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Unbalanced loads and high neutral currents on low voltage networks which frequently use three-phase, four wire systems with no larger conductors must need to be addressed. To overcome the loads and currents in low-voltage networks, an hybrid method is proposed in this manuscript for improving the networks of low-voltage using three-phase four-wire systems. The AOA-RERNN technique is the integration of the Archimedean-Optimization-Algorithm (AOA) and Recalling-Enhanced-Recurrent-Neural-Network (RERNN) technique to mitigate the issues, like neutral voltage offset, and harmonics, and neutral-to-ground voltage raise. At the point-of-common-coupling (PCC), the integration of Archimedean-optimization-algorithm and Recalling-enhanced-recurrent-neural-network approach is used to overcome the above mentioned issues. This strategy involves optimizing converter parameters with AOA and addressing system imbalances with RERNN, including mid-high line current, phase disparities, and neutral line compensation. Also, implementing control-based compensation reduces neutral current without requiring large neutral conductors. The proposed model is done in MATLAB. By this, the proposed approach achieves an impressive efficiency of 97.54%. But, the existing methods, like Artificial Transgender Long corn Algorithm (ATLA), Combined Adaptive Grasshopper Optimization Algorithm and Artificial Neural Network (AGONN), And Proportional Integral (PI) attain the efficiency of 80.23%, 77.26%, and 82.13%, respectively. The outcome of the simulation indicates that the proposed technique provides better findings than the present methods. Finally, this study demonstrates the possibility of the proposed approach for increasing the efficiency and the performance of electronic power converters in renewable generation. [ABSTRACT FROM AUTHOR]

A renewable energy-based DC micro grid system supports the DC power flow for domestic and industrial purposes. The DC voltage drawn from the source is to be adjusted by using the power electronic converters. So, the controlling of converter came into existence. In this paper the Flyback converter is modelled to get the rated voltage regulation with the desired output voltage with proper duty cycle at the converter. The nonlinear system is converted into the linear one by using the lie derivative control technique. In this technique the relative values of current loop and voltage loop is linearized to get the desired output voltage at the flyback converter. Thus as compared to PI controller the proposed controller flows good for the transient response of the power system. The boost converter designed in the existing has modelled with the low output range but in the Flyback converter the ability to achieve the voltage regulation in DC micro grid with variable inductance and capacitance is defined. Simulation result shows the modelled flyback converter flows good and accurate as compared to the PI controller with the settling time difference of 85s of voltage dynamics in the DC micro grid system. The steady state and transient response of the system is evaluated by comparing the simulation of PI and the proposed controller used in the real time power transmission system to reduce the power loss at receiving side. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing interest in using model-free deep reinforcement learning (DRL)-based controllers as an alternative approach to improve the dynamic behavior, efficiency, and other aspects of DC–DC power electronic converters, which are traditionally controlled based on small signal models. These conventional controllers often fail to self-adapt to various uncertainties and disturbances. This paper presents a design methodology using proximal policy optimization (PPO), a widely recognized and efficient DRL algorithm, to make near-optimal decisions for real buck converters operating in both continuous conduction mode (CCM) and discontinuous conduction mode (DCM) while handling resistive and inductive loads. Challenges associated with delays in real-time systems are identified. Key innovations include a chattering-reduction reward function, engineering of input features, and optimization of neural network architecture, which improve voltage regulation, ensure smoother operation, and optimize the computational cost of the neural network. The experimental and simulation results demonstrate the robustness and efficiency of the controller in real scenarios. The findings are believed to make significant contributions to the application of DRL controllers in real-time scenarios, providing guidelines and a starting point for designing controllers using the same method in this or other power electronic converter topologies. [ABSTRACT FROM AUTHOR]

The rise of electric vehicles (EVs) necessitates an efficient charging infrastructure capable of delivering a refueling experience akin to conventional vehicles. Innovations in Extreme Fast Charging (EFC) offer promising solutions in this regard. By harnessing renewable energy sources and employing sophisticated multiport converters, EFC systems can meet the evolving demands of EV refueling. A single-stage topology simplifies the converter design, focusing on efficient DC-AC conversion, vital for feeding solar power into the grid or charging stations. It provides power factor correction, harmonics filtering, and mitigates power quality issues, ensuring stable and efficient operations. Converters with Maximum Power Point Tracking (MPPT) capability facilitate the efficient integration of solar PV systems in charging stations, ensuring maximum solar energy utilization for EV charging. The ability to operate in different modes allows seamless integration with energy storage systems, storing excess solar energy for use during night-time or peak demand periods, enhancing overall efficiency and reliability. Advanced converters support bidirectional energy flow, enabling EV batteries to discharge back to the grid, aiding grid stability and energy management. However, robust control algorithms are needed to handle dynamic conditions like partial shading more effectively. Our review focuses on integrating renewable energy sources with multiport converters, providing insights into a novel EV charging station framework optimized for EFC topology. We highlight the advantages of multiport non-isolated converters over traditional line frequency transformers, particularly in medium voltage scenarios, offering enhanced efficiency and versatility for EFC applications.

