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Abstract Multi‐input converters play an important role in integrating the independent energy sources utilized in the grid‐connected system and electric vehicle (EV) applications. In this scenario, several types of multi‐input converters are presented in the literature. Most of the MICs are operated using a time‐sharing scheme. This leads to a restricted duty cycle which limits the energy sources utilization and output voltage. To overcome the aforementioned limitations, a multi‐input single‐output (MISO) boost converter is proposed. It can improve the utilization of energy sources and output voltage with a reduction in the part count. To verify the feasibility of the proposed scheme, a 200 W prototype circuit is developed; simulation and experimental results are validated.

An active magnetic bearing (AMB) is an evolving research topic in the bearing industry. Compared to the conventional approach, AMB is more advantageous and has different industrial applications. The design of this bearing needs to be improvised to get more optimized performance. This leads us to focus on the better-designed structure of the actuator, which plays an essential role in AMB performance. Thus in this manuscript, we have designed and compared the I-type and U-type actuators’ performances in terms of their resistance, inductance profile, and force profile to show the characteristics of these actuators. These designed actuators are simulated in Ansys Maxwell software, and compared with the simulation results. Further, these results are verified using hardware results, and the tested actuators are used to design two different AMB systems. One current controller is designed and simulated to achieve the stable levitated position of the rotor. This rotor is rotated by inserting an AC magnet in the system and finally, a rotational system is attained, and its speed is observed by the tachometer for both AMB systems. Both the proposed AMB systems are simple and compact in structure as these are having single coil and single power amplifier. These axial AMB systems are used in many high-speed applications as they are having features like less installation space, less cost and more room for heat dissipation.

This paper suggests an alternate level enhancing algorithm to operate the nine-level cascaded multilevel inverter (CHBMLI) for a 31-level output, which involves an inventive selection of optimal asymmetrical voltage values in separate DC sources (SDCs) and a quarter-wave symmetry pulse pattern pulse width modulation (QSP-PWM). The symmetrical CHBMLI structure involves the same DCs with an objectionable value of component count, especially at higher level outputs; consequently, there is a need to reduce the component count. It can be supported largely by the optimal set of asymmetrical voltage values (say 1:2:4:8), such an actualization is quite easy with photovoltaic (PV) applications. Additionally, many of the commonly used PWM methods of MLI are tedious especially when it comes to the generation of pulses. This paper also engraves a straightforward switching strategy apposite to the developed MLI topology. In addition, three new switching angle modulation schemes have been proposed to get the optimum performance, namely, incremental delay switching angle modulation scheme, random delay switching angle modulation scheme and sine referencing-based switching angle modulation scheme. The preliminary study on the tenet of suggested topology is done theoretically, then the performance is analyzed in MATLAB2019a software and finally, experimentally corroborated on a proof-of-concept (POC) prototype with the aid of ModelSim6.3f hardware description language simulation environment, Xilinx 14.5 project navigator tool and XC3S500E field-programmable-gate-array processor. The tested results for the inductive load demonstrate the output voltage synthesizing and the total harmonic distortion curtailment savvies of the proposed MLI and the switching strategies.

Due to the intermittent and hazardous nature of lightning impulse, researchers rely on artificial impulse generators to study it. The Tesla coil is a commonly used device to generate artificial lightning. This paper presents a parametric analysis of the primary side coil of the Tesla transformer. The analysis employs seven key electrical properties: skin depth, current density, voltage distribution, Ohmic loss, electric field strength, charge density, and energy content. These parameters are used to evaluate the response of various coil types to high voltage impulses. The Ansys high-frequency electromagnetic solver is used to perform a comparative analysis of essential parameters. The results can aid lightning researchers and Tesla coil designers in determining the optimal designs for Tesla primary coils in the future. The analyzed data is validated using Tesla coil hardware, which is used as an artificial lightning producer to test the feasibility of a lightning energy harvester.

