVs will become mainstream for passenger car transportation. Even though EVs are gaining interest in sustainable transportation, the future of EV power transmission is facing vital concerns and open research challenges. Considering the case of torque ripple mitigation and improved reliability control techniques in motors, many motor drive control algorithms fail to provide efficient control. To efficiently address this issue, control techniques such as Field Orientation Control (FOC), Direct Torque Control (DTC), Model Predictive Control (MPC), Sliding Mode Control (SMC), and Intelligent Control (IC) techniques are used in the motor drive control algorithms. This literature survey exclusively compares the various advanced control techniques for conventionally used EV motors such as Permanent Magnet Synchronous Motor (PMSM), Brushless Direct Current Motor (BLDC), Switched Reluctance Motor (SRM), and Induction Motors (IM). Furthermore, this paper discusses the EV-motors history, types of EV-motors, EV-motor drives mathematical modelling, and design procedure of EV-motors. The hardware results have also been compared with different control techniques for BLDC hub and SRM. Future direction towards the design of EVs by a critical selection of motors and their control techniques to minimize the torque ripple and other research opportunities to enhance the performance of EVs are also presented.

Brushless direct current (BLDC) motors are mostly preferred for dynamic applications such as automotive industries, pumping industries, and rolling industries. It is predicted that by 2030, BLDC motors will become mainstream of power transmission in industries replacing traditional induction motors. Though the BLDC motors are gaining interest in industrial and commercial applications, the future of BLDC motors faces indispensable concerns and open research challenges. Considering the case of reliability and durability, the BLDC motor fails to yield improved fault tolerance capability, reduced electromagnetic interference, reduced acoustic noise, reduced flux ripple, and reduced torque ripple. To address these issues, closed-loop vector control is a promising methodology for BLDC motors. In the literature survey of the past five years, limited surveys were conducted on BLDC motor controllers and designing. Moreover, vital problems such as comparison between existing vector control schemes, fault tolerance control improvement, reduction in electromagnetic interference in BLDC motor controller, and other issues are not addressed. This encourages the author in conducting this survey of addressing the critical challenges of BLDC motors. Furthermore, comprehensive study on various advanced controls of BLDC motors such as fault tolerance control, Electromagnetic interference reduction, field orientation control (FOC), direct torque control (DTC), current shaping, input voltage control, intelligent control, drive-inverter topology, and its principle of operation in reducing torque ripples are discussed in detail. This paper also discusses BLDC motor history, types of BLDC motor, BLDC motor structure, Mathematical modeling of BLDC and BLDC motor standards for various applications.

The novel coronavirus (nCOV) is a new strain that needs to be hindered from spreading by taking effective preventive measures as swiftly as possible. Timely forecasting of COVID-19 cases can ultimately support in making significant decisions and planning for implementing preventive measures. In this study, three common machine learning (ML) approaches via linear regression (LR), sequential minimal optimization (SMO) regression, and M5P techniques have been discussed and implemented for forecasting novel coronavirus disease-2019 (COVID-19) pandemic scenarios. To demonstrate the forecast accuracy of the aforementioned ML approaches, a preliminary sample-study has been conducted on the first wave of the COVID-19 pandemic scenario for three different countries including the United States of America (USA), Italy, and Australia. Furthermore, the contributions of this study are extended by conducting an in-depth forecast study on COVID-19 pandemic scenarios for the first, second, and third waves in India. An accurate forecasting model has been proposed, which has been constructed on the basis of the results of the aforementioned forecasting models of COVID-19 pandemic scenarios. The findings of the research highlight that LR is a potential approach that outperforms all other forecasting models tested herein in the present COVID-19 pandemic scenario. Finally, the LR approach has been used to forecast the likely onset of the fourth wave of COVID-19 in India.

This manuscript focuses on a multi-port non-isolated (Dual input and single output) DC/DC power electronic interface based on Z-Quasi Resonant (ZQR) network. The converter accommodates grid and Photovoltaic panel (PV) as its input sources. Unlike the basic DC/DC converters, the recommended DC/DC converter requires fewer switches and provides continuous current, high gain in voltage, and minimal voltage stress on converter switch up to 40% duty cycle owing to the presence of ZQR network. This feature of the converter makes it to find its application in Electric Vehicle (EV) off-board charging where high voltage gain is required. In the proposed multi-port ZQR converter, additional input and output ports could be appended without compromising the converter’s gain and efficiency. The developed converter can operate continuously even if any one of the input sources fails to charge the EV. The proposed converter is mathematically modeled using basic laws that govern the converter performance and analyzed in MATLAB simulink platform under various operating modes. A detailed analysis under steady-state, dynamic conditions and a comparison of the developed multiport ZQR DC/DC converter with the topologies addressed in published literature are also presented in this manuscript. In order to verify the proposed converter performance, a prototype model of 300 W has been fabricated with switching frequency of 20 kHz. Experimental results confirm the effectiveness of the theoretical analysis, the aforementioned advantages, and features of the proposed multiport ZQR DC/DC converter.

