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Increasing energy efficiency in manufacturing has significant environmental and cost benefits. Turning on or off a machine dynamically while considering the production rate requirements can offer substantial energy savings. In this work, we examine the optimal policies to control production and turn on and off a machine that operates in working, idle, off, and warmup modes for the case where demand inter-arrival, production, and warmup times have phase-type distributions. The optimal control problem that minimises the expected costs associated with the energy usage in different energy modes and the inventory and backlog costs is solved using a linear program associated with the underlying Markov Decision Process. We also present a matrix-geometric method to evaluate the steady-state performance of the system under a given threshold control policy. We show that when the inter-arrival time distribution is not exponential, the optimal control policy depends on both the current phase of the inter-arrival time and inventory position. The phase-dependent policy implemented by estimating the current phase based on the time elapsed since the last arrival yields a buffer- and time-based policy to control the energy mode and production. We show that policies that only use the inventory position information can be effective if the control parameters are chosen appropriately. However, the control policies that use both the inventory and time information further improve the performance. [ABSTRACT FROM AUTHOR]

Recently renewable energy such as a photovoltaic (PV) system has been utilized more and more since it is pollution-free and permanent. To maintain the PV system functioning at, or near, the peak power point of the PV panel under different conditions such as fluctuating solar irradiation, temperature, and other factors, maximum power point tracking algorithms are required. In this study, a novel hybrid robust intelligent controller for a photovoltaic system is proposed. Three loops are used for creating the proposed controller, ensuring the controller's robustness. The first loop's objective is to locate the photovoltaic system's highest power spots. In the second loop, a novel fractional-order sliding model observer based on deep reinforcement learning optimization approach is proposed as a result of the design of a reliable controller under the lumped uncertainty in the system. Designing a novel back-stepping fast non-singular terminal fractional-order sliding mode controller is achieved at the final step. This method offers a nice transition response, a small tracking error, with a quick response to changes in solar radiation. Numerical analysis shows that the performance of the photovoltaic system by the proposed controller has been able to increase the efficiency of the system significantly. [ABSTRACT FROM AUTHOR]

Hydrogen has become an essential element in the pursuit of sustainable and clean energy solutions. Especially with the fast-paced advancement in demand, supply, and policy environment, its impact on hybrid renewable energy (HRE) management is becoming increasingly relevant. Efficient energy consumption, cost reduction, and enhanced user comfort are now critical factors in energy optimization. The production of green hydrogen, which is generated through water electrolysis using renewable energy sources (RES), has shown great potential as a sustainable energy solution. It offers several advantages, such as zero greenhouse gas emissions, high energy density, and versatile applications. This paper presents a detailed study on the power management and control of a hybrid renewable system (HRES) equipped with a diesel generator (DG) as a backup power source. The main objectives of the hybrid system are to satisfy the load power demand, ensure the most efficient use of the HRES, and keep the battery bank charged to prevent blackouts and extend the battery's life. To guarantee the system's reliability, the DG should be sized to meet the peak load demand when the RES generates less electricity than the load demand. This study explores the feasibility of modified versions of the load following and cycle charging control strategies to overcome the limitations of managing generation and storage systems' operations in different operating modes and to enhance the performance of an HRES with a DG that supplies electricity to a small and remote location. The proposed method not only maximizes the use of RES production but also enables multi-energy source management under different power generation and load demand scenarios. The study's outcomes demonstrate the feasibility of this proposed power dispatch strategy in a remote location environment. The paper includes a detailed discussion of overall control, mathematical models, energy storage in the battery model, and energy dispatching based on load following. To design and simulate the hybrid model system, MATLAB-SIMULINK is used, and the results are analyzed to identify the appropriate operation requirements, component selection, and energy management of the hybrid renewable energy system. [ABSTRACT FROM AUTHOR]

Summary: This work focuses on a novel single‐phase complex band‐pass filter‐based frequency‐locked loop (1ϕ‐CBF‐FLL) control for a grid‐tied solar photovoltaic array (SPVA)‐based energy generating system. Among the novel characteristics of this 1ϕ‐CBF‐FLL control algorithm are as follows: (1) It offers a quick convergence rate; (2) it has the ability to mitigate harmonics and DC offset; and (3) it has simplicity in execution. A voltage source converter (VSC), boost converter, 1‐ϕ grid, ripple filter, and nonlinear loads are all part of the system. Feeding 1‐ϕ nonlinear loads and the grid with active power from the solar array is managed by the VSC. Furthermore, grid current quality is enhanced by the use of VSC. Between the solar array and DC‐link capacitor, there is a boost converter that uses the perturb and observe (P&O) technique to track the maximum power from the SPVA. The primary aim of the proposed system is to remove power quality (PQ) issues brought on by nonlinear and imbalanced loads and to supply sinusoidal grid currents during solar irradiance fluctuations. Additionally, a comparison between 1ϕ‐CBF‐FLL control and other control algorithms is presented. The effectiveness of this 1ϕ‐CBF‐FLL control is examined under varying nonlinear loads and in various environmental settings. Test results are used to confirm the simulated results of the proposed system. According to the IEEE‐519 standard, the recommended control may achieve a source current of total harmonic distortion (THD) of 4.02%, which is lower than 5%, a source voltage of THD of 3.21%, and a load current of THD of 24.54%. [ABSTRACT FROM AUTHOR]

The advancement of renewable energy technology has been significantly aided by solar photovoltaics (PV). Since solar PV is a weather‐dependent source, it cannot be dispatched. To ensure that the solar PV system can harvest the maximum amount of electricity for the available irradiance level, maximum power point tracking (MPPT) algorithms are used. For standalone low‐voltage DC (LVDC) microgrids to utilize the energy storage system as efficiently as possible, maximum power extraction is essential. The sensed PV voltage and current are essential for these MPPT algorithms to ensure that the maximum power point of the panel is captured. This work proposes an effective fault‐tolerant control (FTC) scheme for the solar PV subsystem in the LVDC microgrid that can seamlessly extract the maximum power despite the PV voltage sensor being faulty. The proposed FTC scheme uses a sliding mode observer (SMO)‐based method to detect and isolate PV voltage sensor faults in the standalone LVDC microgrid. The efficacy of the proposed FTC is assessed in a range of circumstances involving load disturbance, irradiance change, and various sensor fault scenarios. The performance of the proposed FTC is validated using experimental analysis on the LVDC microgrid testbed and MATLAB simulations. Given a faulty PV voltage sensor, at a given operating condition of the microgrid, the proposed FTC scheme is successful in reducing the additional power burden on the battery storage by at least two times. Consequently, the additional discharge in terms of SoC is also seen to be decreased by at least 9%. The proposed FTC technique outperforms the popular MPPT approaches for solar PV in terms of PV voltage sensor fault tolerance in the microgrid. [ABSTRACT FROM AUTHOR]