Smart control tactics, wider stability region, rapid reaction time, and high-speed performance are essential requirements for any controller to provide a smooth, vibrationless, and efficient performance of an in-house fabricated active magnetic bearing (AMB) system. In this manuscript, three pre-eminent population-based metaheuristic optimization techniques: Genetic algorithm (GA), Particle swarm optimization (PSO), and Cuckoo search algorithm (CSA) are implemented one by one, to calculate optimized gain parameters of PID controller for the proposed closed-loop active magnetic bearing (AMB) system. Performance indices or, objective functions on which these optimization techniques are executed are integral absolute error (IAE), integral square error (ISE), integral time multiplied absolute error (ITAE), and integral time multiplied square error (ITSE). The significance of an optimization technique and objective function can obtain only by implementing it. As a result, several comparisons are made based on statistical performance, time domain, frequency response behavior, and algorithm execution time. Finally, the applicability of optimization strategies in addition to the performance indices is determined with the aid of the comparative analysis. That could assist in choosing a suitable optimization technique along with a performance index for a high-speed application of an active magnetic bearing system.

Optimal planning for the energy storage elements’ status of charging and discharging, besides managing the buying and selling energy from the grid, is the first step for enhancing energy usage and saving costs for customers. Therefore, in this work, an enhanced multi-objective optimization algorithm of the Marine Predators Algorithm (MOEMPA) is proposed to handle three objective functions for minimizing the operating cost and emission with maximizing the renewable factor for optimal usage of the energy resources. The proposed MOEMPA is applied for managing the sharing energy in an interconnected microgrid with utility grid. The considered microgrid consists of solar and wind renewable energy sources, diesel for emergence loads, and set of batteries for storage extra energy. The described system used for feeding the required power under three different cases for the weather and the grid continuity/discontinuity along 96 h horizon in India, Delhi. The proposed MOEMPA results are compared with recent multi-objective optimization algorithms including, basic variant of multi-objective versions of marine predators algorithm (MOMPA), grasshopper optimization algorithm (MOGOA), slime mould algorithm (MOSMA), grey wolf optimizer(MOGWO), antlion optimizer (MOALO), and multi-verse optimizer (MOMVO) to assess the performance of the proposed scheduled system based MOEMPA. Furthermore, the baseline system is implemented to provide a comprehensive evaluation for the proposed approach based on MOEMPA. The comparisons and analyses reveal the efficacy and excellence of the proposed approach in minimizing the cost and emission with enhancing the profit for the customers via providing an the optimal managing between staging, discharging, buying, and selling decisions. The analysis affirms that, the scheduled system based on MOEMPA has the highest cost-saving of 16.0%, 34.5%, and 26.9% compared with the baseline schedule and other peers over the studied scenarios of operation. Moreover, the schedules based on MOEMPA reduced the emissions by 13.37 % and 14.7%. For the renewable factor, the scheduled system based on MOEMPA enhanced its values to 69.3% and 36.6%; meanwhile, the baseline system has 73.4% and 25.9%, hence the proposed approach guarantee feeding the load with high profit.

