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Brushless DC (BLDC) motor drives are extensively preferred in the automotive industry for their high torque‐to‐weight ratio. In conventional BLDC drives, the torque ripples are high due to 120° conduction mode operation and nonideal back EMF. To minimize the torque ripple in BLDC drives, a flux trajectory is employed based on the extension Park transformation d‐q axis frame. This article introduces a control scheme for BLDC drives that integrate maximum torque per ampere (MTPA) with pseudo–field‐oriented control (FOC). This approach gets an optimal reference d‐axis current (Ids∗) that optimize the current magnitude Is2) and minimize copper loss. Furthermore, the 180o conduction mode is used to minimize the torque ripple and provide high dynamic performance of drives. The efficacy of the proposed MTPA‐based pseudo‐FOC strategy is experimentally validated by employing a 0.75‐kW BLDC motor drive using real‐time interface controller dSPACE 1202 and Danfoss VLT 302 converter. [ABSTRACT FROM AUTHOR]

DC microgrids are getting more attention because majority of the renewable energy sources generate DC output voltage and also modern gadgets require DC voltage for its operation. In this work, high gain SEPIC (HGSC) topology is derived from switched inductor voltage boosting cell (SIVBC). The HGSC converter provides continuous source current due to SIVBC and high conversion ratio and achieves maximum efficiency of 97.88% when compared with the existing SEPIC topology. The operating modes, conversion ratio expression, power loss distribution, voltage drop, current stress of the semiconductor devices, and efficiency are also analysed. In DC microgrids, the HGSC intends to track the peak power from solar PV array. An incremental conductance algorithm is employed to track the peak power of the solar PV modules. The power flow in the microgrid system is analysed by employing synchronous reference frame theory‐based current controller. In order to validate the theoretical concepts of the HGSC converter, the hardware model is developed for the load rating of 1,000 W/380 V output voltage. [ABSTRACT FROM AUTHOR]

The manuscript proposes the design and implementation of a photovoltaic-assisted dynamic voltage restorer with fuzzy-logic control (FLC)-based Improved Second-order Generalized Integrator (I-SOGI) scheme to improve power quality (PQ) in the power system by using an enhanced approach. The proposed method utilizes Firebug Swarm Optimization (FSO) which is motivated by the reproductive swarming behavior of Firebugs. The swarming behavior of FSO is enhanced by using two operators called mutation and crossover. Hence, it is termed as Enhanced FSO (En-FSO) method. Power quality issues considered include harmonic distortions and voltage sag/swell. The Z-source inverter (ZSI) by compensating the dynamic voltage restorer (DVR's), fed voltage into the grid through a series transformer. Long-term balanced voltage sags, swells, unbalanced sags, swells, and interruptions of voltage are all offset by the ZSI-DVR. Using the suggested control method, the total harmonic distortion (THD) of load voltage is kept below 5%. The FLC-based I-SOGI control method for generating the reference signals for voltage compensation enhances a considerable parameter tweaking. The entire analysis demonstrates that the proposed FLC-based I-SOGI control technique effectively mitigates voltage sag, swell, and harmonics while supplying solar photovoltaic (SPV) power to the grid. The simulation and experimental verification of the proposed control algorithm successfully maintains pure sinusoidal load voltage in a variety of test conditions. The results are compared with existing methods to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a new double mover configuration of a linear switched reluctance motor (LSRM). The proposed design is established for optimization of the motional forces and to ensure a high-grade electromechanical energy conversion process. The major drawback of the traditional linear machine is that its force densities within and throughout its area is produced in its radial direction of the yoke and does not contribute to its motion or twisting force of the rotor. If these normal forces happen to be in the direction of motion, a larger motional force profile for SRM is yielded. Based on these guidelines, a new LSRM is developed. In order to compare the energy conversion efficiency of LSRM with that of the conventional SRM, a finite element model is constructed. The proposed system is simulated using the FEM software and tested under four conditions. Further, an experimental prototype of the proposed machine is also developed and tested in the laboratory. The results obtained from the prototype indicate that the proposed geometry offers superior performance in terms of high-power density and higher percentage of the national forces. [ABSTRACT FROM AUTHOR]