With the fast-growing demand for wind energy, small wind turbines are becoming a viable and cost-effective source of clean and renewable electricity for residential, businesses, small industries, and remote applications. To harness the maximum power, wind turbines (WT) should be operated at full capacity; hence, it is crucial to design an effective maximum power point tracking (MPPT) control scheme. This paper presents an effective MPPT control algorithm based on fuzzy logic control (FLC) which tracks the slope of the mechanical power of the WT as a function of the rotational speed. The output of this MPPT generates the duty cycle for the DC–DC converter to allow the WT to extract the maximum power under a wide range of wind speed and loading conditions. The electrical generator used in the studied wind energy conversion system is the permanent magnet synchronous generator (PMSG). The proposed FLC-MPPT strategy is simple to implement and does not require any prior knowledge of the WT parameters. The proposed FLC-MPPT technique for WT is simulated using MATLAB/Simulink and validated experimentally on a laboratory setup using a dSPACE1104 board. In addition, a comparison between the proposed FLC-MPPT and other methods under significant wind speed fluctuations and variable load conditions is presented to demonstrate its effectiveness. The results show that the proposed MPPT has a fast convergence with a high tracking accuracy and exhibits low steady-state oscillations. [ABSTRACT FROM AUTHOR]

Introduction. Recently, photovoltaic (PV) systems are increasingly favored for converting solar energy into electricity. PV power systems have successfully evolved from small, standalone installations to large-scale, grid-connected systems. When the nonlinear loads are connected to a grid-tied PV system, the power quality can deteriorate due to the active power supplied by the PV array, there’s a noticeable decline in the quality of power delivered to consumers. Its combination with the shunt active power filter (SAPF) enhances system efficiency. Consequently, this integrated system is adept at not only powering local loads but also at compensating for reactive power and filtering out harmonic currents from the main grid. The novelty of the work describes how an operation of a small scale PV system connected to the low voltage distribution system, and nonlinear load can be achieved, the investigation aims to analyze the system’s behavior and elucidate the advantages of employing various control algorithms. These proposed algorithms are designed to ensure a unity power factor for the utility grid while prioritizing high convergence speed and robustness against load power fluctuations across different levels of solar irradiation affecting the PV modules. The purpose of this work is to enhance the dynamic performance of the SAPF by cooperatively using a self-tuning filter (STF) based instantaneous active and reactive power method (PQ) with a novel predictive current control, enhance the system resilience, ensure optimal management of the total active power between the PV system, the electrical network and the non-linear load by integrating the functionalities of the SAPF under different levels of solar irradiation and maintain the DC-link capacitor voltage constant. Methods. A novel predictive current controller is designed to generate the switching signals piloted the three phase source voltage inverter, also a novel algorithm of instantaneous active and reactive power is developed, based on STF, to extract accurately the harmonic reference under non ideal grid voltage, also the perturb and observe algorithm is used to extract, under step change of solar irradiation, the maximum power point tracking of the PV module and the PI controller is used to maintain constant the DC-link capacitor voltage of the SAPF. Results. The efficacy of the proposed system is primarily centered on the grid side, and the performance evaluation of the control system is conducted using the STF based PQ algorithm and predictive current control. In addition, comprehensive testing encompasses all modes of operation, including scenarios involving distorted voltage sources, step changes in solar radiation, and variations in nonlinear loads. Results highlight superior performance in both transient and stable states, affirming the robustness and effectiveness of the proposed controllers. Practical value. The total harmonic distortion value of the grid current for all tests respects the IEEE Standard 519-1992. [ABSTRACT FROM AUTHOR]

Optimization algorithms are essential tools used to address real-world problems through minimization or maximization processes. In the context of photovoltaic (PV) systems, various optimization algorithms have been employed to extract solar cell parameters using minimization techniques, and to identify the maximum power point (MPP) using maximization approaches. However, under partial shading conditions or during rapidly changing irradiance, many of these algorithms tend to get trapped in local minima, failing to locate the global maximum power point (GMPPT). This shortcoming leads to significant energy losses from PV cells. Similarly, the extraction of solar cell parameters is often compromised by the random search behavior and inadequate exploration and exploitation capabilities of many existing algorithms. The Jellyfish Search Optimization (JSO) algorithm is a recent, parameter-free method developed to tackle a wide range of optimization problems. Despite its innovative design, JSO is prone to premature convergence and exhibits a longer convergence time. To address these limitations, this paper proposes a modified version of the JSO algorithm, which integrates the state-of-the-art features of JSO with the Lévy flight characteristic from the cuckoo search algorithm. This combination aims to achieve a better balance between exploration and exploitation. To validate the effectiveness of the proposed Improved Jellyfish Search Optimization (IJSO) algorithm in PV systems, extensive experiments were conducted. These experiments included benchmarking with complex test functions, extracting cell parameters using various solar cell models, and maximizing energy output from PV systems under different environmental conditions. The results demonstrate that the proposed IJSO algorithm effectively enhances parameter extraction and global maximum power point tracking, making it a promising solution for optimizing energy extraction from PV cells in dynamic conditions. [ABSTRACT FROM AUTHOR]

The high inductance current ripple and the PV voltage fluctuations limitation at the DC (direct current) -DC link have been the unsolved problems in the photovoltaic systems tied in grid. A control strategy with a current hysteresis loop is proposed to address the issues of high inductance current ripple in photovoltaic systems which can achieve real-time duty cycle regulation. Differing from the conventional mode that uses one switch in the buck-boost DC-DC link, two switches have been designed here to separate the buck and boost modes for the coordinated control, which can achieve a wide PV voltage fluctuations range. Based on the conventional fixed-duty cycle determination method, a real-time duty cycle determination method is proposed by introducing changes in inductance current. In order to improve power conversion efficiency, the incremental conductance method is improved by introducing the steepest gradient descent to quickly achieve the maximum power point tracking. This study experimentally verifies the proposed current hysteresis coordinated control method, effectively suppressing the ripple of the inductor current and expand the PV voltage fluctuation in the DC-DC link on the basis of maintaining power conversion efficiency as much as possible. [ABSTRACT FROM AUTHOR]