This paper introduces a novel modelling framework for conducting dynamic analysis of DC microgrids (MG), considering components such as battery energy storage systems (BESS), combined heat and power (CHP) cogeneration, photovoltaic (PV) plants and DC/DC converters. The MG dynamics are accurately captured using well-founded modelling practices of the power system transient analysis field. Firstly, the power device models are derived based on fundamental operating principles using a lumped-type parameter modelling approach. Secondly, the DC microgrid is formulated using nodal power injections, which is combined with Newton–Raphson and implicit trapezoidal methods to efficiently solve fair-sized networks of arbitrary topology. The dynamic outcomes of a 13-bus MG were compared with those of an electromagnetic transient (EMT) simulation conducted in Simscape Electrical for load and solar irradiance changes and for a DC/DC converter outage. For these events, errors smaller than 2.5% were yielded by the new approach, only requiring 3.1% of the computing time employed by the EMT simulation. The method’s applicability was also showcased by assessing the dynamic performance of a larger DC microgrid containing 118 buses, twelve distributed generators and five BESS, which was subjected to typical contingencies such as load increases and distribution line disconnections.

Mobility has modernized urban areas using an efficient transportation system. However, mobility demand growth has accelerated the expansion of conventional transportation, which significantly contributes to pollution. The need of Green transportation results in the eminence of electric vehicles (EVs). Besides, green mobility minimizes pollution from transportation systems and conventional power sources when EVs are optimally integrated into the utility grid. Thus, this study assesses different significant optimum possibilities of grid-connected EVs. A review of the critical impacts of grid-tied EVs is presented. Vehicle to the grid (V2G) is the future of electric cars. This uses a bidirectional power flow of the EV's battery charging to either charge the car or sustain the utility grid. The V2G is highly affected by diverse loading conditions that challenge the network's acceptable voltage and optimal power-sharing within the electrical network. It is observed that the V2G perspective is based on the 5Ds (decentralisation, de-carbonization, digitalization, deregulation, and democratization) vision to overcome the overall shortcomings in the modern power grid. The 5Ds vision of V2G implementation sustains different stakeholders working on the future of electric cars. Thus, this research is a stronger foundation for the new perspective and vision of V2G development and applications. [ABSTRACT FROM AUTHOR]

Power quality is the manifestation of a disruption in the supply voltage, current or frequency that damages the utility equipment and has become an important issue with the introduction of more sophisticated and sensitive devices. So, the supply power quality issue still remains a major challenge as its degradation can cause huge destabilization of electrical networks. As renewable energy sources have irregular nature, a microgrid essentially needs energy storage system containing advanced power electronic converters which is the root cause of majority of power quality disturbances. Also, the integration of non-linear and unbalanced loads into the grid adds to its power quality problems. This article gives a compact overview on the identification, categorization and mitigation of these power quality events in a microgrid by using various Artificial Intelligence-based techniques like Optimization techniques, Adaptive Learning techniques, Signal Processing and Pattern Recognition, Neural Networks and Fuzzy Logic.

In recent years, there has been a growing interest in using model-free deep reinforcement learning (DRL)-based controllers as an alternative approach to improve the dynamic behavior, efficiency, and other aspects of DC–DC power electronic converters, which are traditionally controlled based on small signal models. These conventional controllers often fail to self-adapt to various uncertainties and disturbances. This paper presents a design methodology using proximal policy optimization (PPO), a widely recognized and efficient DRL algorithm, to make near-optimal decisions for real buck converters operating in both continuous conduction mode (CCM) and discontinuous conduction mode (DCM) while handling resistive and inductive loads. Challenges associated with delays in real-time systems are identified. Key innovations include a chattering-reduction reward function, engineering of input features, and optimization of neural network architecture, which improve voltage regulation, ensure smoother operation, and optimize the computational cost of the neural network. The experimental and simulation results demonstrate the robustness and efficiency of the controller in real scenarios. The findings are believed to make significant contributions to the application of DRL controllers in real-time scenarios, providing guidelines and a starting point for designing controllers using the same method in this or other power electronic converter topologies.

This research focuses on the evolving dynamics of the power grid, where traditional synchronous generators are being replaced by non-synchronous power electronic converter (PEC)-interfaced renewable energy sources. The non-linear dynamics must be accurately modeled to ensure the stability of future converter-dominated power systems (CDPS). However, obtaining comprehensive dynamic models becomes more complex and computationally intensive as the system grows. This study proposes a scalable and automated data-driven partitioned modeling framework for CDPS dynamics. The method constructs reduced-ordered dynamic linear transfer function models using input-output measurements from a PEC switching model. Validation experiments were conducted on single-house and multi-house scenarios, demonstrating high accuracy (over 97%) and significant computational speed improvements (6.5 times faster) compared to comprehensive models. This framework and modeling approach offer valuable insights for efficient analysis of power system dynamics, aiding in planning, operation, and dispatch.