The location of shaded or faulty Photovoltaic modules in the PV array has a negative impact on the harvested power from the entire array. To overcome this significant limitation, PV reconfiguration is a considerable technique developed via interchanging the PV modules’ location physically or electrically. By this inspiration, in this article, the authors propose a novel enhanced heterogeneous hunger games search optimizer (EHHGS) based PV reconfiguration. The innovated EHHGS introduces a modified variant for the basic hunger game search optimizer (HGS) to achieve a high diversity and robust exploitation of the optimal solutions. The EHHGS is applied to identify the optimal relocation for the shaded or faulty modules in two configurations of PV connected array: total-cross-tied array (TCT) and Series–parallel one (S–P). The proposed approach has applied symmetric and asymmetric connected PV arrays with dimensions of 9 × 9 and 10 × 8 throughout five different shade patterns. Moreover, for providing a flexible tool for the user/researcher to detect and observe the benefits achieved via the PV reconfiguration strategy, a simple graphical user interface (GUI) for the PV reconfiguration strategy of TCT or S–P PV connected array using meta-heuristic algorithms is designed. This implemented GUI can extend for any size of PV arrays, different optimization algorithms, and different connection schemes. The proposed EHHGS, HGS, and set of recent optimizers, including harris hawk optimizer (HHO), marine predators algorithm (MPA), and artificial ecosystem-based optimization (AEO), handle a new simplified objective function to boost the optimizer’s ability in catching the optimal modules’ location to alleviate the mismatched power in the studied arrays. Several statistical metrics are computed for providing an unbiased comparison. Through the comparisons, the proposed EHHGS exhibits superior performance. It achieves the best re-design for the considered arrays that helps in avoiding the mismatch losses in the cases of the partial shaded/faulty modules and enhances the power generated profiles. EHHGS enhances the power by percentages of 44.42%, 11.9%, 33.36%, 20. 86% and 13.17% compared to the TCT-connected system. In the case of the S–P connection, the proposed EHHGS generates 47.2% and 10.45%, 30.75%, 17.25%, and 26.27% higher power.

The power output of solar photovoltaic systems can be affected by environmental factors, such as partial shading. This can lead to a decrease in the power conversion rate of the system. Although existing solutions for this issue are cost-effective and efficient, new solutions could further improve the system's performance by increasing consistency, power generation, and reducing mismatch loss and costs. To address this, a new method for configuring PV arrays was proposed using the calcudoku puzzle pattern. The performance of this new array configuration was evaluated in MATLAB/Simulink® for a 9 × 9 PV array and compared to conventional methods like Series-parallel, Total Cross Tied (TCT), and Sudoku array configurations. The performance was evaluated under eight different shading patterns based on power conversion rate and mismatch losses between the PV rows. The proposed array configuration resulted in 3.9%–13.3% of mismatch losses across the different shading patterns, while other configurations had a minimum of 13.8% to a maximum of 51.9% of mismatch losses. This reduction in mismatch losses directly improved the power conversion rate of the PV array.

The research presented in this article aims at the modelling and optimization of hydrogen-fuel-cell-based distributed generation (HFC-DG) to minimize the effect of electric vehicle charging stations (EVCSs) in a radial distribution system (RDS). The key objective of this work is to address various challenges that arise from the integration of EVCSs, including increased power demand, voltage fluctuations, and voltage stability. To accomplish this objective, the study utilizes a novel spotted hyena optimizer algorithm (SHOA) to simultaneously optimize the placement of HFC-DG units and EVCSs. The main goal is to mitigate real power loss resulting from the additional power demand of EVCSs in the IEEE 33-bus RDS. Furthermore, the research also investigates the influence of HFC-DG and EVCSs on the reliability of the power system. Reliability is crucial for all stakeholders, particularly electricity consumers. Therefore, the study thoroughly examines how the integration of HFC-DG and EVCSs influences system reliability. The optimized solutions obtained from the SHOA and other algorithms are carefully analyzed to assess their effectiveness in minimizing power loss and improving reliability indices. Comparative analysis is conducted with varying load factors to estimate the performance of the presented optimization approach. The results prove the benefits of the optimization methodology in terms of reducing power loss and improvising the reliability of the RDS. By utilizing HFC-DG and EVCSs, optimized through the SHOA and other algorithms, the research contributes to mitigating power loss caused by EVCS power demand and improving overall system reliability. Overall, this research addresses the challenges associated with integrating EVCSs into distribution systems and proposes a novel optimization approach using HFC-DG. The findings highlight the potential benefits of this approach in terms of minimizing power loss, enhancing reliability, and optimizing distribution system operations in the context of increasing EV adoption.