The traditional short circuit ratio index does not consider the impact of energy storage devices (ESDs) and cannot be used for the collaborative optimization of ESDs and renewable energy sources (RESs). Therefore, this paper proposes a novel synergistic capacity short circuit ratio (SCSCR) index, which can reflect the interaction between multiple RESs and ESDs on each bus under different capacities. Based on the proposed SCSCR, a coordinated optimization method of RESs and ESDs is proposed. It ensures system strength while determining the optimal location and capacity proportion of RESs and ESDs. Firstly, the location and capacity proportion of RESs are obtained through optimization method because the system strength is mainly determined by RESs connection points. Secondly, the optimal location and capacity proportion of the ESDs are found under the optimal configuration of the RESs. The proposed optimization method ensures the system strength while obtaining the optimal location and optimal capacity proportion of RESs and ESDs. Finally, the simulation results verify the effectiveness of this method on the IEEE 9 bus and 39 bus systems, providing reference for the site selection and efficient operation of RESs and ESDs. [ABSTRACT FROM AUTHOR]

The variable pressure source system is widely used in renewable energy recovery scenarios. However, the instability of the new energy input and the nonlinearity of the power generation system can lead to problems such as more difficult tracking and control of the maximum power point and poor power generation quality. To address the problem of lower efficiency under varying inputs, which is prevalent in renewable energy generation, this paper establishes an energy recovery system model based on power conversion rectifier topology, designs a speed current double closed‐loop control strategy, proposes an online variable‐step maximum efficiency point tracking method based on experience curves, and tests the overall energy recovery effect through system simulation and comparative experiments. The simulation results show that the maximum efficiency point tracking method proposed in this paper reduces the number of optimization searches by 50% and improves optimization speed. The experiment results show that, under the drastic changes of the input, the method proposed in this paper could reduce the total harmonic distortion to 5.65%, improve the energy recovery efficiency by 10.976%, and reduce the fluctuation ratio of the voltage to 2.43%. This study can provide an important reference for the collection and utilization of new energy sources. [ABSTRACT FROM AUTHOR]

This paper presents an innovative scheme to enhance the efficiency of power extraction from wind energy conversion systems (WECSs) under random loads. The study investigates how stochastic load consumption, modeled and predicted using a Markov chain process, impacts WECS efficiency. The suggested approach regulates the rectifier voltage rather than the rotor speed, making it a sensorless and reliable method for small-scale WECSs. Nonlinear WECS dynamics are represented using Takagi–Sugeno (TS) fuzzy modeling. Furthermore, the closed-loop system's stochastic stability and recursive feasibility are guaranteed regardless of random load changes. The performance of the suggested controller is compared with the traditional perturb-and-observe (P&O) algorithm under varying wind speeds and random load variations. Simulation results show that the proposed approach outperforms the traditional P&O algorithm, demonstrating higher tracking efficiency, rapid convergence to the maximum power point (MPP), reduced steady-state oscillations, and lower error indices. Enhancing WECS efficiency under unpredictable load conditions is the primary contribution, with simulation results indicating that the tracking efficiency increases to  99.93 % . [ABSTRACT FROM AUTHOR]

The paper presents a novel control algorithm for a standalone Photovoltaic Reverse-Osmosis (PV-RO) system without a battery. It highlights the importance of tracking the maximum power tracking points in the photovoltaic panels. The development of new methods has become more urgent than before. The PV-RO system combines solar energy and reverse osmosis technology to provide reliable and sustainable access to clean drinking water in remote areas. Traditional PV-RO systems often require energy storage solutions such as batteries to maintain a constant water supply during periods of low solar irradiation. However, the integration of batteries adds complexity, cost, and environmental impact to the system. The paper presents the development of different MPPT techniques, which are better than traditional techniques (more accurate and faster). The paper also provides a comparison with the studied methods (P&O and Incremental Conductance) and the proposed one. [ABSTRACT FROM AUTHOR]

Photovoltaic power generation systems mainly use the maximum power tracking (MPPT) controller to adjust the voltage and current of the solar cells in the photovoltaic array, so that the photovoltaic array runs at the maximum power point (MPP) to achieve the purpose of maximum power output. At present, photovoltaic power stations mainly adopt the traditional method to track the maximum power point, but this fixed step method easily causes output power oscillation of the photovoltaic array when tracking the maximum power point, and it easily falls into the local extreme point under partial shadow conditions. In order to solve these problems, this paper proposes an improved perturbation observation method and backstepping method (IP&O-backstepping) to replace the traditional method applied to the MPPT controller to optimize the operating state of the solar cell, thereby improving the output power point of the photovoltaic array and increasing the output power of the photovoltaic array. The algorithm first uses the improved perturbation and observation (IP&O) method to search the maximum power point of the photovoltaic array and output the reference voltage. Secondly, the reference voltage is input into the backstepping algorithm for voltage tracking. Finally, the algorithm tracks the reference voltage and makes the photovoltaic array operate at the maximum power point. The simulation is carried out by using MATLAB/Simulink. The IP&O-backstepping algorithm is compared with the intelligent algorithm and the traditional method, and the results show that compared to the above algorithm, the IP&O-backstepping algorithm can not only track the maximum power point of the photovoltaic array, but also has a faster tracking speed, and the output power has almost no oscillation when the photovoltaic array runs at the maximum power point. [ABSTRACT FROM AUTHOR]

Based on the characteristics of the whale optimization algorithm (WOA) and perturbation observation (P&O) method, this paper proposes a novel hybrid approach called the improved chaotic whale optimization combined with perturb and observe (ICWOA-P&O) method for maximum power point tracking (MPPT) control to solve the challenge of low efficiency in photovoltaic (PV) power generation under local shadows. First, the ICWOA is used for a global search to quickly locate the position of the maximum power point (MPP). Then, the P&O method is used for a fine-grained local search to quickly track the position of the global maximum power point (GMPP) with low oscillation. To ensure accuracy, the tracking performance of the ICWOA-P&O method is comprehensively compared with the WOA-P&O, WOA, and PSO models under four conditions: uniform irradiance, static local shading, dynamic shading, and sudden changes in irradiance and temperature. The simulation results verify that under the above four conditions, the ICWOA-P&O method can track the MPP continuously and stably and greatly improves the convergence time and accuracy. Compared with the other three methods, the ICWOA-P&O method can effectively obtain the fastest tracking speed (less than 0.1 s), the highest tracking accuracy (more than 99.97%), the smallest relative error (less than 0.03%), and the smallest oscillation fluctuation. Finally, this study integrated the ICWOA-P&O algorithm into the designed MPPT controller hardware and established a practical PV experimental platform based on the ICWOA-P&O control algorithm for practical tests. [ABSTRACT FROM AUTHOR]