The operation of the electricity network has grown more complex due to the increased adoption of renewable energy resources, such as wind and solar power. Using energy storage technology can improve the stability and quality of the power grid. One such technology is flywheel energy storage systems (FESSs). Compared with other energy storage systems, FESSs offer numerous advantages, including a long lifespan, exceptional efficiency, high power density, and minimal environmental impact. This article comprehensively reviews the key components of FESSs, including flywheel rotors, motor types, bearing support technologies, and power electronic converter technologies. It also presents the diverse applications of FESSs in different scenarios. The progress of state-of-the-art research is discussed, emphasizing the use of artificial intelligence methods such as machine learning, digital twins, and data-driven techniques for system simulation, fault prediction, and life-assessment research. The article also addresses the challenges related to current research and the application of FESSs. It concludes by summarizing future directions and trends in FESS research, offering valuable information for further advancement and improvement in this field. [ABSTRACT FROM AUTHOR]

Real-time temperature monitoring of power semiconductors is a powerful tool for the predictive maintenance and lifetime prediction of power converters, and is typically performed by measuring temperature sensitive electrical parameters (TSEP). In this work, we demonstrate a machine learning-based method to estimate the on-state voltage of a real converter prototype featuring variable load of electric vehicle (EV) and photovoltaic (PV) systems under different ambient temperatures induced in a climate chamber. The method provides for the first-time insights on the uncertainties and feature importance for the predictions, and is aimed to be industrial compatible by applying only methods that are well established in industry. The approach is further flexible to any converter system, independently of its specifications. We have used the Support Vector Machine, K-Nearest Neighbors, and Decision Tree algorithms to predict the on-state voltage as functions of the readily measured parameters of a converter. We show that for the PV case, the K-Nearest Neighbor method yields the lowest mean absolute error of about 0.75 % for prediction, while for EV, the K-Nearest Neighbor algorithm gives the lowest mean absolute error of 5 %.

Electric vehicles (EVs) are a promising solution to reduce carbon dioxide (CO2) emissions, but this reduction depends on the fraction of renewable sources used to generate electricity. Wind energy is thus a vital candidate and has experienced a remarkable surge recently, establishing itself as a leading renewable power source worldwide. The research on Direct-Driven Permanent Magnet Synchronous Generator (PMSG)-based type 4 wind farms has indicated that the Phase-locked Loop (PLL) bandwidth significantly impacts Sub-Synchronous Resonance (SSR). However, the influence of PLL architecture on SSR remains unexplored and warrants investigation. Therefore, this paper investigates PLL architectural variations in PLL Loop Filter (LF) to understand their impact on SSR in type 4 wind farms. Specifically, an in-depth analysis of the Notch Filter (NF)-based enhanced PLL is conducted using eigenvalue analysis of the admittance model of a PMSG-based type 4 wind farm. The findings demonstrate that the NF-based enhanced PLL exhibits superior performance and improved passivity in the sub-synchronous frequency range, limiting the risk of SSR below 20 Hz. Additionally, Nyquist plots are employed to assess the impact on system stability resulting in increased stability margins. In the future, it is recommended to further investigate and optimize the PLL to mitigate SSR in wind farms. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive review on the employment of wide bandgap (WBG) semiconductor power devices in wind energy conversion systems (WECSs). Silicon-carbide- (SiC) and gallium-nitride (GaN)-based power devices are highlighted and studied in this review, focusing on their application in the wind energy system. This is due to their premium characteristics such as the operation at high switching frequency, which can reduce the switching losses, and the capability to operate at high temperatures compared with silicon (Si)-based devices. These advantages promote the replacement of the conventional Si-based devices with the WBG semiconductor devices in the new modular converter topologies due to the persistent demand for a more-efficient power converter topology with lower losses and smaller sizes. The main objective of this paper was to provide a comprehensive overview of the WBG power devices commercially available on the market and employed in the modular converter topologies for renewable energy systems. The paper also provides a comparison between the WBG power technologies and the traditional ones based on the Si devices. The paper starts from the conventional modular power converter topology circuits, and then, it discusses the opportunities for integrating the SiC and WBG devices in the modular power converters to improve and enhance the system's performance. [ABSTRACT FROM AUTHOR]

Recently, renewable energy is considered a vital source for electricity generation that aims to reduce the carbon dioxide emissions acquired from fossil fuels. Concentrated solar power (CSP) is a growing technology that collects solar energy from the sunbeams. One of the efficient CSP topologies is the solar power tower (SPT), which aims to collect the direct sunbeams on a central collector using thousands of reflecting mirrors, called heliostats. Many literature reviews have presented the development of control techniques to improve tracking accuracy and SPT performance. However, on the component level, little work has been issued. This article introduces a comprehensive review of the different SPT drives. More than 100 papers have been classified and discussed to allocate the development and the research-gaps in SPT drives. The drive mechanisms, considering both the power source and mechanical transmission systems, have been classified and discussed. Additionally, a comprehensive review of the different electrical motors, along with their power electronic converters, used for heliostat units are presented, discussed, and compared. The advantages and the drawbacks of the different electrical drive systems are presented and discussed. Besides, the azimuth-elevation tracking technique is selected, discussed, and investigated with a dual-axis two linear actuators mechanism. Additionally, a case study for a small-sized heliostat prototype is presented, discussed, and analyzed using the azimuth-elevation dual-axis tracking to investigate the SPT’s performance in Dhahran, KSA, as a promising location in the Gulf region. According to this review, the research gaps and future work have been highlighted to help interested researchers in this area finding potential challenges.