The present research work focused on optimizing the input parameters of the automated TIG welding process of SS304 metal. Since SS304 possesses high inter‐granular toughness and corrosion resistance with increased lifespan in the pressure vessel and automobile sector, SS304 is mainly referred for gas arc welding compared with SS 202. The SS304 workpieces of 60 mm × 40 mm × 4 mm with dimension are utilized in the experiment and the same metal has been used as the filler material. The TIG welding experimental lab‐scale setup utilizes a 2‐axis servo workbench programmed with PLC to perform an automatic trajectory path using Taguchi design of optimization to obtain the optimal welding parameters for the SS304 welding process. To analyze the influence of welding current, welding speed, gas flow rate, and welding arc length on the tensile strength and hardness based on predicted R‐squared, p‐value and co‐efficient of the sum of squares from are verified. From the regression analysis, the predicted model R‐squared value holds 95.78% and 94.83% for the hardness and tensile strength respectively associating with the actual coefficient confirming the model which has maximum precision. Further, it is inferred that on increasing welding current and welding speed, the hardness of the welded joints seems to increase whereas when the minimum gas flow rate is maintained, the tensile strength of the SS304 decreases drastically. Overall, among the four input factors, the welding current is a major influencing parameter on the SS304 which is directly proportional to tensile strength and hardness. [ABSTRACT FROM AUTHOR]

The accurate classification of power quality disturbances (PQDs) is crucial for advancing real‐time monitoring and classification systems within the modern power grid. The proposed system must ensure dependable, safeguarded, and stable operating conditions amidst diverse power quality issues. This paper presents an approach to classifying power quality disturbances using a deep learning model that synergizes deep convolutional neural networks (DCNN) and Bidirectional Long Short‐Term Memory (BiLSTM). This amalgamation effectively extracts and classifies disturbance signals in real time, grounded on noise levels. The initial feature extraction from the signal is accomplished through a time‐frequency matrix. Subsequently, secondary extraction employs the BiLSTM layer to intricately and significantly classify disturbances in the power signal. This aids in transforming high‐dimensional matrices into a reduced set for enhanced performance. The detailed classification is facilitated by the softmax layer. The simulation results support the power quality evaluations under varied constraints and underscore the substantial classification of power quality disturbances through the DCNN‐BiLSTM algorithm, in comparison to alternative classification algorithms in terms of computational speed and accuracy. [ABSTRACT FROM AUTHOR]

Pulse width modulation for Selective Harmonics Elimination (SHE) is mostly employed in the reduction of lower order harmonics. The PV system in this research provides input voltage to the reduced switch 31-level inverter, which is based on the Artificial Bee Colony algorithm. With a high gain DC-DC single-ended primary-inductor converter (SEPIC), the PV panel output voltage is kept constant. The Grey wolf optimization algorithm (GWO) approach is used to get the most power out PV scheme. Multi Carrier modulation, a high-frequency modulation technology, is also used in this novel design of the inverter to reduce upper order harmonics. The suggested Artificial Bee Colony (ABC) algorithm, harmonics is compared to a SHE technique based on a genetic algorithm. The hardware findings were confirmed using DSPIC30F2010 controller simulation, and the recommended system was validated using Matlab simulation. [ABSTRACT FROM AUTHOR]

Given the various aspects of climate change and the growing demand for energy, energy efficiency and environmental protection have become major concerns worldwide. If not taken care of, energy demand will become unmanageable due to technological growth in cities and nations. The solution to the global energy crisis could be an advanced two-way digital power flow system that is capable of self-healing, interoperability, and predicting conditions under various uncertainties and is equipped with cyber protections against malicious attacks. The smart grid enables the integration of renewable energy sources such as solar, wind, and energy storage into the grid. Therefore, the perception of the smart grid and the weight given to it by researchers and policymakers are of utmost importance. In this paper, the studies of many researchers on smart grids are examined in detail. Based on the literature review, various principles of smart grids, the development of smart grids, functionality of smart grids, technologies of smart grids with their characteristics, communication of smart grids, problems in the implementation of smart grids, and possible future studies proposed by various researchers have been presented. [ABSTRACT FROM AUTHOR]