Due to the increasing energy demand, traditional fossil fuels are gradually decaying day by day as analyzed by many researchers. Fossil fuels are not sufficient to fulfil the requirement of energy demand and it also produces greenhouse gas emissions. In this regard, worldwide research is going on related to renewable energy sources (RESs) like solar photovoltaic (SPV), wind turbines, fuel cells etc. The source of SPV is plentiful and environment friendly which converts solar radiation to non-linear electrical power. This power is not suitable for a stable system. Therefore, the maximum power point tracking (MPPT) controller is required to find the optimum maximum power point (MPP) to the load. The MPPT technology regulates the duty-cycle in favour of the DC-DC converter to continuously obtain maximum power from the SPV arrays. In the past few decades, the learning of MPPT techniques has made substantial progress in the RESs. This research article analyzes the performance of various MPPT techniques in the proposed SPV framework. The main investigation is to assess different MPPT techniques to optimize power from the SPV framework. The artificial neural network (ANN)-MPPT method has been observed to be more effective in output power production and transient response about the MPP than conventional perturb and observe (P&O)-MPPT and fuzzy logic controller (FLC)-MPPT technology. [ABSTRACT FROM AUTHOR]

This article implements maximum power point tracking (MPPT) based on the improved hill-climbing algorithm for photovoltaic (PV) systems feeding resistive loads. A direct current-to-direct current boost converter is inserted between the PV system and the load to achieve matching. The converter is managed using MPPT based on the hill-climbing algorithm. The objective of this paper is to optimize the code program to achieve the best compromise between accuracy and rapidity by implementing this algorithm using a microcontroller. Two PV systems are tested under identical meteorological conditions. In the first, an improved hill-climbing MPPT controller is used whereas, in the second, the conventional version is employed. The experimental results obtained show a significant enhancement in terms of speed for the improved algorithm with a value of 0.4 s for the response time and 3% for the oscillation power; those values remain satisfactory in terms of precision of the algorithm compared with the conventional system studied and the compared algorithm from the literature. [ABSTRACT FROM AUTHOR]

This paper presents an innovative way to enhance the performance of photovoltaic (PV) arrays under uneven shadowing conditions. The study focuses on a triple-series–parallel ladder configuration to exploit the benefits of increased power generation while addressing the challenges associated with uneven shadowing. The proposed methodology focuses on the implementation of improved sliding-mode control technique for efficient global maximum power point tracking. Sliding-mode control is known for its robustness in the presence of uncertainties and disturbances, making it suitable for dynamic and complex systems such as PV arrays. This work employs a comprehensive simulation framework to comment on the performance of the suggested improved sliding-mode control strategy in uneven shadowing scenarios. Comparative analysis has been done to show the better effectiveness of the suggested method than the traditional control strategies. The results demonstrate a remarkable enhancement in the tracking accuracy of the global maximum power point, leading to enhanced energy-harvesting capabilities under challenging environmental conditions. Furthermore, the proposed approach exhibits robustness and adaptability in mitigating the effect of shading on the PV array, thereby increasing overall system efficiency. This research contributes valuable insights into the development of advanced control strategies for PV arrays, particularly in the context of triple-series–parallel ladder configurations operating under uneven shadowing conditions. Under short narrow shading conditions, the improved sliding-mode control method tracks the maximum power better compared with perturb & observe at 20.68%, incremental-conductance at 68.78%, fuzzy incremental-conductance at 19.8%, and constant-velocity sliding-mode control at 1.25%. The improved sliding-mode control method has 60% less chattering than constant-velocity sliding-mode control under shading conditions. [ABSTRACT FROM AUTHOR]

In addressing the critical challenge of developing sustainable energy solutions for electric vehicle (EV) battery charging, this study introduces an innovative direct current (DC) microgrid system optimized for areas with high solar irradiance, such as Ain El Ibel, Djelfa. The research confronts two primary difficulties: maximizing solar energy utilization in the microgrid system and ensuring system stability and response accuracy for reliable EV charging. To tackle these challenges, the study presents two original achievements. Firstly, it develops a neural network-enhanced Maximum Power Point Tracking (MPPT) controller, which is further optimized with Particle Swarm Optimization (PSO) to increase the efficiency of solar energy capture. Secondly, it refines the system's reliability through the advanced calibration of a Fractional Order Proportional-Integral (FOPI) controller using the Grey Wolf Optimization (GWO) technique, marking a notable improvement in microgrid system stability and response accuracy. The integration of a solar panel array, battery storage, and a supercapacitor, coupled with these advanced optimization techniques, exemplifies a significant leap forward in enhancing efficiency and reliability of EV battery charging through renewable energy sources. Comprehensive simulation and evaluation of the system underscore its superiority over conventional methods, demonstrating the effectiveness of combining neural network-based optimization with PSO and GWO. This breakthrough not only advances the field of renewable energy, particularly for solar-powered EV charging stations, but also aligns with global efforts towards sustainable transportation solutions. [ABSTRACT FROM AUTHOR]

Programmable matter refers to material that can be programmed to alter its physical properties, including its shape. Such matter can be built as a lattice of attached robotic modules, each seen as an autonomous agent with communication and motion capabilities. Self-reconfiguration consists in changing the initial arrangement of modules to form a desired goal shape, and is known to be a complex problem due to its algorithmic complexity and motion constraints. In this paper, we propose to use a max-flow algorithm as a centralized global planner to determine the concurrent paths to be traversed by modules through a porous structure composed of 3D Catoms meta-modules with the aim of increasing the parallelism of motions, and hence decreasing the self-reconfiguration time. We implement a traffic light system as a distributed asynchronous local planning algorithm to control the motions to avoid collisions. We evaluated our algorithm using VisibleSim simulator on different self-reconfiguration scenarios and compared the performance with an existing fully distributed synchronous self-reconfiguration algorithm for similar structures. The results show that the new method provides a significant gain in self-reconfiguration time and energy efficiency. [ABSTRACT FROM AUTHOR]