Abstract—Renewable sources are replacing diesel aerators for sustainable aquaculture. The power electronic converters are inevitable and should be highly reliable to minimize crop loss in aquaculture. Recently, application-specific reliability case studies have gained more attention in the research fraternity. In this regard, new mission-profile-based reliability studies are conducted on the power converters of the grid-integrated PV-Battery systems for the sustainable aquaculture aerator application. In addition, a new energy management scheme is also proposed based on the state-of-charge of the battery for the DC link and AC link configured grid-integrated PV-battery system to ensure continuous power to the aerator load for the given environmental and operating conditions. Furthermore, a critical reliability analysis is performed for both DC and AC link configurations considering the failure of the power device, power diode, and capacitor of each converter in the system. The reliability analysis is extended to the converter and system level; the comparison is made in terms of the lifetime of the whole system in both configurations. This study revealed that the AC link configuration had ensured 24 years more than the DC-link configuration for 10% population failure (B10 life) for the given load, mission profile, and energy management system. [ABSTRACT FROM AUTHOR]

The manuscript presents a software implementation of methods for the optimal design of power electronic devices based on MATLAB. Modelbased optimization is one of the effective approaches to guarantee the performance of power electronic devices and systems. Through its application, the efficiency of the conversion of electrical energy is increased when setting a certain target function. Several examples are presented to demonstrate the effectiveness of the proposed optimization. [ABSTRACT FROM AUTHOR]

In recent years, renewable energy generation, storage, and transmission has been the focus of research. Extraction of power from renewable energy sources is increasing rapidly. Progressively more wind farms are being fastened to the power grid. Large-scale merging of wind farms into electrical power grid presents a few challenges like voltage stability, system operation and control, and power quality due to usage of power electronic converters presenting a major bottleneck. This paper elucidates various types of wind power plant technologies, consequences of power electronic converters and reviews various types of system strength determination methods. [ABSTRACT FROM AUTHOR]

Supplying shipping vessels with electricity at the ports can improve the air quality of the ports, reduce the greenhouse gas emissions from the shipping industry, contribute to the economic growth, and increase the political dependency of the countries by reducing the dependence on conventional fossil fuels. Several countries promote supplying the vessels when they are docking at ports from renewable energy systems by establishing dedicated funding mechanisms to remove the obstacles facing the shore power systems. In this context, this paper presents a new modular power electronic converter for shore power systems at shipping ports, which can perform three functions. Firstly, it will harvest the energy from a renewable energy source, such as hydroelectric and solar photovoltaic (PV) sources and ensure maximum energy extraction. Secondly, it will control the power flow from these sources to the battery storage. Finally, it will control the power flow from the battery to the vessels and/or the utility grid when necessary. A current-source converter based on isolated Cuk converter is used as the submodule (SM) of the proposed modular converter due to several features. The Cuk SM can provide high efficiency, minimised dc capacitance, and flexible output voltage higher or lower than the input voltage from the PV modules. To verify the mathematical analyses and computer simulations, experimental results are obtained from a small-scale modular prototype controlled by a TMSF28335 DSP. [ABSTRACT FROM AUTHOR]

Ocean energy systems (OESs) convert the kinetic, potential, and thermal energy from oceans and seas to electricity. These systems are broadly classified into tidal, wave, thermal, and current marine systems. If fully utilized, the OESs can supply the planet with the required electricity demand as they are capable of generating approximately 2 TW of energy. The wave energy converter (WEC) systems capture the kinetic and potential energy in the waves using suitable mechanical energy capturers such as turbines and paddles. The energy density in the ocean waves is in the range of tens of kilowatts per square meter, which makes them a very attractive energy source due to the high predictability and low variability when compared with other renewable sources. Because the final objective of any renewable energy source (RES), including the WECs, is to produce electricity, the energy capturer of the WEC systems is coupled with an electrical generator, which is controlled then by power electronic converters to generate the electrical power and inject the output current into the utility AC grid. The power electronic converters used in other RESs such as photovoltaics and wind systems have been progressing significantly in the last decade, which improved the energy harvesting process, which can benefit the WECs. In this context, this paper reviews the main power converter architectures used in the present WEC systems to aid in the development of these systems and provide a useful background for researchers in this area. [ABSTRACT FROM AUTHOR]

The operation of the electricity network has grown more complex due to the increased adoption of renewable energy resources, such as wind and solar power. Using energy storage technology can improve the stability and quality of the power grid. One such technology is flywheel energy storage systems (FESSs). Compared with other energy storage systems, FESSs offer numerous advantages, including a long lifespan, exceptional efficiency, high power density, and minimal environmental impact. This article comprehensively reviews the key components of FESSs, including flywheel rotors, motor types, bearing support technologies, and power electronic converter technologies. It also presents the diverse applications of FESSs in different scenarios. The progress of state-of-the-art research is discussed, emphasizing the use of artificial intelligence methods such as machine learning, digital twins, and data-driven techniques for system simulation, fault prediction, and life-assessment research. The article also addresses the challenges related to current research and the application of FESSs. It concludes by summarizing future directions and trends in FESS research, offering valuable information for further advancement and improvement in this field.

Power electronic systems present a non-linear behavior mainly due to their switching nature. This is often combined with their interaction with non-linear systems, such as other switching converters, diode rectifiers, motor drives, etc. and with possible non linearities of the power grid in the case of grid connected systems. The major effect of these non-linear interactions is the generation of harmonic distortion on voltages and currents (both in DC and AC), which needs to be compensated to achieve high power quality systems. The use of passive filters is often the simplest and most immediate solution; however, this decreases converter efficiency and increases its weight and volume. Thus, the use of a control strategy capable of tracking periodic signals, rejecting periodic disturbance and largely improving steady state behavior and harmonic distortion with a limited bandwidth is a very desirable feature. Repetitive Control (RC) represents an extremely practical and efficient solution for the aforementioned issues, and it is widely employed in many different applications. This paper focuses on state of the art of RC used in power electronics and drives. RC basic concepts, different control structures, design methods, fixed and variable frequency operating conditions, etc. are investigated. Furthermore, many example applications and existing control approaches developed in recent years for power electronics and drive systems based on RC, have also been discussed in detail.