Increased load demands worsen distribution system problems such as greater line losses, voltage deviation and a plethora of other concerns. This current work presents an approach stressing simultaneous optimal allocation and sizing of capacitor banks and distributed generations, as well as optimal radial distribution system (RDS) reconfiguration, to address these difficulties. The above objectives are accomplished through the maiden application of the proposed quasi-reflection-based slime mould algorithm (QRSMA). The efficacy of QRSMA is established by testing it on different benchmark functions. A new modified backward forward load flow approach is also proposed and validated by comparing its results to those obtained using MATPOWER software for IEEE 69, 85, and 118 bus RDSs. The proposed load flow technique is independent of the sequential bus numbering scheme and may be applied to any RDS network topology. The proposed QRSMA is tested on 118 bus RDS and to prove its effectiveness; its results are compared to those of other studied algorithms. The study takes into account both fixed and variable loading scenarios. A cost-benefit analysis of the strategy is also performed in order to make the methodology more realistic.

Unmanned Aerial Vehicles (UAVs) are becoming increasingly popular for applications such as inspections, delivery, agriculture, surveillance, and many more. It is estimated that, by 2040, UAVs/drones will become a mainstream delivery channel to satisfy the growing demand for parcel delivery. Though the UAVs are gaining interest in civil applications, the future of UAV charging is facing a set of vital concerns and open research challenges. Considering the case of parcel delivery, handling countless drones and their charging will become complex and laborious. The need for non-contact based multi-device charging techniques will be crucial in saving time and human resources. To efficiently address this issue, Wireless Power Transmission (WPT) for UAVs is a promising technology for multi-drone charging and autonomous handling of multiple devices. In the literature of the past five years, limited surveys were conducted for wireless UAV charging. Moreover, vital problems such as coil weight constraints, comparison between existing charging techniques, shielding methods and many other key issues are not addressed. This motivates the author in conducting this review for addressing the crucial aspects of wireless UAV charging. Furthermore, this review provides a comprehensive comparative study on wireless charging’s technical aspects conducted by prominent research laboratories, universities, and industries. The paper also discusses UAVs’ history, UAVs structure, categories of UAVs, mathematical formulation of coil and WPT standards for safer operation.

Voltage source converter based multi – terminal DC grids (VSC – MTDC) are widely used for integration of renewable resources. Control of these grids requires vector control method, which includes inner and outer current controllers, tuning of these controllers is very important. Conventionally used tuning methods consider approximated linear model to tune the proportional – integral (PI) parameters of voltage source converter (VSC) which fails to produce optimum disturbance rejection results. In this research an Ant Colony Optimization (ACO) technique is used to get the optimum parameters of inner and outer power control layers for multi – terminal DC system. ACO is applied simultaneously on inner and outer control layers and results are compared with classical tuning method and well established meta - herustic technique, Particle Swarm Optimization (PSO) using MATLAB software. Dynamic models of VSC – MTDC are developed in PSCAD/EMTDC to exalt the qualities of the presented tuning method. In this paper, a four terminal VSC – MTDC has been used under different sequence of events of disturbances e.g. load change on AC side, active power reference change for inverter and rectifier mode and disconnection of one terminal to evaluate the robustness of ACO algorithm with respect to classical method. The proposed tuning method gives superior results under different disturbance compared to conventional method.

Considering a future scenario in which a driverless Electric Vehicle (EV) needs an automatic charging system without human intervention. In this regard, there is a requirement for a fully automatable, fast, safe, cost-effective, and reliable charging infrastructure that provides a profitable business model and fast adoption in the electrified transportation systems. These qualities can be comprehended through wireless charging systems. Wireless Power Transfer (WPT) is a futuristic technology with the advantage of flexibility, convenience, safety, and the capability of becoming fully automated. In WPT methods resonant inductive wireless charging has to gain more attention compared to other wireless power transfer methods due to high efficiency and easy maintenance. This literature presents a review of the status of Resonant Inductive Wireless Power Transfer Charging technology also highlighting the present status and its future of the wireless EV market. First, the paper delivers a brief history throw lights on wireless charging methods, highlighting the pros and cons. Then, the paper aids a comparative review of different type’s inductive pads, rails, and compensations technologies done so far. The static and dynamic charging techniques and their characteristics are also illustrated. The role and importance of power electronics and converter types used in various applications are discussed. The batteries and their management systems as well as various problems involved in WPT are also addressed. Different trades like cyber security economic effects, health and safety, foreign object detection, and the effect and impact on the distribution grid are explored. Prospects and challenges involved in wireless charging systems are also highlighting in this work. We believe that this work could help further the research and development of WPT systems.

Power loss is a bottleneck in every power system and it has been in focus of majority of the researchers and industry. This paper proposes a new method for determining the power loss in wind-solar power system based on deep learning. The main idea of the proposed scheme is to freeze the feature extraction layer of the deep Boltzmann network and deploy deep learning training model as the source model. The sample data with closer distribution with the data under consideration is selected by defining the maximum mean discrepancy contribution coefficient. The power loss calculation model is developed by configuring the deep neural network through the sample data. The deep learning model is deployed to simulate the non-linear mapping relationship between the load data, power supply data, bus voltage data and the grid loss rate during power grid operation. The proposed algorithm is applied to an actual power grid to evaluate its effectiveness. Simulation results show that the proposed algorithm effectively improved the system performance in terms of accuracy, fault tolerance, nonlinear fitting and timeliness as compared with existing schemes.