The efficiency of standalone photovoltaic (PV) systems heavily relies on the effectiveness of their maximum power point tracking (MPPT) controller. This study aims to improve the operational efficiency and reliability of standalone PV systems by introducing a novel control scheme, the Immersion and Invariance Neural Network (II-NN). This innovative system integrates a nonlinear estimator of solar irradiance with a neural network (NN) model, eliminating the need for direct irradiance measurements and associated costly sensors. The proposed methodology uses the Immersion and Invariance algorithm to design a nonlinear estimator that leverages the real-time measurements of PV current and voltage to estimate the incident irradiance. The NN then processes this estimated irradiance to determine the MPP voltage accurately. A robust nonlinear controller ensures the PV system operates at the MPP. This approach stands out by managing the nonlinearities, parametric uncertainties, and dynamic variations in PV systems without relying on direct irradiance measurements. The II-NN system was rigorously tested and validated under real climatic conditions, providing a realistic performance assessment. The principal results show that the II-NN system achieves a mean error of 0.0183V and a mean absolute percentage error of 0.3913%, with an overall MPPT efficiency of up to 99.84%. Comparisons with the existing methods, including perturb and observe, incremental conductance, and three other recent algorithms, reveal that the II-NN system outperforms these alternatives. The major conclusion is that the II-NN algorithm significantly enhances the operational efficiency of PV systems while simplifying their implementation, making them more cost-effective and accessible. This study substantially contributes to PV system control by advancing a robust, intelligent, and sensorless MPPT control scheme that maintains high performance even under varying and unpredictable climatic conditions. [ABSTRACT FROM AUTHOR]

This work presents a system design for extracting maximum power using the modified maximum power point tracking (MPPT) technique and a novel high-gain DC-DC converter, which was then used to supply a microgrid system with a conventional buck converter. We present a novel structure comprising the MPPT, voltage boosting, and voltage regulating components for a DC microgrid in a single system. The most important features of a photovoltaic (PV) system include a high-gain converter and maximum PV power extraction; considering these, we present a high-gain DC-DC converter that boosts the output voltage to ten times the input voltage. Furthermore, the MPPT technique extracts maximum power from the PV panel based on model predictive control through its better transient response than the conventional incremental conductance method. The MPPT approach was tested with both fixed-and variable-step operations, and the results were compared for load variations. Considering the economics of the system, the proposed approach attempts cost reduction by optimizing the number of sensors to two instead of three. Simulations were conducted under different environmental conditions using MATLAB-Simulink, and the performance differences between the conventional incremental conductance and proposed MPPT-based methods are shown. Next, DC voltage regulation was implemented for the proposed PV and existing systems by considering different load and irradiation conditions while maintaining constant temperature. The simulation results showed the latter system had better performance than the former under different environmental conditions, with persistent results for voltage regulation at different load and irradiation conditions. [ABSTRACT FROM AUTHOR]

A power processing system (PPS) with a seven‐level dual‐buck inverter (SLDBI) for a photovoltaic (PV) power generation system is proposed. The PPS is comprised of a boost power converter and an SLDBI. The boost power converter matches the voltage of the PV array with that of the SLDBI and functions as the maximum power point tracker for the PV array. The SLDBI consists of a dual‐buck bridge converter (DBBC) and a three‐level DC converter (TLDC). The DBBC effectively reduces leakage current in the PPS and prevents current from flowing through the parallel body diode of the power switches, allowing the use of MOSFETs as power electronic switches to enhance power efficiency. The TLDC is structured as a semi‐controlled full‐bridge system to generate a three‐level DC voltage. Besides, the DBBC and TLDC produce a seven‐level AC voltage and drive a sinusoidal current to the utility. The SLDBI reduces both the filter inductor size and the switching loss of power switches. With only six power switches, it simplifies the power circuit. A hardware prototype has been built to experimentally verify the proposed PPS. [ABSTRACT FROM AUTHOR]

This article presents the contribution of multicellular converters in improving of the quality of power produced in photovoltaic chain, with the aim of exploiting the maximum power produced by the photovoltaic generator with low oscillations around of the maximum power point (MPP) at steady state and to reduce switching losses. After modeling the multicellular parallel boost converter, fractional short circuit current (FSCC) MPPT was modified to get an estimated photocurrent as a reference to control the inductance current for good functioning of the converter in pursuit of the maximum power point. To verify the performance of the proposed solution, the system was submitted to irradiance and temperature variations. The simulations carried out in the Matlab/Simulink environment presented satisfactory results of the proposed solution, in comparison with the high-gain quadratic boost converter we have a response time of 0.04 s, power oscillations at maximum point around 0.05 W and efficiency of 99.08%; in comparison with the interleaved high-gain boost converter the results show a response time of 0.1 s for the transferred power, a very low output voltage ripples of 0.001% and 98.37% as efficiency of the chain. The proposed solution can be connected to a grid with a reduction of level of the inverter and active filter. [ABSTRACT FROM AUTHOR]

The Photovoltaic system (PV) is gaining popularity as a future energy source due to its vast, secure, essentially limitless, and widely accessible nature. However, a prevalent challenge in PV systems is the impact of solar cell radiation intensity and temperature on the output power induced in the photovoltaic module. As a result, monitoring the input source's maximum power point becomes essential for maximizing the effectiveness of the renewable energy system. In order to harvest the most power from the photovoltaic system and channel it to the load through a boost converter, which raises the voltage to the necessary level, most Power Point Tracking, or MPPT, is essential. This study uses the Perturb and Observe (P&O) method to track the photovoltaic module's Maximum Power Point (MPP) and maximize the system's overall power production to overcome this obstacle. By varying the array terminal voltage or current, the P&O approach perturbs the system and then compares the PV output power to the preceding perturbation cycle. Therefore, the main goal of this study is to simulate and assess the effectiveness of the P&O MPPT algorithm in MATLAB/Simulink when it comes to maximizing power extraction from a photovoltaic system. The simulation findings show that the suggested P&O approach is a reliable means of tracking MPP from PV panels, even when there are fluctuations in solar irradiation. [ABSTRACT FROM AUTHOR]