Globally,wind power plants (WPPs) installations are dramatically increasing, leading to a large-scale integration into the power grids. Huge endeavors are devoted to achieving an efficient operation of WPPs. This paper offers a novel chaotic-billiards optimizer (C-BO) algorithm to properly design the fuzzy logic control (FLC) strategy for stability enhancement of grid-integrated WPPs. The C-BO algorithm has distinct features such as simple construction, low numbers of parameters required to design, high convergence speed, and low computational burden. The permanent-magnet synchronous generator (PMSG)-based WPP is interlinked into the electric power grid through power converters. A cascaded FLC approach, which is optimally fine-tuned by the C-BO, is offered as the control methodology for these converters. The C-BO algorithm fine-tunes the gain factors of multiple FLCs at a rapid convergence speed. A simulation-based optimization approach is applied, in which the integrated square error criterion is chosen as an objective function. For the sake of preciseness, the PMSG-based WPP is connected to the IEEE 39-bus New England test system. The effectiveness of the optimal FLC using the C-BO algorithm is compared with that achieved using the water cycle algorithm-based optimal FLC, the genetic algorithm-based optimal FLC, and the marine predator algorithm-based optimal proportional-integral controller, taking into account severe grid disturbances. Moreover, real wind speed measured data captured from Zaafarana Station in Egypt are extensively performed in the system studies. The validity of the offered control approach is widely verified by the simulation analyses using the MATLAB/Simulink environment. [ABSTRACT FROM AUTHOR]

In modern dc shipboard microgrid (SMG) systems, the propulsion motors and hotel loads are always supplied through tightly regulated point of load converters, which behave as constant power loads (CPLs). The negative incremental impedance due to CPL’s characteristics destabilizes the dc bus voltage of dc SMGs. Due to uncertain operating conditions of maritime ships on the sea, the dc bus voltage robust control is a crucial matter. Therefore, this paper presents a cutting-edge systematic review on advanced nonlinear control strategies to stabilize and control the CPLs in dc SMGs, such as sliding mode control, synergetic control, backstepping control, model predictive control, and passivity-based control. The latest stabilization techniques and the future trends towards an adaptive nonlinear control have been presented throughout this review. Several feedforward control-based observation and estimation techniques have been highlighted. The stability analysis and stability challenges of dc SMGs are also discussed. [ABSTRACT FROM AUTHOR]

Power electronics is seeing an increase in the use of sophisticated self-learning controllers as single board computers and microcontrollers progress faster. Traditional controllers, such as PI controllers, suffer from transient instability difficulties. The duty cycle and output voltage of a DC/DC converter are not linear. Due to this non-linearity, the PI controller generates variable levels of voltage fluctuations depending on the operating region of the converter. In some cases, non-linear controllers outperform PI controllers. The non-linear model of a non-linear controller is determined by data availability. So, a self-calibrating controller that collects data and optimizes itself as the operation goes on is necessary. Iteration and oscillation can be minimized with a well-trained reinforcement learning model utilizing a non-linear policy. A boost converter's output power supply capacity changes with a change in load, due to which the maximum duty cycle limit of a converter also changes. A support vector calibrated by reinforcement learning can dynamically change the duty cycle limit of a converter under variable load. This research highlights how reinforcement learning-based non-linear controllers can improve control and efficiency over standard controllers. The proposed concept is based on a microgrid system. Simulation and experimental analysis have been conducted on how reinforcement learning-based controller works for DC-DC boost converter.

The rely on internal combustion engines is gradually decreased with the recent evolution of electric vehicles (EVs) in the automotive industry. Electric motors are replacing the energy systems mainly to improve the powertrain's efficiency and ensure they are environmentally friendly. These novel powertrains are designed to operate solely on batteries or supercapacitors. For these types of EVs, the battery is charged using an alternating current supply in connection to the grid in the case of plug-in electric vehicles. Internal combustion engines are equally used for some hybrid vehicles. Charging of the battery can also be carried out via regenerative braking from the traction motor. This study presents a brief background about the different available EVs, detailed information on various power converter electronics used in battery electric vehicles, and a summary of the strengths (S), weaknesses (W), opportunities (O), and threats (T) of the EV is presented. Moreover, SWOT analysis of the battery electric vehicles (BEV) and their prospects in the automotive industry are introduced.