A bi-directional power exchange between the plug-in electric vehicle (PEV) and the AC electrical grid is necessary to perform the Vehicle to Grid (V2G) and Grid to Vehicle (G2V) operations. While performing these operations, different power converters and controllers play an important role as mediators between the PEV and electric grid. Various works have demonstrated the utilization of controllers for PEV’s battery power management. However, the existing conventional controllers have technical shortcomings about vulnerability to controller gain, accurate mathematical modelling, poor adaptability, sluggish response to a sudden outburst and lengthy interval execution processing. Therefore, this paper develops an adaptive neuro-fuzzy inference system (ANFIS) control strategy based bidirectional power management scheme to ensure the optimal electrical power flow exchange between the AC electrical grid and battery storage system in PEVs. This paper aims to reduce the stress on the grid power side and utilize the unused power properly. The performance of the ANFIS model is varied using two PEVs based on real-life power consumptions by different loads at home based on five operational modes. Besides, a comparative analysis between the ANFIS controller and the PI controller is carried out to demonstrate the effectiveness of the proposed control scheme. The results illustrate that the proposed ANFIS controller delivers a smoother power injection from the PEV to the AC power grid with the least harmonics as well as achieves a smoother battery profile and less distortion when power is absorbed by PEV battery.

Modular multilevel converters (MMCs) have been conceived as an alternative in front-end converter applications to enhance the converter system’s reliability, minimize total harmonic distortion, and improve power quality. These converters utilize several DC-link capacitors and power electronic switches, along with switches operating with high switching frequencies, to attain the desired characteristics. Thereby, this paper systematically proposes a novel three-phase asymmetric hybrid modular multilevel converter (AHMMC) for front-end converters used in lower-medium-voltage applications. The AHMMC configuration is based on a three-phase converter connected to a per-phase series arrangement with a cascaded converter module (CCM). The study investigates the AHMMC and proposes a control scheme, which minimizes the voltage range on switches and maintains the current to its reference value. Furthermore, the study also introduces an active balancing of voltage across DC-link capacitors based on the phase opposition disposition PWM (POD-PWM) method. Our new configuration has features such as low switching loss, reduced DC-link voltage, a wider modulation range for the unity power factor (PF), and low voltage and current harmonic distortion. The simulation results are added to verify the performance of the new AHMMC topology and the usefulness of the modular control scheme. In addition, a low-voltage laboratory prototype based on customized control and power boards is built to validate the proposed converter and its control scheme in practice.

Cyber-Physical System (CPS) is a new kind of digital technology that increases its attention across academia, government, and industry sectors and covers a wide range of applications like agriculture, energy, medical, transportation, etc. The traditional power systems with physical equipment as a core element are more integrated with information and communication technology, which evolves into the Cyber-Physical Power System (CPPS). The CPPS consists of a physical system tightly integrated with cyber systems (control, computing, and communication functions) and allows the two-way flows of electricity and information for enabling smart grid technologies. Even though the digital technologies monitoring and controlling the electric power grid more efficiently and reliably, the power grid is vulnerable to cybersecurity risk and involves the complex interdependency between cyber and physical systems. Analyzing and resolving the problems in CPPS needs the modelling methods and systematic investigation of a complex interaction between cyber and physical systems. The conventional way of modelling, simulation, and analysis involves the separation of physical domain and cyber domain, which is not suitable for the modern CPPS. Therefore, an integrated framework needed to analyze the practical scenario of the unification of physical and cyber systems. A comprehensive review of different modelling, simulation, and analysis methods and different types of cyber-attacks, cybersecurity measures for modern CPPS is explored in this paper. A review of different types of cyber-attack detection and mitigation control schemes for the practical power system is presented in this paper. The status of the research in CPPS around the world and a new path for recommendations and research directions for the researchers working in the CPPS are finally presented.

Multilevel inverters (MLIs) have drawn tremendous attention in the power sector. Application of MLI has grown extensively to improve the power quality and efficiency of the photovoltaic (PV) system. For an MLI interfacing PV system, the size, cost and voltage stress are the key constraints of the MLI that need to be minimized. This paper presents a novel reduced part count MLI interfacing single-stage grid-tied PV system along with a closed-loop control strategy. The proposed MLI consists of n repeating units and a level boosting circuit (LBC) that assists in generating 4n + 7 voltage levels instead of 2n + 3 levels. Three different algorithms are proposed for a proper selection of dc-link voltages to enhance the levels further. A comparative analysis is carried out to confirm the superiority of the developed MLI. The workability of the proposed MLI is investigated with a 1.3 kW PV system. The closed-loop control strategy ensures the maximum power tracking, dc-link voltage balancing, satisfactory operation of the MLI and injection of clean sinusoidal grid current under any dynamic changes. Comprehensive simulation analysis is carried out considering a 15-level MLI structure. Experimental tests further confirm the practicality of the topological advancement for a PV system under different dynamic conditions.