This article describes the electromagnetic analysis of high efficient hybrid motor, which comprises the salient features of switched reluctance motor (SRM) and spoke‐type brushless DC motor. The main objective is to develop a motor with a high‐power density and winding faulty capability. Furthermore, this research article extends in the manner to increase the power density of the motor through the sensitivity analysis on rotor geometry by replacing the rotating part of SRM and adopting the rotor of spoke type brushless DC motor, originating the hybrid motor with the high‐power density and enhanced efficiency. To ensure the winding fault capability, a SRM‐based stator winding is adopted. Then, the modelling process for hybrid motor 48 V, 1500 RPM, 2 kW, and 12.7 Nm are detail in both analytical and finite element methods. The electromagnetic analysis is carried out to estimate the torque characteristics and flux pattern of the proposed motor. Furthermore, the proposed motor is analysed with the selection of laminating core material among M 27 24 Ga, 36F155, 46F165, 47F165, M 420 50D, and arnon 7. This infers 36F155 material assists proposed motor has high‐performance characteristics. The vibration frequencies are investigated in modal aspects to estimate the natural frequencies of vibrations. These analyses are validated among analytical and finite element results under no‐load conditions. [ABSTRACT FROM AUTHOR]

Many progressed information scientific strategies, particularly Artificial Intelligence (AI) and profound learning methods, have been proposed and tracked down wide applications in our general public. This proposition creates information driven arrangements by utilizing the most recent profound learning and AI innovation, including outfit learning, meta‐learning and move learning, for energy the executives framework issues. Genuine world datasets are tried on proposed models contrasted and best in class plans, which exhibit the predominant presentation of the proposed model. In this proposition, the engineering of the Smart Grid testbed is additionally planned and created by using ML calculations and true remote correspondence frameworks to such an extent that constant plan necessities of Smart Grid testbed is met by this reconfigurable system with stacking of full convention in medium access control (MAC) and physical layers (PHY). The proposed engineering has the reconfiguration property in view of the organization of remote correspondence and trend setting innovations of Information and communication technologies (ICT) which incorporates Artificial Intelligence (AI) calculation. The fundamental plan objectives of the Smart Grid testbed is to make it simple to construct, reconfigure and scale to address the framework level prerequisites and to address the ongoing necessities. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient fuzzy logic-based fault detection scheme for diagnosing the inter-turn short-circuit (ITSC) faults in induction motors (IMs). The proposed approach utilizes the fast Fourier transforms (FFTs) and wavelet packet transform (WPT) for this detection of fault. To improve the efficiency and secure the operation, the proposed approach is detecting the fault in online manner. The WPT is utilized to extract the stator current signal into time-frequency domain characteristics. The variation in the amplitude of the vibration spectrum at different characteristic frequencies by FFT is utilized to identify the stator ITSC. The vibration signal is dignified by a MEMS accelerometer. The performance of the fuzzy logic fault detector (FLFD) for online condition is monitored with stator current, vibration and input speed. The performance of the proposed approach is performed at MATLAB/Simulink working site, and then the performance is compared to other existing works. The accuracy, precision, recall and specificity of the proposed approach are analyzed. Similarly, the statistical measures like root mean square error (RMSE), mean absolute percentage error (MAPE), mean bias error (MBE) and consumption time are analyzed. [ABSTRACT FROM AUTHOR]