Electric vehicles (EVs) are a promising solution to reduce carbon dioxide (CO2) emissions, but this reduction depends on the fraction of renewable sources used to generate electricity. Wind energy is thus a vital candidate and has experienced a remarkable surge recently, establishing itself as a leading renewable power source worldwide. The research on Direct-Driven Permanent Magnet Synchronous Generator (PMSG)-based type 4 wind farms has indicated that the Phase-locked Loop (PLL) bandwidth significantly impacts Sub-Synchronous Resonance (SSR). However, the influence of PLL architecture on SSR remains unexplored and warrants investigation. Therefore, this paper investigates PLL architectural variations in PLL Loop Filter (LF) to understand their impact on SSR in type 4 wind farms. Specifically, an in-depth analysis of the Notch Filter (NF)-based enhanced PLL is conducted using eigenvalue analysis of the admittance model of a PMSG-based type 4 wind farm. The findings demonstrate that the NF-based enhanced PLL exhibits superior performance and improved passivity in the sub-synchronous frequency range, limiting the risk of SSR below 20 Hz. Additionally, Nyquist plots are employed to assess the impact on system stability resulting in increased stability margins. In the future, it is recommended to further investigate and optimize the PLL to mitigate SSR in wind farms.

This paper presents a comprehensive review on the employment of wide bandgap (WBG) semiconductor power devices in wind energy conversion systems (WECSs). Silicon-carbide- (SiC) and gallium-nitride (GaN)-based power devices are highlighted and studied in this review, focusing on their application in the wind energy system. This is due to their premium characteristics such as the operation at high switching frequency, which can reduce the switching losses, and the capability to operate at high temperatures compared with silicon (Si)-based devices. These advantages promote the replacement of the conventional Si-based devices with the WBG semiconductor devices in the new modular converter topologies due to the persistent demand for a more-efficient power converter topology with lower losses and smaller sizes. The main objective of this paper was to provide a comprehensive overview of the WBG power devices commercially available on the market and employed in the modular converter topologies for renewable energy systems. The paper also provides a comparison between the WBG power technologies and the traditional ones based on the Si devices. The paper starts from the conventional modular power converter topology circuits, and then, it discusses the opportunities for integrating the SiC and WBG devices in the modular power converters to improve and enhance the system’s performance.

Solar photovoltaic (SPV) modules have a low output voltage and are load-dependent. Therefore, it is critical that the SPV system has an adequate DC–DC converter to regulate and improve the output voltage to get maximum output voltage. To meet load requirements, the voltage must be increased, necessitating the use of energy-efficient power electronic converters. The performance of an SPV system coupled to a high-gain quadratic boost converter (HG-QBC) with a load is investigated in this paper. The suggested HG-QBC for the SPV system at a lower value of duty ratio provides high voltage gain with a boost factor of four times. An analytical comparison is carried out with the various existing boost converters in terms of the components and the boost factor. The issue of locating the maximum power generation point from the SPV system is crucial. As a result, choosing an appropriate maximum power point tracker (MPPT)-based technique to obtain the peak power output of the SPV system under the rapidly varying atmospheric conditions is vital. To determine the highest output power of an SPV system, a hybrid-based MPPT with a neural network assisted by a perturb and observe (P&O) technique is proposed. For the HG-QBC, a comparison of the proposed MPPT with a traditional P&O-based MPPT is illustrated. The comparative analysis takes into account rise time, settling time and voltage ripples. The output voltage and power characteristics of the proposed model are analysed under constant and varying irradiation conditions using MATLAB®/Simulink®. The results of a hybrid-based MPPT show that the oscillations are minimum at the maximum power point with fewer ripples of 0.20% and a settling time of 1.2 s in comparison with the other two techniques. [ABSTRACT FROM AUTHOR]

This paper presents an improved structure for the submodules (SMs) in the three-phase modular inverter (TPMI) based on a dual isolated SEPIC/CUK (DISC) converter for large-scale photovoltaic (LSPV) plants. The DISC SMs can offer several advantages, including increased efficiency, reduced passive elements, and galvanic isolation via compact-size high-frequency transformers. The SMs can also provide a wide range for the output voltage and draw continuous currents with low ripples from the input source. However, the high dv/dt value across the switches during hard switching can cause current oscillations and voltage spikes, which will impair the operation of complementary switches and affect the safety of the power devices. For this challenge, the DISC SM is improved by replacing the output switches with diodes and adding a bypassing switch. In comparison to the conventional DISC SM, the improved DISC SM reduces the switch's voltage spikes; hence, it can increase the overall efficiency. Thus, the DISC SM's will be able to suppress voltage spikes in the TPMI inverter and therefore the total reliability will be improved. The work will detail the analysis of the proposed system along with design guidelines. Additionally, the simulation and experimental results to validate the operation of proposed DISC SM are presented using MATLAB/SIMULINK as well as a scaled-down experimental prototype. [ABSTRACT FROM AUTHOR]

• Efficient dynamic modelling framework for the analysis of fair-sized DC microgrids. • Nodal formulation combined with Newton–Raphson and implicit trapezoidal methods. • BESS, CHP units, PV plants and series connected DC/DC converters can be studied. • Results are validated versus EMT simulation conducted in Simscape Electrical. • Small errors are obtained with a small fraction of time taken by the EMT simulation. This paper introduces a novel modelling framework for conducting dynamic analysis of DC microgrids (MG), considering components such as battery energy storage systems (BESS), combined heat and power (CHP) cogeneration, photovoltaic (PV) plants and DC/DC converters. The MG dynamics are accurately captured using well-founded modelling practices of the power system transient analysis field. Firstly, the power device models are derived based on fundamental operating principles using a lumped-type parameter modelling approach. Secondly, the DC microgrid is formulated using nodal power injections, which is combined with Newton–Raphson and implicit trapezoidal methods to efficiently solve fair-sized networks of arbitrary topology. The dynamic outcomes of a 13-bus MG were compared with those of an electromagnetic transient (EMT) simulation conducted in Simscape Electrical for load and solar irradiance changes and for a DC/DC converter outage. For these events, errors smaller than 2.5% were yielded by the new approach, only requiring 3.1% of the computing time employed by the EMT simulation. The method's applicability was also showcased by assessing the dynamic performance of a larger DC microgrid containing 118 buses, twelve distributed generators and five BESS, which was subjected to typical contingencies such as load increases and distribution line disconnections. [ABSTRACT FROM AUTHOR]