Clean and environment-friendly energy harvesting are of prime interest today as it is one of the key enablers in achieving the Sustainable Development Goals (SDGs) as well as accelerates social progress and enhances living standards. India, the second-most populous nation with a population of 1.353 billion, is one of the largest consumers of fossil fuels in the world which is responsible for global warming. An ever-increasing population is projected until 2050, and consequently, the energy demand in the upcoming decades will be co-accelerated by the rapid industrial growth. The Ministry of New and Renewable Energy (MNRE) with the support of National Institution for Transforming India (NITI) Aayog is working to achieve the Indian Government's target of attaining 175 GW through renewable energy resources. Many Indian states are currently increasing their renewable energy capacity in an objective to meet future energy demand. The review paper discusses in-depth about the three Indian states, namely Karnataka, Gujarat, Tamil Nadu, which pioneers the renewable energy production in India. The global energy scenario was discussed in detail with Indian contrast. Further, the barriers to the development of renewable energy generation and policies of the Indian government are discussed in detail to promote renewable energy generation throughout India as well as globally since the challenges are similar for other nations. This study analyzed various prospects of the country in renewable energy which has been done in a purpose to help the scholars, researchers, and policymakers of the nation, as it gives an insight into the present renewable energy scenario of the country.

The main focus of this paper is to design and assess the characteristics investigation of Novel Dual Stator Pseudo-Pole Five Phase Permanent Magnet Synchronous Generator (NDSPPFP-PMSG) for wind power application. The proposed generator has a dual stator and two sets of five phase windings which enhance its power density and fault tolerant capability. The novelty of this generator is based on the fact that, eight magnetic poles are formed using only four poles of actual magnets on both the surfaces of the rotor. For the designing and optimal electromagnetic performance of the proposed generator, a Dynamic Magnetic Circuit Model (DMCM) is reported. To validate the results obtained from DMCM, Finite Element Method (FEM) has been opted owing to its high accuracy. For showing the performance superiority, the proposed generator is compared with two conventional generators namely, Dual Stator Embedded-Pole Five Phase (DSEPFP) and Single Stator Single Rotor Five Phase (SSSRFP) PMSG. To compare their performances, FEM results are considered. The electromagnetic performance namely, generated Electromotive Force(EMF), percentage(%) Total Harmonic Distortion(THD) of generated EMF, generated EMF vs speed, terminal voltage vs load current, electromagnetic torque developed on rotor vs time, %ripple content in the torque, and %efficiency vs load current are investigated for all the three generators. From these investigations, it is found that the power density (power to weight ratio) of the proposed generator is maximum.

The rise in power demand today necessitates its generation and transmission at high voltages. The efficient transmission of electric power requires transformers with an insulation system that exhibits excellent dielectric properties. In this paper ZnO and CuO nanomaterials are utilized to investigate the dielectric characteristics of pure synthetic ester oil and its related nanofluids (NFs) from room temperature up to 60 °C at increments of 20 °C, including AC breakdown voltage, Dielectric Dissipation factor, and DC resistivity. The breakdown testing is carried out in accordance with experimental IEC-60156 requirements. The DC resistivity and dissipation factor of oils are measured using the Dissipation Factor meter, resistivity meter, and a heating chamber with an oil cell that follows IEC 60247 standard. The statistical analysis is performed on the breakdown voltages test values using the Weibull probability distribution model for better accuracy. From the results, it has been found that ZnO nanofluid possesses a higher breakdown voltage among all the tested liquids. Furthermore CuO nanofluid gives a minimum value of dissipation factor even at higher temperatures.

The current sensorless vector-controlled permanent-magnet synchronous motor (PMSM) drive using a single sensor (i.e., speed sensor) is presented in this work. The current sensors are removed, and the estimated currents are used to close the current loop to minimize the drive cost and make it fault-tolerant against current sensor failure. A classical vector-control PMSM drive requires at least three sensors, i.e., two current sensors and one speed/position sensor. This paper presents a new current estimation technique that is free from inverter switching states, an integrator, and differentiator terms. The drive is suitable for retrofit applications, as it does not require any additional hardware. The reference voltages (vds and vqs) are used to estimate the rotor reference frame currents (i.e., iqs and ids). The presented algorithm depends on the stator resistance (Ɍs). The online Ɍs estimation algorithm is used for compensation to overcome the effect of the Ɍs on the estimated currents. The sensitivity analysis for the currents against the speed is verified and presented. The speed loop is closed with actual speed information, which will try to maintain the reference speed under any circumstances. The proposed current sensorless PMSM drive was validated using MATLAB/Simulink and also verified on a hardware prototype. The presented technique was verified for various operation conditions, and some of the extensive results are presented.

Due to the flexibility, sustainability, affordability, and ease of installation of solar photovoltaic systems, their use has significantly increased over the past two decades. The performance of a solar PV system can be constrained by a variety of external conditions, including hotspots, partial shade, and other minor faults. This causes the PV system to permanently fail and power losses. The power output in a partially shaded solar system is improved in this work by the introduction of a fault classifier based on thermal image analysis with a reconfiguration algorithm. For that purpose, the entire PV array is divided into two parts, with one of these being the male part and the other being the female part. MOSFET switches are used to build the switching matrix circuit that connects these parts. The Flir T420bx thermal camera captures thermal pictures, and MATLAB/Simulink® is used to extract the image properties. The pairing reconfiguration pattern is found using an algorithm based on image processing and the image attributes. The switching signals to the switching circuit are triggered by an Arduino controller. The image attributes of the thermal images may also be used to categorize PV system defects. This reconfiguration technique is easy, simple to use, and it can also be used to check the health of each PV module. The performance of the proposed work was validated using a 5 kW PV system with a 4 × 5 TCT array configuration at Sethu Institute of Technology’s renewable energy lab in India. The proposed method was simulated using the MATLAB-Simulink software program, and the outcomes were verified on different hardware setups.