Photovoltaic‐powered electric transportation systems are gaining global momentum owing to their superior enactment and zero carbon emissions. With a growing number of electric vehicles (EVs) on the road, implementation of efficient and well‐organised charging stations is extremely indispensable. The authors investigate the feasibility of creating a charging station for plug‐in‐hybrid EVs at an educational institution, E.G.S. Pillay Engineering College, Nagapattinam, Tamil Nadu, India (10°48.2′N, 79°50.1′E). The statistics related to the solar radiations of the Nagapattinam region are employed to find out the energy availability for the EV charging station (EVCS) and the requirement for grid connection. The authors use a hybrid optimisation model for an electric renewable (HOMER) simulator to determine the optimum specification of the proposed EVCS. Also, the authors carry out a techno‐economic study against the specified load requirement to evaluate the potential of the charging station. The solar irradiance data obtained from NASA ground climatology and the solar power dataset of the designated site are used to realise the precise fallouts. The optimisation results provide minimum annual system cost and reliable power to the EVs. Based on these results, an EVCS is erected in the college campus to charge plug‐in hybrid electric vehicles. The established EVCS contains 3 kW EV charger operating as a microgrid and includes nine solar panels with 335 W each, 48 V, 150 Ah specification. The authors use solar photovoltaic (PV) panels using Copper Indium Selenide‐Zinc sulfide (CISZS) quantum dots for maximising energy yield from the EVCS. The authors consider that eight different charging profiles for different months are employed for estimating the solar irradiance probability density in the designated site. From this study, the authors can conclude that Gaussian mixer model (G4) profile is optimum for the Nagapattinam region to install a potential and reliable EVCS. [ABSTRACT FROM AUTHOR]

Conventional outage management practices in distribution systems are tedious and complex due to the long time taken to locate the fault. Emerging smart technologies and various cloud services offered could be utilized and integrated into the power industry to enhance the overall process, especially in the fault monitoring and normalizing fields in distribution systems. This paper introduces smart fault monitoring and normalizing technologies in distribution systems by using one of the most popular cloud service platforms, the Microsoft Azure Internet of Things (IoT) Hub, together with some of the related services. A hardware prototype was constructed based on part of a real underground distribution system network, and the fault monitoring and normalizing techniques were integrated to form a system. Such a system with IoT integration effectively reduces the power outage experienced by customers in the healthy section of the faulted feeder from approximately 1 h to less than 5 min and is able to improve the System Average Interruption Duration Index (SAIDI) and System Average Interruption Frequency Index (SAIFI) in electric utility companies significantly. [ABSTRACT FROM AUTHOR]

This research investigates a tropical indigenous nonedible ester such as Pongamia oil (PO). PO and PO methyl ester (POME) were taken as test samples to develop a nonparametric model for investigations. This work implements the empirical cumulative distribution function (eCDF) to develop a PO and POME lifetime model. The lifetime model of the PO and POME acts as a decisive factor in determining the early failure regions corresponding to the bathtub model. On the other hand, the Bayesian theorem calculates the Bayesian inference. This enables one to foresee the variable as a posterior distribution to conclude their correlation in the parameter space. The developed lifetime model of PO and POME supports experimental results over hypothesis and assists one to predict the risk probability. The model predicted early failures of the PO at less than 40 kV and POME at less than 54 kV and life of POME from 41 to 54 kV and POME at 55–65 kV. The useful life of PO and POME will experience a failure rate of 45% and 25%, respectively. Furthermore, such detections aid power engineers to treat insulating fluids to overcome early aging effects and deterioration of oil–paper insulation. [ABSTRACT FROM AUTHOR]

Nowadays the demand for electric vehicles has been increasing due to their dropping price range and zero emission of carbon. This article initially discusses the development of various electric vehicles from the past to the present. Since the electric motor plays a significant role in EVs, various motors suitable for EVs have been identified and surveyed in this work. The battery storage system is a critical component of EVs; hence, the article goes over all the different types of batteries, from lead acid to lithium ion. Additionally, the other vehicle components such as converters required for charging the batteries, intelligent controllers, electric vehicle charging process, power management, and battery energy management concepts that are available are discussed in detail. Moreover, we bring our work to a conclusion by outlining the research opportunities that remain for the academic and industrial groups. Therefore, the proposed work intends to assist as a state-of-the-art reference for researchers in the field of various electric vehicle configurations, storage systems, converter configurations, charging techniques, control methods, and modulation methods. [ABSTRACT FROM AUTHOR]