Partial shading of Photovoltaic (PV) system is commonly observed in outdoor field conditions specially in the Road- Integrated Photovoltaic systems. The non-uniform illumination causes mismatch in the electrical output between the cells, which results in an instantaneous effect on power generated and a long-term effect on reliability, it also causes hotspot issues. The objective of this work is to study the partial shading effect on the Road-Integrated PV (RIPV) cells and the soiling model effect under the active and inactive states of Multilevel Bypass (MLB) diodes. In this work, an electrical Simulation Program with Integrated Circuit Emphasis (Ng-SPICE) simulation model has been developed using irradiance model data. This data was collected using a Digital Elevation Model (DEM) called Light Resolution Light detection and Ranging (LidAR). The model was simulated using the Simple Sky Dome Projector (SSDP) software to analyze the impact of different shading conditions and study the effect of MLB diodes on the partial shading RIPV modules and the effect of MLB diodes in the soiling model. [ABSTRACT FROM AUTHOR]

Maximum power point tracking (MPPT) algorithms are commonly used in photovoltaic (PV) systems to optimize the power output from the solar panels. Among the various MPPT algorithms, the perturb and observe (P&O) algorithm is a popular choice due to its simplicity and effectiveness. However, the basic P&O algorithm has some limitations, such as oscillations and steadystate error under rapidly changing irradiance conditions. The enhanced algorithm includes a modified perturbation step and a dynamic step size adjustment scheme. This reduces the oscillations and improves the tracking accuracy. In the dynamic step size adjustment scheme, the step size is adjusted based on the rate of change of the PV output power. This improves the tracking performance under rapidly changing irradiance conditions. In order to prove the performance of the designed control algorithm, we will test it under simple climatic conditions of fixed temperature (30°C) and variable irradiation in the form of steps (500W/m2 and 2000w/m2) and see the system response. The performance of the enhanced P&O algorithm has been evaluated using MATLAB simulations. [ABSTRACT FROM AUTHOR]

This study presents the design and implementation of an efficient off-grid photovoltaic (PV)-powered motor-pump system utilizing a two-stage power converter. The system integrates a neural network-based maximum power point tracking (MPPT-NN) algorithm with a proportional integral (PI) controller and an additional bidirectional PI charger. Controller gains are optimized using ant colony optimization (ACO) to achieve optimal performance. The proposed MPPT-NN-PI/ACO controller enhances control responses and improves energy utilization efficiency by 17% compared to traditional PI controller. Performance comparisons of MPPT techniques demonstrates that the proposed controller outperforms several existing methods, including commercial on-off controllers, the modified Perturb & Observe algorithm, and neural network-based controllers, by approximately 4%-20%. It shows a slightly different performance of about 1%-6% compared to advanced adaptive controllers, including fuzzy logic and neuro-fuzzy controllers. For bidirectional charger performance, the DC bus voltage connecting the boost converter and bidirectional converter remains stable with small ripples and is well-aligned with the reference voltage, ensuring uninterrupted operation under varying weather conditions. The bidirectional charge management effectively maintains battery state-of-charge (SOC), showing a decline during periods of insufficient PV energy and achieving full charging during periods of excess PV energy. System performance is validated through both simulation and laboratory-scale prototyping, ensuring robust operation. [ABSTRACT FROM AUTHOR]

Owing to the effect of temperature on the maximum power point of photovoltaic modules, the anticipated benefits of maximum power point tracking charge controller are also greatly affected under high-temperature environments. This paper presents experimental study results based on the investigations of the effect of high cell temperatues on the performance of maximum power point tracking charge controllers. The study was carried out over three months at Kenyatta University in Nairobi, Kenya under moderate and high temperature conditions. The experiments are carried out using two market-ready maximum power point tracking charge controllers and a normal pulse-width modulation charge controller as the reference. The study establishes that below 50 °C, the maximum power point tracking charge controller exhibits significant advantages over its counterpart with about 24% - 29%, additional energy gain. However, at high cell temperatures, the maximum power point tracking charge controller loses its added advantage over the pulse-width modulation charge controller and its performance benefits drop from an average of 28% to 0.2%. This demonstrates that an ordinary pulse-width modulation charge controller would achieve the same yields under high-temperature conditions and at an even lower premium. [ABSTRACT FROM AUTHOR]

Solar water pumps are crucial for farmers, significantly reducing energy costs and providing independence from conventional fuels. Their adoption is further incentivized by government subsidies, making them a practical choice that aligns with sustainable agricultural practices. However, the cost of the required solar panels for the chosen power makes it essential to optimize solar water pumping systems (SWPS) for economic viability. This study enhances the efficiency and reliability of permanent magnet synchronous motor (PMSM)-driven SWPS in rural areas using hybrid maximum power point tracking (MPPT) algorithms and voltage-to-frequency (V/f) control strategy. It investigates the sensorless scalar control method for PMSM-based water pumps and evaluates various MPPT algorithms, including grey wolf optimization (GWO), particle swarm optimization (PSO), perturb and observe (PO), and incremental conductance (INC), along with hybrid combinations. The study, conducted using MATLAB Simulink, assesses these algorithms on convergence time, MPPT accuracy, torque ripple, and system efficiency under different partial shading conditions. Findings reveal that INC-GWO excels, providing higher accuracy, faster convergence, and reduced steady-state oscillations, thus boosting system efficiency. The V/f control strategy simplifies control mechanisms and enhances performance. Considering system non-idealities and maximum duty cycle limitations, PMSM-based SWPS achieve superior efficiency and stability, making them viable for off-grid water pumping applications. [ABSTRACT FROM AUTHOR]

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The maximum power point tracker (MPPT) is essential for optimizing the performance of PV systems, especially off-grid systems in remote areas. Although incremental conductance (InC) is the most commonly used of MPPT strategies, it has drawbacks, including trade-off between fluctuation and convergence speed, as well as the occurrence of drift phenomenon. To address these issues, this paper proposes an enhanced InC MPPT strategy. The latter utilizes a new adaptive step magnitude based on an absolute value of the variation in power, voltage, and current of the PV array to automatically adjust the perturbation step with respect to the weather variation circumstances. In addition, it includes a drift avoidance mechanism to further increase the robustness of the tracking. To investigate the performance of the amended InC MPPT strategy, it is integrated into an off-grid PV system, which feeds batteries as loads. First, it is examined by simulation using MATLAB/Simulink software under sudden and ramp changes in solar intensity and ambient temperature. Then it is performed using an experimental test bench using a dSPACE DS1104 card. The results obtained during simulation and practical investigations verify the highest effectiveness of the amended InC strategy compared to the original one in terms of tracking precision, fluctuation, converging velocity, power loss, and tracking efficiency. Additionally, it shows robust and superior performance compared to recently proposed MPPT techniques, with the lowest tracking time (less than 0.012 s) and the highest average tracking efficiency (99.84%), leading to effective optimization of PV energy. [ABSTRACT FROM AUTHOR]