To satisfy global electrical energy requirements, photovoltaic (PV) energy is a promising source that can be obtained from the available alternative sources, but partial shading conditions (PSCs), which trap the local maxima power point instead of the global maxima peak power point (GMPP), are a major problem that needs to be addressed in PV systems to achieve the uninterruptable continuous power supply desired by consumers. To avoid these difficulties, a marine predator algorithm (MPA), which is a bio-inspired meta-heuristic algorithm, is applied in this work. The work is validated and executed using MATLAB/Simulink software along with hardware experimentation. The superiority of the proposed MPA method is validated using four different PSCs on the PV system, and their characteristics are compared to those of existing algorithms. The four different PSC outcomes in terms of GMPP are case 1 at 0.07 s 995.0 Watts; case 2 at 0.06 s 674.5 Watts; case 3 at 0.04 s 654.1 Watts; and case 4 at 0.04 s 364.2 Watts. The software- and hardware-validated results of the proposed MPA method show its supremacy in terms of convergence time, efficiency, accuracy, and extracted power.

The aging of PV cells reduces their electrical performance i.e., the parasitic parameters are introduced in the solar panel. The shunt resistance (RSh), series resistance (RS), photo current (IPh), diode current (Id), and diffusion constant (a1) are known as parasitic or extraction parameters. Cracks and hotspots reduce the performance of PV cells and result in poor V–I characteristics. Certain tests are carried out over a long period of time to determine the quality of solar cells; for example, 1000 h of testing is comparable to 20 years of operation. The extraction of solar parameters is important for PV modules. The Tabu Search Optimization (TSO) algorithm is a robust meta-heuristic algorithm that was employed in this study for the extraction of parasitic parameters. Particle Swarm Optimization (PSO) and a Genetic lgorithm (GA), as well as other well-known optimization methods, were used to test the proposed method’s correctness. The other approaches included the lightning search algorithm (LSA), gravitational search algorithm (GSA), and pattern search (PS). It can be concluded that the TSO approach extracts all six parameters in a reasonably short period of time. The work presented in this paper was developed and analyzed using a MATLAB-Simulink software environment.

Transformer oil is conventionally used as an insulating liquid for the purpose of insulation and cooling in power transformers. The rise in the power demand has put stress on the existing insulation system used for power transmission. Nanotechnology provides an advanced approach to upgrade the conventional insulation system by producing nano-oil with enhanced dielectric characteristics. The aim of the study is to present the influence of area volume effect on the dielectric performance of mineral oil and its nanofluids. In this paper, nanofluids are prepared by dispersing two different concentrations of SiO2 nanoparticles in base transformer oil using a two-step method. The effect of area and volume is investigated on nanofluids in the laboratory using coaxial electrode configurations under different test conditions. The AC breakdown voltage and maximum electric stress is determined for the pure oil and nanofluids. The results show that the addition of SiO2 nanoparticles significantly improves the dielectric characteristics of transformer oil. Moreover, the breakdown phenomenon is also discussed to analyze the effect of nanoparticle, stressed area, and stressed volume on the dielectric strength of insulating oil. Nanofluids could be an alternative to mineral oil.

In modern industrial manufacturing processes, induction motors are broadly utilized as industrial drives. Online condition monitoring and diagnosis of faults that occur inside and/or outside of the Induction Motor Drive (IMD) system make the motor highly reliable, helping to avoid unscheduled downtimes, which cause more revenue loss and disruption of production. This can be achieved only when the irregularities produced because of the faults are sensed at the moment they occur and diagnosed quickly so that suitable actions to protect the equipment can be taken. This requires intelligent control with a high-performance scheme. Hence, a Field Programmable Gate Array (FPGA) based on neuro-genetic implementation with a Back Propagation Neural network (BPN) is suggested in this article to diagnose the fault more efficiently and almost instantly. It is reported that the classification of the neural network will provide the output within 2 µs although the clone procedure with microcontroller requires 7 ms. This intelligent control with a high-performance technique is applied to the IMD fed by a Voltage Source Inverter (VSI) to diagnose the fault. The proposed approach was simulated and experimentally validated.