The demand for electric vehicles continues to grow, as evidenced by global sales of electric vehicles reaching 2.2 million in 2019 and more than doubling to 6.6 million in 2021. The rapid growth of renewable energies and electric vehicles (EVs) necessitates the use of microgrids, which are a promising solution to the problem of integrating large-scale renewables and EVs into the electric power system. Besides, the essential policy support provided by the government is an increase in the availability of public charging infrastructure for EVs. This research employs a fast-charging configuration of an off-board charger with DC energy transfer. Implementation of DC energy transfer for vehicle-to-grid and grid-to-vehicle technology in a microgrid due to DC charging's unrestricted charger-rated power and rapid power transfer. However, the integration of EVs in the Microgrid system creates some operational challenges, which in this research are power quality issues such as harmonics in power systems that affect both utilities and consumers. The design models using the PI controller and the fuzzy controller based on MATLAB software are simulated to determine the control system's effectiveness. These simulations assess the control system's performance, and both approaches help improve the system's performance power quality by minimizing the system's total harmonic distortion (THD). According to the results, the fuzzy logic controller exceeded the traditional PI controller as demonstrated by minimizing the THD and also in terms of improving the waveform quality which achieved high accuracy with good performance. This research also utilized the fuzzy logic controller to control the power transfer between EVs and the microgrid, which differs from other research work, to achieve high system efficiency for the benefit of consumers. [ABSTRACT FROM AUTHOR]

Air is one of the most fundamental constituents for the sustenance of life on earth. The meteorological, traffic factors, consumption of nonrenewable energy sources, and industrial parameters are steadily increasing air pollution. These factors affect the welfare and prosperity of life on earth; therefore, the nature of air quality in our environment needs to be monitored continuously. The Air Quality Index (AQI), which indicates air quality, is influenced by several individual factors such as the accumulation of NO2, CO, O3, PM2.5, SO2, and PM10. This research paper aims to predict and forecast the AQI with Machine Learning (ML) techniques, namely linear regression and time series analysis. Primarily, Multilinear Regression (MLR) model, supervised machine learning, is developed to predict AQI. NO2, Ozone(O2), PM 2.5, and SO2 sensor output collected from Central Pollution Control Board (CPCB), Chennai region, India, fed as input features and optimized AQI calculated from sensor's output set as a target to train the regression model. The obtained model parameters are validated with new and unseen sensor output. The Key Performance Indices (KPI) like coefficient of determination, root mean square error, and mean absolute error were calculated to validate the model accuracy. The K-cross-fold validation for testing data of MLR was obtained as around 92%. Secondly, the Auto-Regressive Integrated Moving Average (ARIMA) time series model is applied to forecast the AQI. The obtained model parameters were validated with unseen data with a timestamp. The forecasted AQI value of the next 15 days lies in a 95 % confidence interval zone. The model accuracy of test data was obtained as more than 80%. [ABSTRACT FROM AUTHOR]

With the evolving trends and growing population there arise the need of assistive supporting and rehabilitation devices which are either used for improvising the strengths of the human or to offer an assistance to regain the lost motion of a particular limb joint. Particularly the musculoskeletal diseases are getting increased in the younger age due to work environment and aging and as a consequence the need of assistive devices is on the rises. The current demand is for designs that are adaptable and affordable, and for these designs, design perspectives must be considered in order to create fewer complex designs and ease production restrictions. The paper proposes a novel idea to offer rehabilitation to the lost motion on the human upper limb and develops a cost-effective upper extremity exo-skeleton to address the post stroke patients. Design analysis is carried out to obtain the important joint motions for offering rehabilitation to the user with easily available components in an affordable way. The Degrees of Freedom with respect to the human upper limb joint is kept in mind in designing the model to offer effective rehabilitation at minimal cost. The developed model is a self-sustaining, battery-operated and detachable design which can be placed in compact places. [ABSTRACT FROM AUTHOR]