Partial shading (PS) phenomena significantly degrade the power output from the photovoltaic (PV) array. Due to the PS effect, there is a mismatch in current between the PV modules of a series-connected PV string. As a result, in-shaded module power dissipates, resulting in increased module temperature and the creation of local hotspots. To avoid the hotspot effect, bypass diodes are connected anti-parallel to the PV modules. But the activation of bypass diodes creates multiple peaks in I-V and P-V characteristics. The effect of PS can be reduced by using a suitable array configuration and the performance of the PV array gets improved. Among all the existing PV array configurations, the total cross-tied (TCT) configuration performs better under partial shading conditions (PSCs). But the disadvantage of TCT is that it has an enormous number of tie connections, creating complexity in an array configuration and, therefore, more cable losses. The main objective of this paper is to propose a novel array configuration named benzene (BZ) configuration, which eliminates the demerits of the TCT array configuration. Benzene configuration has improved performance with fewer tie connections compared to conventional tie-connected configurations such as TCT, bridge link (BL), and honeycomb (HC). The performance of the tie-connected array configurations is analyzed under different PSCs by considering the various performance parameters such as global maximum power (GMP), the voltage at maximum power (Vmp), current at maximum power (Imp), mismatch loss (ML), shading loss (SL), fill factor (FF) and performance ratio. It is observed that the proposed benzene array configuration gives better output performance, even having lower mismatch losses with a reduced amount of tie connections. Further, the effect of the bypass diode on a partially shaded PV string is investigated through MATLAB Simulation and experimental analysis.

The performance of the solar photovoltaic system is affected by the unpredictable phenomenon of partial shading. This causes the mismatch losses that suppress the power generation of healthy PV modules in it. The objective of the proposed work in this paper is to bring out the maximum power from each PV module in the PV array by reducing the mismatch losses. A new array configuration method is proposed in this paper, which follows the screw pattern in the row formation. Each PV row is created with distinct PV modules from the rows of the conventional array configuration. This proposed work allows the PV system to operate with minimum mismatch losses by even shade dispersion over the PV array. The proper mathematical expression with all necessary constraints was derived for the array formation of the proposed work. The output analysis is been validated in the simulation of the $9\times 9$ PV array in MATLAB/Simulink Ⓡ. The mismatch loss generation and output power enhancement are measured and compared with the various conventional array configuration methods under six kinds of partial shading patterns. The proposed array configuration is 40% more efficient than the conventional series-parallel array configuration and also performs better than the total cross-tied and sudoku puzzle pattern methods. The shade dispersion rate of the proposed array configuration has highly reduced the mismatch losses in the PV system and hence, it improves the power output.

Power conversion efficiency is the most important factor to be considered in PV systems because it is affected by various environmental conditions. The effect of partial shading is the most influenced factor in the reduction of power output. Various research schemes like Maximum Power Point Tracking (MPPT), array configuration scheme, reconfiguration, etc., work on the PV system to reduce the impact of partial shading. This paper presents a new kind of array configuration scheme that forms the PV array based on the moves of the Knight coin in the chess game. This arrangement creates the squared PV array of rows with distinct PV modules which is capable of evenly dispersing the shading in the partially shaded PV array. Also, this scheme is applicable for the non-squared PV arrays to create PV rows with the PV modules from a distinct location or from the same row with optimized distance to disperse the maximum level of shading. The proposed method has been discussed with the proper mathematical formulation with all necessary constraints and also it been validated with the hardware arrangements and MATLAB/Simulink® model.