The flashover performance of contaminated insulators highly depends on the type of pollutant and its present concentration. In this paper, important agricultural salts (NaCl, K2SO4, NaHCO3, CaSO4, KHCO3, MgSO4, NH4), 2Fe(SO4)2, and 6H2O (ferrous ammonium sulphate, dust, and urea) at different concentrations, and biological contaminants, such as algae and fungi, were taken as pollutants, and the AC flashover behavior of a porcelain-cap-and-pin-type insulator polluted with these two different pollutants was investigated. The experiment was carried out by a semi-natural method, wherein the insulator was first polluted artificially; thereafter, natural fog was applied to measure the wet flashover voltage. Test results indicated that the flashover voltages were affected by both soluble salts and non-soluble components deposited on the insulator surface. In the case of the thickly contaminated layers, non-soluble deposits greatly reduced the flashover voltage. Moreover, by using regression analysis, four empirical models based on different variables were developed. The empirical models developed in the present work represented a good degree of relation in predicting the flashover voltage of naturally contaminated insulators.

Demand Side Management (DSM) is an effective tool for utilities through reducing the demand of peak load and controlling the utilization of the energy of the system. The implementation of DSM provides benefits for utilities and is profitable for the customers who are involved in this process. DSM based on a load shifting strategy is proposed in this paper by employing various devices to minimize the energy consumption pattern in the system. The proposed hybrid strategy is the joint implementation of the Wingsuit Flying Search Algorithm (WFSA) and Artificial Cell Swarm Optimization (ACSO). The searching behavior of WFSA is enhanced by ACSO. Hence, it is named the WFS2ACSO technique. The implementation of this load shifting technique was carried out on three different types of loads, these being residential loads, commercial loads, and industrial loads. Two case studies, over summer and winter, were validated to check the feasibility of the test system. The proposed method aimed to achieve the load demand in an effective way for the minimization of bill electrification, Peak to Average Ratio (PAR), and the consumption of power. The Time-of-Use (TOU) pricing was implemented to calculate the savings in energy bills. The proposed test system of the Micro Grid (MG) was executed on a MATLAB platform with two case studies based on the optimization methods WFSA and WFS2ACSO. Simulation results demonstrated the comparative analysis of electricity cost and peak load with different algorithms and were carried out with and without DSM consideration. The projected DSM methodology achieved considerable savings as the peak load demand of MG decreased. Furthermore, the decrease in PAR levels of 14% in the residential load, 16% in the commercial load, and 10% in the industrial load, with and without the DSM methodology, was presented. The flight length and awareness of probability tuning parameters make the proposed algorithm more effective in obtaining better results. The test results obtained prove the effectiveness of the hybridized algorithm as compared with other trend-setting optimization techniques such as Particle Swarm Optimization (PSO) and Ant Lion Optimization (ALO).

Uncertainties are the most significant challenges in the smart power system, necessitating the use of precise techniques to deal with them properly. Such problems could be effectively solved using a probabilistic optimization strategy. It is further divided into stochastic, robust, distributionally robust, and chance-constrained optimizations. The topics of probabilistic optimization in smart power systems are covered in this review paper. In order to account for uncertainty in optimization processes, stochastic optimization is essential. Robust optimization is the most advanced approach to optimize a system under uncertainty, in which a deterministic, set-based uncertainty model is used instead of a stochastic one. The computational complexity of stochastic programming and the conservativeness of robust optimization are both reduced by distributionally robust optimization.Chance constrained algorithms help in solving the constraints optimization problems, where finite probability get violated. This review paper discusses microgrid and home energy management, demand-side management, unit commitment, microgrid integration, and economic dispatch as examples of applications of these techniques in smart power systems. Probabilistic mathematical models of different scenarios, for which deterministic approaches have been used in the literature, are also presented. Future research directions in a variety of smart power system domains are also presented.

The paper proposes a novel hybrid feature selection (FS) method for day-ahead electricity price forecasting. The work presents a novel hybrid FS algorithm for obtaining optimal feature set to gain optimal forecast accuracy. The performance of the proposed forecaster is compared with forecasters based on classification tree and regression tree. A hybrid FS method based on the elitist genetic algorithm (GA) and a tree-based method is applied for FS. Making use of selected features, aperformance test of the forecaster was carried out to establish the usefulness of the proposed approach. By way of analyzing and forecasts for day-ahead electricity prices in the Australian electricity markets, the proposed approach is evaluated and it has been established that, with the selected feature, the proposed forecaster consistently outperforms the forecaster with a larger feature set. The proposed method is simulated in MATLAB and WEKA software.

Energy safe is mandatory for all isolated IoT tools, such as in long way roads, mountains, or even in smart cities. If increasing the lifetime of these tools, the rentability of the global network loop becomes more efficient. Therefore, this paper presents a new approach for saving energy inside the source nodes by supervising the state of energy inside each source node and calculating the duty cycle factor. The relationship between these parameters is based on an optimization problem formulation. In this respect, the present paper is designed to propose a new approach that deals with increasing the lifetime of the wireless sensor network (WSN)-attached nodes, as fixed in the application. The newly devised design is based on implementing the IEEE 802.15.4 standard beacon-enabled mode, involving a cluster tree topology. Accordingly, every subgroup is allotted to apply a specifically different duty cycle, depending on the battery’s remaining energy level, which contributes to creating a wide range of functional modes. Hence, various thresholds are defined. Simulation results prove the efficiency of the proposed approach and show the energetic benefit. The proposed flowchart has minimized the consumed energy for the WSN, which improves the battery lifetime and enhances the IoT application’s robustness. Simulations and experiments have been carried out under different conditions and the results prove that the proposed method is a viable solution.