Visually impaired people are often subjugated under extreme circumstances even in their day-to-day life. The daily requirements of a common man appear to be an impediment in their routine life. Simplest of tasks like walking, eating, bathing, conversing and even eating is of utmost difficulty to them. Moreover, with such difficulties their only way-out seems to be dependency on the privileged lot, which further diminishes their confidence in themselves and gradually makes them even more dependent. The conventional devices that are used by visually impaired people include basic walking sticks which fail at the job in hand by not providing adequate stabilisation on rough surfaces and misguiding the users into unfavourable conditions. There is no way for the person to know what the object in front of them is without hitting it with the stick, which could also lead to accidents. To solve these problems, a smart walking stick is developed which not only recognises the object in front of it using Machine Learning (ML) models, but also gives a voice output to alert its user about the particular object thereby limiting the chance of any and all accidents. The concept is realised in hardware and integrated to the walking stick. This helps in stabilisation of phone and to produce better results in object identification. Further an application is developed to alert the user by converting the obtained image into a voice messages. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a novel control topology for a multilevel inverter with reduced number of power switches for achieving an increased number of levels in output voltage along with improved power quality. The diode clamped multilevel inverter configuration is modified and the gating signals are generated using selective harmonic optimization technique at fundamental frequency using Satin Bower Bird optimization (SBO). It enhances the quality of output voltage waveform with the reduction in total harmonic distortion (THD), less switching power losses, and better efficiency. The inverter is investigated for its competent operation capability on grid‐tied applications. Detailed simulation is conducted and results are presented for various load conditions. To validate the inverter operation, an experimental setup of the modified diode clamped modular inverter (MDCMI) interfacing the grid is presented. The loss analysis is carried out at the inverter and converter segments. Based on the analysis, the proposed control topology demonstrates the reliable and efficient operation of the modular inverter. From the results obtained it is evident that the proposed inverter design suits superlative for the grid applications. [ABSTRACT FROM AUTHOR]

Summary This paper describes an optimal way to produce and utilize green power with better energy efficiency. The power quality issues in wind energy conversion systems (WECS) are improved by implementing a photovoltaic (PV) based modular statcom. Modular statcom consist of a modular multilevel inverter (MMI). The peculiar feature of the MMI is that it does not need any auxiliary circuit to provide the negative voltage levels. It is inherent in nature. The DC link of the modular statcom is fed from the PV source, battery, and Flywheel. The excess power obtained from PV and WECS is stored in the battery and Flywheel. Continuous power can be supplied to the load without any interruption is an added advantage of the proposed system. A landsman converter is implemented for the proposed PV system to provide regulated and constant DC supply to the modular statcom. An enhanced second order generalized integrator (ESOGI) with fuzzy logic controller (FLC) is employed to extract the source reference current signal and produce gating pulses for the modular statcom. To verify the performance of the PV‐integrated modular statcom, the simulation and experimental studies are performed under specific load conditions. Results show that the proposed system with the ESOGI method reduces the current distortions and satisfies the IEEE‐519 standards. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to examine the design and control of a symmetric multilevel inverter (MLI) using grey wolf optimization and differential evolution algorithms. Design/methodology/approach: The optimal modulation index along with the switching angles are calculated for an 11 level inverter. Harmonics are used to estimate the quality of output voltage and measuring the improvement of the power quality. Findings: The simulation is carried out in MATLAB/Simulink for 11 levels of symmetric MLI and compared with the conventional inverter design. A solar photovoltaic array-based experimental setup is considered to provide the input for symmetric MLI. Field Programmable Gate Array (FPGA) based controller is used to provide the switching pulses for the inverter switches. Originality/value: Attempted to develop a system with different optimization techniques. [ABSTRACT FROM AUTHOR]

Summary: This study aims to propose a novel effective modular multilevel inverter (MMI) with improved performance parameters. The improved performance parameters are switch reduction, harmonic reduction, and grid synchronization. The reductive design of MMI includes three bidirectional switches and eight unilateral switches to generate a symmetrical nine‐level output, while the same configuration can create a 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, and 31‐level output with asymmetric DC sources. The developed structure requires H‐bride for the generation of positive and negative levels. Reduction in switch count reduces the losses of the MMI and increases the efficiency of the MMI. The selective harmonic elimination technique gives pulses to the proposed design and for the computation of switching angles, Harris hawk's optimization (HHO) is used. Moreover, as an application point of view, the proposed photovoltaic (PV) fed MMI is connected to the grid. A closed‐loop control system is implemented for the PV fed MMI in which the gains of the PI controllers are tuned using the particle swarm optimization, firefly, and HHO‐based controller. The proposed system is simulated and experimentally validated on a laboratory prototype of PV (1 kWp) grid‐connected MMI. The performance of the proposed structure under steady‐state and dynamic conditions are investigated using simulation and experimental setup. [ABSTRACT FROM AUTHOR]