Abstract Solar photovoltaic energy is a vital renewable resource because it is clean, endless, and pollution‐free. Due to the fast growth of the semiconductor and power electronics sectors, photovoltaic (PV) technologies are climbing significant attention in modern electrical power applications. Operating PV energy conversion systems at the maximum power point is essential for getting the maximum power output and raising efficiency. This paper proposes a regression‐based Perturb and Observe method to quickly find a global maximum power point, avoiding being stuck in local maxima, likewise analytical and metaheuristic methods. The improved control focuses on the narrowed search areas by linear and non‐linear regression analyses using the generated PV model on a flexible Python environment. Furthermore, the method's accuracy is validated in real time under variable temperatures, irradiations, and loads. This method was proven with a hardware implementation. The proposed method is more than 98% accurate and can withstand long‐term modelling. The suggested regression‐based perturbation and observation method provided a short learning time and easy implementation. Additionally, the dynamic recorded results can be visualized for researchers to utilize efficiently.

This paper compares different maximum power point tracking (MPPT) control strategies in microgrid dynamics, focussing on perturb and observe (P&O), adaptive neuro-fuzzy inference system (ANFIS), particle swarm optimisation (PSO), and PSO sliding mode controller techniques. The study investigates their performance under varying microgrid conditions, considering factors like weather and load variations. The simulation results provide a detailed comparative analysis of the power at the point of common coupling (PCC) for MPPT techniques at different time intervals. Both the P&O and PSO sliding mode recorded a power output of 287 kW, while PSO-ANFIS achieved a slightly higher power output of 294 kW. At 2.5 seconds, the P&O method recorded a power output of 712 kW, while the PSO sliding mode and the PSO-ANFIS techniques achieved 717 kW and 738 kW, respectively. Overall, the PSO-ANFIS technique consistently outperformed the other methods in terms of power output, demonstrating its effectiveness in maximising energy extraction and adaptability to dynamic conditions. These findings provide valuable insights for designing and implementing MPPT controllers in microgrid systems, emphasising the efficiency of the hybrid PSO-ANFIS technique in enhancing the overall performance and stability of renewable energy systems.

In this research work, three maximum power point tracking (MPPT) control algorithms based on power feedback are applied to two types of converters, namely Zeta and buck-boost, in the photovoltaic (PV) system. The PV system is intended to guarantee the power supply to a remote smart greenhouse. The control algorithms investigated are perturb and observe (P&O), incremental conductance (IncCond), and fuzzy logic (FLC) methods. Their performance are investigated and compared for each converter. It is found that the maximum power is always achieved, even during abrupt changes in irradiation or/and in temperature. The three methods have shown to have good performance; fast response time and very low steady-state error, with minor preference of the P&O method where the output voltage followed the input with high efficiency. The comparative study revealed that the power response time of the PV generator under stable conditions (constant irradiance and constant temperature) for P&O and IncCond was longer in the buck-boost converter than in the Zeta. On the other hand, the ripple level was better for the buck-boost. For the FLC, the maximum power was reached in a shorter time (short response time) with the smallest ripple. As for operation under variable environmental conditions, the Zeta outperformed the buck-boost for each control technique.

In practical engineering applications, factors like dust adhesion and environmental changes can cause photovoltaic arrays to exhibit multiple peaks in output power. An optimization algorithm with global optimization capability is needed to track its maximum power. In this regard, this paper proposes an improved marine predator algorithm (IMPA) to extract the maximum power point of photovoltaic system under complex solar irradiation conditions. To overcome the issues in the traditional marine predator algorithm (MPA), the opposition-based learning(OBL) strategy is introduced in IMPA, and the sine cosine algorithm (SCA) is integrated into the iteration stage to enhance the search ability of the algorithm. Furthermore, the low-order converter in the traditional MPPT control system is replaced by the Zeta converter, which increases the operating voltage range. Ultimately, simulation results demonstrate that the MPPT based on IMPA has higher tracking efficiency and shorter response time.The experimental results also indicate the practical feasibility of this method, as well as its high level of stability and robustness. [ABSTRACT FROM AUTHOR]

Now, the present power generation and distribution companies are working on renewable energy systems because their features are low-level atmospheric pollution, producing less greenhouse pollutants, more reliability, good static performance, and high robustness. In this work, the sunlight Photovoltaic (PV) system is selected because of its advantages are easily available in the atmosphere, high flexibility, zero carbon footprint, easy to maintain, and less transportation cost. However, solar networks produce nonlinear I-V characteristics. Due to the non-linear nature of the solar system, the extraction of peak voltage from the PV module is a very tough task. So, in this article, a variable modified step grey wolf method is integrated with the adaptive-neuro-fuzzy-inference-system to improve the energy production of solar systems. The features of this proposed maximum power point tracking controller are fast identification of the solar system operating point, generating the less fluctuated oriented converter load power, providing more MPP tracking accuracy, less dependence on the solar system installation, and useful for all environmental bad weather conditions. Another problem of solar systems is less voltage production which is improved by introducing a wide voltage gain-boost converter circuit. The features of this converter circuit are less development cost because it does not require more power electronics switches. Here, the proposed IGWM with an AFLC-fed sunlight system is investigated by using MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

In this article, a model predictive control (MPC) with common-mode voltage (CMV) suppression is proposed for single-phase cascaded H-bridge (CHB) inverters, which can also simultaneously achieve control objectives of gridconnected current tracking, voltages balancing of different H-bridge submodules on the DC-side and switching frequency reduction. To suppress high-frequency components of the common-mode voltage without additional switching devices, the algorithm proposed designs the predicted and reference values of the CMV and incorporates them in the cost function. At the same time, the capacitor voltages balancing control is integrated in the calculation of the optimal modulation function of the H-bridge, which reduces the complexity of control effectively. Besides, switching times of the MOSFETs are compared in two cycles. The cost function is constructed to represent comprehensive effect of the control. Finally, an experiment is performed on the hardware-in-the-loop experimental platform. The experimental results show that the proposed algorithm can offer a better voltage THD and reduce the times of switch action by nearly half while maintaining high-precision current tracking and maximum power point of photovoltaic modules, which alleviate the potential electromagnetic interference and cabling problem. [ABSTRACT FROM AUTHOR]