This article develops a survivability-based protection for three-phase induction motor drive ($3\phi$ IMDs) systems against severe dynamic events. The developed protection is based on defining a survivability index $\Gamma _{\text{IMD}}$ , which is formulated in terms of the change in power created by a dynamic event experienced by a $3\phi$ IMD. The determination of $\Gamma _{\text{IMD}}$ provides an accurate tool to detect, identify, and respond to unsurvivable dynamic events. The efficacy and validity of the survivability-based protection for $3\phi$ IMDs are verified through simulation and experimental tests. These tests are conducted for 10 hp $3\phi$ IMD under various types of dynamic events. Test results demonstrate the effectiveness of the survivability-based protection in responding to unsurvivable dynamic events to prevent damage to different components in a $3\phi$ IMD. [ABSTRACT FROM AUTHOR]

Power electronic converter can produce massive voltage stresses on the connected transformer, with great effects on electrical ageing of the insulation. Such effects can be diverse upon different insulation components under different harmonic voltages, which have not been well clarified. This article aims at enhancing online monitoring of converter transformers from two aspects. First, electrical ageing evaluation models are developed for different types of transformer insulation. It is the first time to clarify the effects related to the component location and the ageing severity under differential-mode and common-mode (CM) voltages. Interlayer (IL) insulation faces a serious ageing threat and the line-lead component is aged with the severest degree. Second, CM voltages are used to build transfer function (TF) measurement for IL insulation monitoring. The key is to identify the optimal monitoring frequency with the highest sensitivity. Based on the analytical case introducing typical parameters, the TF performance is analyzed in different ageing conditions. Online experiments validate that the proposed method is effective and has good application prospect. The contributions lie in that the diverse ageing effects of converter harmonic voltages on different insulation components can be distinguished and online monitoring of IL insulation can be achieved effectively. [ABSTRACT FROM AUTHOR]

This paper proposes a simplified analytical model for electromagnetic interference (EMI) filters used in high-power converters. High-power converters produce radio frequency conducted noise because they use high-frequency switching in the range of a few kHz to tens of kHz. The noise propagates into the power grid, which disturbs the functionality of the radio frequency apparatuses. Well-known standards, such as CISPR, provide the measurement and assessment methodologies for these devices. Moreover, the emission level of the noise is restricted at the source side. Using EMI filters is the most effective approach for dealing with this issue. However, due to the nonlinear nature of the common-mode (CM) cores, the modeling of the cores is a complicated task, which makes the selection and design of the filters less than optimal. In this paper, an analytical modeling of the CM filters is provided to suit the nonlinear frequency-dependent behavior of the CM cores. The simplicity of the proposed model makes it a very suitable choice for inclusion in the design procedures, which results in a more accurate and optimum filter selection among the many available commercial industrial filters. To validate the proposed model, the frequency-dependent model obtained is verified by experimental tests with a commercial CM choke. According to the results, the proposed model accurately describes the actual EMI filter behavior.

To enable more wide-scale exploitation of renewable energy sources (RESs), distributed generations (DGs) as in microgrids have gained prominence recently. This study describes the design, modeling, implementation, and operation of a microgrid, in which a standalone hybrid power system has been installed for an education and research laboratory. The unstable nature of RESs results in power compatibility issues on DGs. Therefore, providing a decent power flow from renewables to loads is the scope of this paper. To satisfy defined load profiles and sustain the power with the desired level, the design and operation of power converters are a remarkable part of performing microgrids as well. For a robust microgrid structure, the presented control algorithm includes an energy management system (EMS) between renewables, batteries, and loads. Primarily, modeling of the system has been developed in MATLAB/Simulink environment. Additionally, case studies have been exemplified to further demonstrate the simulated system. Within this scope, certain load profiles not only have been fed but also power flow has been managed and analyzed to ensure effective and flexible operation with two different EMS cases. The real-time operation has been also provided to validate the system under various input and output conditions during a lab course.

The power electronics industry is undergoing a revolution driven by an industry 4.0 perspective, with smart and green/hybrid energy management systems being the requirement of the future. There is a need to highlight the potential of fractional order control in power electronics for the highly efficient systems of tomorrow. This paper reviews the developments in fractional order control in power electronics ranging from stand-alone power converters, industrial drives and electric vehicles to renewable energy systems and management in smart grids and microgrids. Various controllers used in power electronics such as the fractional order PI/PID (FOPI/FOPID) and fractional-order sliding mode controllers have been discussed in detail. This review indicates that the plug-and-play type of intelligent fractional order systems needs to be developed for our sustainable future. The review also points out that there is tremendous scope for the design of modular fractional-order intelligent controllers. Such controllers can be embedded into power converters, resulting in smart power electronic systems that contribute to the faster and greener implementation of industry 4.0 standards.