The super‐lift technique is an exceptional contribution to DC–DC conversion technology. A replacement approach of symmetrical super‐lift multilevel inverter (SLMLI) DC/AC technology is proposed with a reduced number of elements compared with the traditional multilevel inverter. In this method, the firefly algorithm conveys a leading task for the SLMLI topology for solar‐photovoltaic applications. It generates low distortion output and consumes the harmonic band of the fast Fourier transform framework by the employment of the proposed algorithm. The simulation circuit for 15 levels output uses single switch super‐lift inverter feed with different kinds of load (R, RL and RLE) conditions. The power quality is improved in SLMLI with minimised harmonics underneath the various modulation indices while varied from 0.1 up to 0.8. The circuit is designed in a field‐programmable gate array, which includes the firefly rule to help the multilevel output, to reduce the lower order harmonics and to find the best switching angle. As a result, the minimum total harmonic distortion from the simulation and hardware circuit is achieved. Due to the absence of bulky switches, inductor and filter elements expose the effectiveness of the proposed system. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a novel design of an asymmetric multilevel inverter with a very few semiconductor switches for a single‐phase grid‐connected photovoltaic (PV) system. The proposed structure consists of 14 switches and five direct current (DC) sources to produce 27 levels. The proposed structure when connected in the cascaded form generates more voltage levels. The proposed topology is superior to other multilevel inverter topologies because of its structure. It does not need any extra circuit or polarity generation circuit (H‐bridge) to come up with the negative levels. Since the proposed topology uses 5 DC sources, it can be applicable in photovoltaic farms for producing higher voltages. An efficient controller is implemented for the grid‐connected system. For obtaining high‐quality output voltage with less total harmonic distortion (THD), the pulses to the proposed inverter are obtained using selective harmonic elimination (SHE) technique. The performance of the proposed inverter is simulated and tested under steady‐state and transient conditions. A prototype of the proposed inverter is developed and investigated using a 1 − kWp solar PV plant. Simulation and experimental results prove that the proposed inverter reduces the harmonics and satisfies the IEEE519 standards. [ABSTRACT FROM AUTHOR]

This paper presents an intelligent-based fault-tolerant system for a solar photovoltaic (PV) inverter. Artificial neural network based controller is used to monitor, detect, and diagnose the faults in solar PV panels, battery, semiconductor switches, and inverters. The cascaded multilevel inverter is connected across the combination of solar PV panel and battery for dc–ac conversion. The major advantage of the proposed topology is that it can deliver power from source to the load even under fault and partial shaded conditions. The paper also reviews on various faults in the solar PV energy conversion process and provides suitable solutions for each circumstance. Simulations are undertaken in MATLAB 2016a/Simulink and experimental investigation is carried out for a 3 kWp solar PV system. The results have proven that the system is capable to deliver power in spite of faulty environments. The comparison and discussion of the results were made to show the effectiveness of the proposed system. [ABSTRACT FROM AUTHOR]

The effectiveness of evolutionary algorithms (EAs) such as differential search algorithm (DSA), Real-Coded genetic algorithm with simulated binary crossover (RGA-SBX), particle swarm optimization (PSO), and chaotic gravitational search algorithm (CGSA) on the optimal design of cross-coupled nonlinear PID controllers is compared in this paper. A cross-coupled multivariable PID controller structure for the binary distillation column was developed with two inputs and two outputs. EA simulations are run to lower IAE using two stopping criteria, namely, maximum number of functional evaluations (Fevalmax) and Fevalmax plus PID parameter and IAE tolerance. Over 20 separate trials were used to compare the performances of several EAs using statistical measures such as best, mean, standard deviation of outcomes, and average calculation time. This article presents the design of a cross-coupled nonlinear PID controller using single-objective evolutionary algorithms. Using evolutionary algorithms (EAs) with a multicrossover strategy, the results achieved by various EAs are compared to previously reported results. The results of a multivariable cross-coupled system clearly show that a single-objective nonlinear PID controller performs better. Simulations further show that all four techniques evaluated are suitable for PID controller tweaking off-line. However, only the single-objective evolutionary algorithms are acceptable for online PID tuning due to their higher consistency and shorter computation time. [ABSTRACT FROM AUTHOR]