The restructuring of power systems and the ever-increasing demand for electricity have given rise to congestion in power networks. The use of distributed generators (DGs) may play a significant role in tackling such issues. DGs may be integrated with electrical power networks to regulate the drift of power in the transmission lines, thereby increasing the power transfer capabilities of lines and improving the overall performance of electrical networks. In this article, an effective method based on the Harris hawks optimization (HHO) algorithm is used to select the optimum capacity, number, and site of solar-based DGs to reduce real power losses and voltage deviation. The proposed HHO has been tested with a complex benchmark function then applied to the IEEE 33 and IEEE 69 bus radial distribution systems. The single and multiple solar-based DGs are optimized for the optimum size and site with a unity power factor. It is observed that the overall performance of the systems is enhanced when additional DGs are installed. Moreover, considering the stochastic and sporadic nature of solar irradiance, the practical size of DG has been suggested based on analysis that may be adopted while designing the actual photovoltaic (PV) plant for usage. The obtained simulation outcomes are compared with the latest state-of-the-art literature and suggest that the proposed HHO is capable of processing complex high dimensional benchmark functions and has capability to handle problems pertaining to electrical distribution in an effective manner.

The proposed special issue (SI) has invited submissions related to renewable energy, energy storage, power converters and electric drive systems for electrified transportation and smart grid applications [...]

This paper proposes a hybrid control scheme for a newly devised hybrid multilevel inverter (HMLI) topology. The circuit configuration of HMLI is comprised of a cascaded converter module (CCM), connected in series with an H-bridge converter. Initially, a finite set model predictive control (FS-MPC) is adopted as a control scheme, and theoretical analysis is carried out in MATLAB/Simulink. Later, in the real-time implementation of the HMLI topology, a hybrid control scheme which is a variant of the FS-MPC method has been proposed. The proposed control method is computationally efficient and therefore has been employed to the HMLI topology to mitigate the high-frequency switching limitation of the conventional MPC. Moreover, a comparative analysis is carried to illustrate the advantages of the proposed work that includes low switching losses, higher efficiency, and improved total harmonic distortion (THD) in output current. The inverter topology and stability of the proposed control method have been validated through simulation results in MATLAB/Simulink environment. Experimental results via low-voltage laboratory prototype have been added and compared to realize the study in practice.

Power quality (PQ) has become an important topic in today’s power system scenario. PQ issues are raised not only in normal three-phase systems but also with the incorporation of different distributed generations (DGs), including renewable energy sources, storage systems, and other systems like diesel generators, fuel cells, etc. The prevalence of these issues comes from the non-linear features and rapid changing of power electronics devices, such as switch-mode converters for adjustable speed drives and diode or thyristor rectifiers. The wide use of these fast switching devices in the utility system leads to an increase in disturbances associated with harmonics and reactive power. The occurrence of PQ disturbances in turn creates several unwanted effects on the utility system. Therefore, many researchers are working on the enhancement of PQ using different custom power devices (CPDs). In this work, the authors highlight the significance of the PQ in the utility network, its effect, and its solution, using different CPDs, such as passive, active, and hybrid filters. Further, the authors point out several compensation strategies, including reference signal generation and gating signal strategies. In addition, this paper also presents the role of the active power filter (APF) in different DG systems. Some technical and economic considerations and future developments are also discussed in this literature. For easy reference, a volume of journals of more than 140 publications on this particular subject is reported. The effectiveness of this research work will boost researchers’ ability to select proper control methodology and compensation strategy for various applications of APFs for improving PQ.

Soaring energy demand and the establishment of various trends in the energy market have paved the way for developing demand-side management (DSM) from the consumer side. This paper proposes a reinforced DSM (RDSM) approach that uses an enhanced binary gray wolf optimization algorithm (EBGWO) that benefits the consumer premises with load scheduling, and peak demand reduction. To date, DSM research has been carried out for residential, commercial and industrial loads, whereas DSM approaches for educational loads have been less studied. The institution load also consumes much utility energy during peak hours, making institutional consumers pay a high amount of cost for energy consumption during peak hours. The proposed objective is to reduce the total electricity cost and to improve the operating efficiency of the entire load profile at an educational institution. The proposed architecture integrates the solar PV (SPV) generation that supplies the user-comfort loads during peak operating hours. User comfort is determined with a metric termed the user comfort index (UCI). The novelty of the proposed work is highlighted by modeling a separate class of loads for temperature-controlled air conditioners (AC), supplying the user comfort loads from SPV generation and determining user comfort with percentage UCI. The improved transfer function used in the proposed EBGWO algorithm performs faster in optimizing nonlinear objective problems. The electricity price in the peak hours is high compared to the off-peak hours. The proposed EBGWO algorithm shift and schedules the loads from the peak hours to off-peak hours, and incorporating SPV in satisfying the user comfort loads aids in reducing the power consumption from the utility during peak hours. Thus, the proposed EBGWO algorithm greatly helps the consumer side decrease the peak-to-average ratio (PAR), improve user comfort significantly, reduce the peak demand, and save the institution’s electricity cost by USD 653.046.