When the battery or supercapacitor is connected to the output of a PV system, the conventional voltage equation expressing its mathematical model usually must be replaced by the current relationship to study the maximum power point tracking (MPPT) control theory. However, hitherto, there is a lack of an attempt to disclose the current constraint relationships at the maximum power point (MPP), which leads to the potential risk of MPPT failure. To solve this problem, in this paper, the MPPT constraint conditions on the basis of currents are built and then a new MPPT control strategy is proposed. In this strategy, a linearized model parameter of a PV cell is used as the bridge to find the current relationships. On the basis of them, some expressions involving the duty cycle are built to directly calculate the control signal of the MPPT controller. Meanwhile, an implementation method is designed to match this proposed MPPT strategy. Finally, some simulation experiments are conducted. The simulation results verify that the proposed MPPT constraint expressions are accurate and workable and that the proposed MPPT strategy and its implementation process are feasible and available. In addition, the simulation results also show that the proposed MPPT strategy has a better MPPT speed and the same MPPT accuracy when the P&O method and fuzzy algorithm are compared. By this work, the MPPT constraint conditions based on current relationships are first found, representing a breakthrough in disclosing the inherent relationships between different currents when the PV system is operating around the MPP. [ABSTRACT FROM AUTHOR]

This article presents a proportional-integral sliding mode control (PI-SMC) approach for a two-mass variable speed wind turbine (VSWT) system. Most studies on wind turbines typically focus mainly on the electromagnetic part of the generators, or even on the high-speed part, considering the shaft stiffness as negligible. However, the generator torque is actually driven by the aerodynamic torque, and a two-mass system like the one studied here plays the role of a transmission element for this power. To address this challenge, the problem of low power generation resulting from wind speed variability is tackled by designing a PI-SMC control law, capable of controlling the mechanical turbine model that optimizes power and torque by tracking the maximum power point (MPPT) for rotational speed and aerodynamic power. To validate the developed theoretical results, an application of the wind turbine system is simulated in Matlab/Simulink, for a particular case. The control used is capable of satisfying the dynamic performance of the systems. [ABSTRACT FROM AUTHOR]

Hybrid Renewable Energy Systems (HRES) have recently been proposed as a way to improve dependability and reduce losses in grid-connected load systems. This research study suggests a novel hybrid optimization technique that regulates UPQC in order to address the Power Quality (PQ) problems in the HRES system. The load system serves as the primary link between the battery energy storage systems (BESS), wind turbine (WT), and solar photovoltaic (PV) components of the HRES system. The major objective of the study is to reduce PQ issues and make up for the load requirement inside the HRES system. The addition of an Optimized Fractional Order Proportional Integral Derivative (O-FOPID) controller improves the efficiency of the UPQC. The Crow-Tunicate Swarm Optimization Algorithm (CT-SOA), an enhanced variant of the traditional Tunicate Swarm Optimization (TSA) and Crow Search Optimization (CSO), is used to optimize the control parameters of the FOPID controller. Utilizing the MATLAB/Simulink platform, the proposed method is put into practice, and the system's performance is assessed for sag, swell, and Total Harmonic Distortion (THD). The THD values for the PI, FOPID, and CSA techniques, respectively, are 5.9038%, 4.9592%, and 3.7027%, under the sag condition. This validates the superiority of the proposed approach over existing approaches. [ABSTRACT FROM AUTHOR]

The present power production companies are working on renewable energy systems because their features are more reliable for the local energy consumers, high continuity in the energy production, and less cost is required for maitainence. In this article, the proton exchange membrane fuel stack (PEMFS) renewable energy is utilized to supply energy to the automotive systems. Here, the PEMFS is selected because of its merits are high energy density, quick system response concerning the source operational temperature, and more suitable for electric vehicle application. However, the PEMFS supplied voltage is completely nonlinear which is solved by utilizing the modified particle swarm optimization with adaptive neuro-fuzzy inference system (MPSO with ANFIS) controller. This hybridization-based maximum power point tracking controller provides more accuracy, high power point identifying speed, best dynamic response at different fuel stack functioning temperature conditions, and easy maitainence. Here, the fuel stack generated current is very high which is optimized by introducing the new DC–DC converter. The advantages of this DC–DC converter are more voltage transformation ratio, low-level voltage stress appearing across the switches, and wide voltage gain. The overall system is investigated by utilizing the MATLAB/Simulink tool. [ABSTRACT FROM AUTHOR]

As the significance and complexity of solar panel performance, particularly at their maximum power point (MPP), continue to grow, there is a demand for improved monitoring systems. The presence of variable weather conditions in Maroua, including potential partial shadowing caused by cloud cover or urban buildings, poses challenges to the efficiency of solar systems. This study introduces a new approach to tracking the Global Maximum Power Point (GMPP) in photovoltaic systems within the context of solar research conducted in Cameroon. The system utilizes Genetic Algorithm (GA) and Backstepping Controller (BSC) methodologies. The Backstepping Controller (BSC) dynamically adjusts the duty cycle of the Single Ended Primary Inductor Converter (SEPIC) to align with the reference voltage of the Genetic Algorithm (GA) in Maroua's dynamic environment. This environment, characterized by intermittent sunlight and the impact of local factors and urban shadowing, affects the production of energy. The Genetic Algorithm is employed to enhance the efficiency of BSC gains in Maroua's solar environment. This optimization technique expedites the tracking process and minimizes oscillations in the GMPP. The adaptability of the learning algorithm to specific conditions improves energy generation, even in the challenging environment of Maroua. This study introduces a novel approach to enhance the efficiency of photovoltaic systems in Maroua, Cameroon, by tailoring them to the specific solar dynamics of the region. In terms of performance, our approach surpasses the INC-BSC, P&O-BSC, GA-BSC, and PSO-BSC methodologies. In practice, the stabilization period following shadowing typically requires fewer than three iterations. Additionally, our Maximum Power Point Tracking (MPPT) technology is based on the Global Maximum Power Point (GMPP) methodology, contrasting with alternative technologies that prioritize the Local Maximum Power Point (LMPP). This differentiation is particularly relevant in areas with partial shading, such as Maroua, where the use of LMPP-based technologies can result in power losses. The proposed method demonstrates significant performance by achieving a minimum 33% reduction in power losses. [ABSTRACT FROM AUTHOR]