Economic load dispatch should be given special care even when the primary responsibility of any demand response model is to provide a consistent supply to the load. Demand can be satisfied by the utility grid as well as self-sustaining user sources. If a user generates excess power after meeting demand, the user can pool it and transfer it to the grid or neighboring consumers. This is referred to as the prosumer model, in which the user serves as both a producer and a consumer. Furthermore, some of the surplus power may be stored in energy storage devices. A sophisticated mathematical model is required to estimate how much power should be generated, pooled, pulled from the grid, gathered from close users, supplied to nearby customers, and so on. This paper tries to present a smart economic load dispatch model for a demand response system that combines a multithreaded swarm model with a reward-based reinforcement system to assure optimal source selection and power flow management. To identify the optimum cost-effective power sharing model among a user, the grid, and neighboring users, the system uses particle swarm optimization (PSO) and artificial bee colony (ABC) optimization. Both models have benefits and drawbacks, and not all models work well with all data input. Using two models at the same time consumes a significant amount of time and computational power. As a consequence, for each data input, an upper bound confidante (UBC) model is used in parallel to select the best economical swarm model based on a semisupervised reinforcement model. A weighted Boruvka's algorithm based on transmission line cost and transmission loss is being used to construct an optimum economic power sharing model, which is backed by swarm models. The efficiency of each model is evaluated using the same data for both models, and error analysis is performed. It was discovered that each model performs differently for various data, and creating a reinforced multithreaded model helps to increase accuracy, reduce computing time, and improve efficiency. [ABSTRACT FROM AUTHOR]

Fly ash generated during boiler combustion are carried away with hot flue gas and deposited over the heat exchanger surfaces. This study was motivated by the decline in the thermal efficiency of the heat exchangers in thermal power plants due to ash fouling. The paper discusses a new computational procedure that has been attempted to identify the best statistical model for predicting heat exchanger fouling and also to choose the most strongly linked covariates. In this context, three different statistical models including multiple linear regression, sliced inverse regression, and random forests regression are used to estimate the cleanliness factor of the heat exchanger. The variables and model are chosen solely on the basis of analytical procedures. Heat exchanger metal tube temperature that highly influences the cleanliness factor is selected using a measure of importance based on random perturbations. Metal temperature above a threshold is selected as a strongly linked covariate. The R package modevarsel is used here to perform the statistical analysis. The value of root mean square error for the cleanliness factor from sliced inverse regression and random forest is 0.0002, indicating that as more feasible in predicting ash fouling. [ABSTRACT FROM AUTHOR]

The proposed research work is focused on forecasting the future requirements of water supply based on the current requirement of water and also identifying the possibility of occurrences of cracks and leaks using the ARIMA (autoregressive integrated moving average) model. The experiments were conducted using real-time experimental hardware. The pressure data obtained and their p -value is less than 0.05, which represents the stability of the data in the ARIMA model. The forecasted pressure data range between 0.451379 N/m2 and 2.022273 N/m2. The frequency of the forecasted pressure ranges between 1.706869 N/m2 and 3.065836 N/m2 (maximum peak) and −0.81046 N/m2 and 1.042164 N/m2 (minimum peak). Forecasted data of pressure at damaged condition lie between 2.880788 N/m2 and 3.29797 N/m2 and frequency ranges between 4.866227 N/m2 and 5.664348 N/m2. Similarly, future forecasted data of water requirement for the next 1 year range between 614.6292 (liters/week) and 620.0099 (liters/week), the frequency of the forecast value with maximum ranging from 617.0086 (liters/week) to 628.5465 (liters/week), and the minimum peaks ranging from 611.0967 (liters/week) to 612.2914 (liters/week). The above data are for a single water distribution pipeline. [ABSTRACT FROM AUTHOR]