Multiport converters play a significant role in portable electronic and electric vehicle (EV) applications. In literature, different configurations of single-input multi-output (SIMO) converters are presented. Most of the SIMO converters generate the outputs with operating constraints on the duty ratio and charging of inductors. The cross-regulation problem is still a challenge in SIMO converters design. A SIMO topology is proposed in this study to overcome the limitations mentioned earlier. It can generate three different output voltages without constraint on the duty cycle and inductor currents (like $i_{L1} > i_{L2} > i_{L3} $ or $i_{L1} < i_{L2} < i_{L3}$ ). Cross regulation problems do not exist in the proposed topology, so the load voltage $V_{01}$ ( $V_{02}$ ) ( $V_{03}$ ) is not affected by the variation of output current $i_{03}$ ( $i_{02}$ ) ( $i_{01}$ ). The loads are isolated from each other during control. In the laboratory, a 200 W prototype circuit is developed; simulation and experimental results are validated.

This paper proposes a closed-loop and implements some metaheuristic optimization approaches to regulate an unstable active magnetic bearing (AMB) system. First of all, a hardware model of an AMB is fabricated in the laboratory. Mathematical analysis is carried out and a linearized open-loop transfer function is obtained for an equilibrium point of operation, using the parameters of the hardware model. Then, a closed loop is proposed for this AMB system, which comprises a PID controller, power amplifier, and position sensor. Further, three different metaheuristic nature-inspired hunting-based optimization algorithms i.e., Ant lion optimization (ALO), Grey wolf optimization (GWO), and Whale optimization algorithm (WOA) are implemented individually to calculate the gain parameters of the PID controller. Separately, the performance of these optimization algorithms is evaluated and observed on four different performance indices: integral of absolute error (IAE), integral of squared error (ISE), and an integral of time multiplied absolute error (ITAE) and integral of time multiplied squared error (ITSE). For a stable, efficient, and reliable bearing operation, it is vital to perform an analysis of the performance of optimization techniques with different objective functions for the proposed system. Therefore, few comparisons are conducted, first based on data obtained from statistical analysis. The second is based on data obtained from transient state performance and phase margin. Third on the scale of algorithm execution time. Finally, with the assistance of observed data effectiveness of each optimization technique to control the proposed AMB system is concluded which can serve as theoretical and experimental foundations for the continued use of AMB in high-speed applications.

Partial shading is the most unexpected scenario encountered by the arrays that degrade the performance causing power reduction, non-convex characteristics curves, losses, hotspot, module damage, and system failure. The adoption of various reconfiguration techniques has recently provided relief to the PV array to reduce power losses during partial shading. However, these techniques exhibit vulnerabilities such as reliable operation, ease of implementation and, higher cost and complexity due to the requirement of abundant manpower, labour, complex algorithms, dynamic operation, switches and sensors that cause additional power losses. Hence, this paper presents a low cost and less complex reconfiguration technique for PV arrays to effectively increase the power generation during partial shading scenarios. The proposed reconfiguration does not require any manpower, labour, algorithms or additional devices to reduce the losses in arrays during shading. The efficacy of the technique is tested using two array sizes under various shading scenarios using MATLAB modelling and real-time field experiments. Also, for better analysis, the performance of the proposed technique is compared with conventional configurations and Sudoku reconfiguration. The investigation proclaimed that the proposed reconfiguration technique has an average power enhancement of 20% higher than any other conventional configurations.

The growing Indian economy calls for the urgent need to review the ways and means to conserve energy consumed by the industrial sector. As a part of fulfilling the objective, the Government of India has made it mandatory to conduct periodic energy audits in the bulk industrial sector to be followed up with a practical implementation of energy conservation (ECON) measures as suggested by the energy audit team. This paper presents the energy audit conducted in a medium-size textile mill in western Tamil Nadu of India. The energy audit has been conducted, and an exhaustive study of the energy consumption pattern of the mill has been carried out using tools like power quality analyzer, thermal leak detectors, air leakage detectors, and luminance meter. The qualitative data measured has been studied, and suitable site-specific and problem-specific methods, suggestions, and recommendations have been examined to yield substantial energy savings. The implementation of energy efficiency technologies and energy saving (ES) methods like effective use of the existing machinery, installing application-specific machinery, reduction/ avoiding energy wastage was also carried out. The conducted energy audit on the chosen textile leads to the reduction of energy consumption by almost 21 % per day. It saved company utility energy by nearly 13.59 %. This paper examines the recommendations and the subsequent energy efficiency implementations about specifically (i) energy demand management, (ii) equipment healthiness, and (iii) identification of energy wasted. The energy-saving outcome is elaborated with cost-effective planning and execution.