Currently, most of the vehicles make use of fossil fuels for operations, resulting in one of the largest sources of carbon dioxide emissions. The need to cut our dependency on these fossil fuels has led to an increased use of renewable energy sources (RESs) for mobility purposes. A technical and economic analysis of a one-stop charging station for battery electric vehicles (BEV) and fuel cell electric vehicles (FCEV) is investigated in this paper. The hybrid optimization model for electric renewables (HOMER) software and the heavy-duty refueling station analysis model (HDRSAM) are used to conduct the case study for a one-stop charging station at Technical University of Denmark (DTU)-Risø campus. Using HOMER, a total of 42 charging station scenarios are analyzed by considering two systems (a grid-connected system and an off-grid connected system). For each system three different charging station designs (design A-hydrogen load; design B-an electrical load, and design C-an integrated system consisting of both hydrogen and electrical load) are set up for analysis. Furthermore, seven potential wind turbines with different capacity are selected from HOMER database for each system. Using HDRSAM, a total 18 scenarios are analyzed with variation in hydrogen delivery option, production volume, hydrogen dispensing option and hydrogen dispensing option. The optimal solution from HOMER for a lifespan of twenty-five years is integrated into design C with the grid-connected system whose cost was $986,065. For HDRSAM, the optimal solution design consists of tube trailer as hydrogen delivery with cascade dispensing option at 350 bar together with high production volume and the cost of the system was $452,148. The results from the two simulation tools are integrated and the overall cost of the one-stop charging station is achieved which was $2,833,465. The analysis demonstrated that the one-stop charging station with a grid connection is able to fulfil the charging demand cost-effectively and environmentally friendly for an integrated energy system with RESs in the investigated locations.

Wind energy is one of the supremely renewable energy sources and has been widely established worldwide. Due to strong seasonal variations in the wind resource, accurate predictions of wind resource assessment and appropriate wind speed distribution models (for any location) are the significant facets for planning and commissioning wind farms. In this work, the wind characteristics and wind potential assessment of onshore, offshore, and nearshore locations of India—particularly Kayathar in Tamilnadu, the Gulf of Khambhat, and Jafrabad in Gujarat—are statistically analyzed with wind distribution methods. Further, the resource assessments are carried out using Weibull, Rayleigh, gamma, Nakagami, generalized extreme value (GEV), lognormal, inverse Gaussian, Rician, Birnbaum–Sandras, and Bimodal–Weibull distribution methods. Additionally, the advent of artificial intelligence and soft computing techniques with the moth flame optimization (MFO) method leads to superior results in solving complex problems and parameter estimations. The data analytics are carried out in the MATLAB platform, with in-house coding developed for MFO parameters estimated through optimization and other wind distribution parameters using the maximum likelihood method. The observed outcomes show that the MFO method performed well on parameter estimation. Correspondingly, wind power generation was shown to peak at the South West Monsoon periods from June to September, with mean wind speeds ranging from 9 to 12 m/s. Furthermore, the wind speed distribution method of mixed Weibull, Nakagami, and Rician methods performed well in calculating potential assessments for the targeted locations. Likewise, the Gulf of Khambhat (offshore) area has steady wind speeds ranging from 7 to 10 m/s with less turbulence intensity and the highest wind power density of 431 watts/m2. The proposed optimization method proves its potential for accurate assessment of Indian wind conditions in selected locations.

The analysis and the assessment of interconnected photovoltaic (PV) modules under different shading conditions and various shading patterns are presented in this paper. The partial shading conditions (PSCs) due to the various factors reduce the power output of PV arrays, and its characteristics have multiple peaks due to the mismatching losses between PV panels. The principal objective of this paper is to model, analyze, simulate and evaluate the performance of PV array topologies such as series-parallel (SP), honey-comb (HC), total-cross-tied (TCT), ladder (LD) and bridge-linked (BL) under different shading patterns to produce the maximum power by reducing the mismatching losses (MLs). Along with the conventional PV array topologies, this paper also discusses the hybrid PV array topologies such as bridge-linked honey-comb (BLHC), bridge-linked total-cross-tied (BLTCT) and series-parallel total-cross-tied (SPTCT). The performance analysis of the traditional PV array topologies along with the hybrid topologies is carried out during static and dynamic shading patterns by comparing the various parameters such as the global peak (GP), local peaks (LPs), corresponding voltage and current at GP and LPs, fill factor (FF) and ML. In addition, the voltage and current equations of the HC configuration under two shading conditions are derived, which represents one of the novelties of this paper. The various parameters of the SPR-200-BLK-U PV module are used for PV modeling and simulation in MATLAB/Simulink software. Thus, the obtained results provide useful information to the researchers for healthy operation and power maximization of PV systems.

This editorial manuscript reviews the papers accepted for publication in the Special Issue “DC & Hybrid Microgrids” of Applied Sciences. This Special Issue, co-organized by the University of Pisa, Italy and Østfold University College in Norway, has collected nine papers from 25 submitted, with authors from Asia, North America and Europe. The published articles provide an overview of the most recent research advances in direct current (DC) and hybrid microgrids, exploiting the opportunities offered by the use of renewable energy sources, battery energy storage systems, power converters, innovative control and energy management strategies.