In a microgrid, photovoltaic (PV) systems are broadly preferred with energy storage systems (ESSs) that form small-sized direct current (DC) microgrids. They are also termed local grids i.e., DC nanogrids, which feed the local consumers to some extent in the next decades. Therefore, ESSs enable the DC nanogrids more flexible and stable by preserving the intermittent nature of renewables. Yet still, feeding local consumers smoothly with PV-battery-based systems is exceedingly a considerable theme. In this context, proper control of power electronics converters as the main carrier of the system is essential. Besides, the rise of PV applications challenges possible issues upon integrating the conventional grid. Emerging possible issues in stability, reliability, efficiency and the ways of dealing with them have been developing day by day. Thus, it is inevitable that innovative methods will be put into practice. To achieve this goal, the deep learning aided-sensorless control approach is adopted. To validate the proposed control method, the training phase is presented elaborately with the help of the experimental setup of a DC nanogrid. From the obtained results, it is concluded that the deep learning-based approach reaches very small error values, captures the system dynamics successfully, enables a flexible structure with tunable hyper-parameters, and allows the possibility to apply practically.

Electrical energy is the most efficient and the cleanest form of energy at the moment that is being transmitted and distributed amongst end-users. From its earlier days, the AC system was preferred as an economical solution for transmission and distribution. However, the development in the power electronics technology and the evolution of highly efficient power electronic converters have established the resurgence of DC power system. Furthermore, the trend is shifting towards DC loads as various energy efficient appliances, such as DC inverter air conditioners, operate on DC nowadays. This further advocates the shift towards the DC power system. This research works is an effort to perform the comparative analysis of AC Distribution System (ACDS) and DC Distribution System (DCDS), with regards to power quality and harmonic distortion in particular. The comparison is performed considering load profile and load variation on daily basis. Simulations are performed in MATLAB. It has been concluded at the end that ACDS is better than DCDS in terms of power quality as total harmonic distortion of the DCDS under the same loading and same load variation during the whole day was significantly higher than that of ACDS.

Replacing conventional generation by power electronics based generation changes the dynamic characteristics of the power system. This results among others in the increased susceptibility to sub synchronous oscillations (SSO). This paper proposes a systematic procedure for mitigating the interactions between a DFIG and a series compensated transmission line using the phase imbalance compensation (PIC) concept. The impact of the series and parallel PIC on the resonance behaviour of the grid is first thoroughly investigated. Then, the influence of the system strength on the capabilities of the PIC to mitigate DFIG-SSR is assessed. Based on the findings a design framework which enables the systematic assessment of the series and parallel PIC for mitigating DFIG-SSR is developed and successfully implemented in the modified IEEE 39 bus system. Comparison between both concepts reveals that the parallel PIC is better suited to mitigate DFIG-SSR. The impedance based stability analysis and detailed time domain electromagnetic transient (EMT) simulations are used to respectively screen and validate the results. [ABSTRACT FROM AUTHOR]

In Part I, a method for the Harmonic Power-Flow (HPF) study of three-phase power grids with Converter-Interfaced Distributed Energy Resources (CIDERs) is proposed. The method is based on generic and modular representations of the grid and the CIDERs, and explicitly accounts for coupling between harmonics. In Part II, the HPF method is validated. First, the applicability of the modeling framework is demonstrated on typical grid-forming and grid-following CIDERs. Then, the HPF method is implemented in MATLAB and compared against time-domain simulations with Simulink. The accuracy of the models and the performance of the solution algorithm are assessed for individual resources and a modified version of the CIGRÉ low-voltage benchmark microgrid (i.e., with additional unbalanced components). The observed maximum errors are 6.3E-5 p.u. w.r.t. voltage magnitude, 1.3E-3 p.u. w.r.t. current magnitude, and 0.9 deg w.r.t. phase. Moreover, the scalability of the method is assessed w.r.t. the number of CIDERs and the maximum harmonic order (≤25). For the maximum problem size, the execution time of the HPF method is 6.52 sec, which is 5 times faster than the time-domain simulation. The convergence of the method is robust w.r.t. the choice of the initial point, and multiplicity of solutions has not been observed. [ABSTRACT FROM AUTHOR]

Power distribution systems are experiencing a large-scale integration of Converter-Interfaced Distributed Energy Resources (CIDERs). This complicates the analysis and mitigation of harmonics, whose creation and propagation are facilitated by the interactions of converters and their controllers through the grid. In this paper, a method for the calculation of the so-called Harmonic Power-Flow (HPF) in three-phase grids with CIDERs is proposed. The distinguishing feature of this HPF method is the generic and modular representation of the system components. Notably, as opposed to most of the existing approaches, the coupling between harmonics is explicitly considered. The HPF problem is formulated by combining the hybrid nodal equations of the grid with the closed-loop transfer functions of the CIDERs, and solved using the Newton-Raphson method. The grid components are characterized by compound electrical parameters, which allow to represent both transposed or non-transposed lines. The CIDERs are represented by modular linear time-periodic systems, which allows to treat both grid-forming and grid-following control laws. The method’s accuracy and computational efficiency are confirmed via time-domain simulations of the CIGRÉ low-voltage benchmark microgrid. This paper is divided in two parts, which focus on the development (Part I) and the validation (Part II) of the proposed method. [ABSTRACT FROM AUTHOR]