The intelligent optimization techniques have been introduced by carefully observing the behavior of various hunters like a whale, grey wolf, Aquila, and lizards for estimating global optimum solutions in fair time by forming appropriate mathematical models. However, hunting-based algorithms suffer from slow and pre-requisite convergence and get caught up in local minimum. Aquila optimizer (AO) is one of the recently developed hunting-based methods that encounter a similar type of shortcoming in a few situations. This research introduces the concept of chaotic mapping to the standard AO in order to increase the convergence speed. Also to maintain the balance of exploration performed by AO with its exploitation capability, a single stage evolutionary algorithm is also integrated with it. The performance of standard AO and modified AO are tested for well-defined unimodal and multimodal Benchmark functions. The proposed framework produces one population by standard AO and a new population by single stage genetic algorithm based evolutionary concept in which binary tournament selection, roulette wheel selection, shuffle crossing over and displacement mutation occur to generate a new population. The chaotic mapping criteria are then applied to obtain various variants of the standard AO technique. The general results obtained from the proposed novel chaotic mapping-based advanced AO with single stage evolutionary algorithm shows that it outperforms the standard AO. This advanced technique is thus applied to real-world design engineering problems to study its significance from an industrial point of view.

Modeling of solar photovoltaic (PV) cell/modules to estimate its parameters with the measured current–voltage (I–V ) values is a very important issue for the control, optimization, and effectiveness of the PV systems. Therefore, in this research work, a robust approach based on Stochastic Fractal Search (SFS) optimization algorithm is introduced to estimate accurate and reliable values of solar PV parameters for its precise modeling. To assess the excellence of the proposed SFS algorithm, different solar PV equivalent circuit models, i.e. single-diode model (SDM), double-diode model (DDM), and PV module model are taken into consideration. The introduced algorithm is examined under three different case studies; (i) first case study: an experimental standard dataset of a commercial R.T.C. France silicon solar cell working at 33°C, and solar radiance of 1000 W/m2; (ii) second case study: using a polycrystalline solar panel STP6 120/36 with 36 cells in series working at 22°C, and (iii) third case study: an experimental dataset of ESP-160 PPW PV module working at 45°C, this experimentation were carried out in the Laboratory of Renewable Energy at Assiut University, Egypt. The results obtained using the proposed method are compared with other recently published works, and hence, the achieved results show the superiority, perfectness, and effective modeling concerning various performance parameters. Thereby, the proposed SFS approach can be used for effective PV modeling to improve the efficiency of the PV system.

There has been a global expansion of solar electricity during the past decades. Solar photovoltaic (PV) electricity generation is not only simple but also environmentally benign and cost-effective. This is the most affordable and suitable power source for rural areas. The PV power output is unavoidably suppressed when there is partial shade. There were numerous maximum power point tracking (MPPT), arrays, and reconfiguration approaches that were available earlier. The authors have previously described an L-shaped propagating array arrangement. The L-shaped configuration creates the PV rows based on the chess game coin. The knight coin of this game will move in the shape of "L," which inspired new array configurations. This work discusses the hybridization of the L-shaped configuration with the conventional array configurations. The proposed hybrid L-shaped configuration has been developed in MATLAB/Simulink, where the conventional configuration and the hybrid configurations are validated under different test conditions. The test conditions are created based on the all possible real-word shading patterns. The power generation, mismatch losses, and characteristic curves were attained for each method, and this work presents all these validations with the inferences. In addition, the justification for the suitable array configuration with. [ABSTRACT FROM AUTHOR]