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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.

We present an algorithm for evaluating analytic nuclear energy gradients of the state-averaged density matrix renormalization group complete-active-space self-consistent field (SA-DMRG-CASSCF) theory based on the newly derived coupled-perturbed (CP) DMRG-CASSCF equations. The Lagrangian for the conventional SA-CASSCF analytic gradient theory is extended to the SA-DMRG-CASSCF variant that can fully consider a whole set of constraints on the parameters of multi-root canonical matrix product states formed at all the DMRG block configurations. An efficient algorithm to solve the CP-DMRG-CASSCF equations for determining the multipliers was developed. The complexity of the resultant analytic gradient algorithm is overall the same as that of the unperturbed SA-DMRG-CASSCF algorithm. In addition, a reduced-scaling approach was developed to directly compute the SA reduced density matrices (SA-RDMs) and their perturbed ones without calculating separate state-specific RDMs. As part of our implementation scheme, we neglect the term associated with the constraint on the active orbitals in terms of the active–active rotation in the Lagrangian. Thus, errors from the true analytic gradients may be caused in this scheme. The proposed gradient algorithm was tested with the spin-adapted implementation by checking how accurately the computed analytic energy gradients reproduce numerical gradients of the SA-DMRG-CASSCF energies using a common number of renormalized bases. The illustrative applications show that the errors are sufficiently small when using a typical number of the renormalized bases, which is required to attain adequate accuracy in DMRG's total energies. [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]

A labeled multi‐Bernoulli filter is used to obtain estimates of the identities and states of targets in complex environments. However, when tracking multiple targets in dense clutters, the computational complexity of the traditional labeled multi‐Bernoulli filter will increase exponentially. A labeled multi‐Bernoulli tracking algorithm based on maximum likelihood recursive update is proposed, which can reduce the computational scale while maintaining tracking accuracy. Specifically, when performing posterior estimation, a maximum likelihood recursive update method is proposed to replace the complete enumeration, truncated enumeration, or sampling enumeration methods used in many traditional methods. Furthermore, combined with the Gaussian mixture technique, a maximum likelihood recursive updating labeled multi‐Bernoulli tracking algorithm is constructed. Simulation results demonstrated that the proposed filter obtained a good balance between the tracking accuracy and computational efficiency. [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]

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]

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]

Owing to the proven footmark for the clean, green energy, compact in size, cost effective and ease of availability of sunlight, applications of photovoltaic (PV) panels are continuously on rise. However, the low conversion efficiency is a major concern for researchers. The conversion efficiency may be improved significantly if PV panels are operating on maximum power point. Solar irradiations received by PV panel are not constant always. The irradiation may be linear and nonlinear, fast, slow and sudden varying in nature. Thus, tracking of maximum power point is a huge challenge. This irregular behaviour of incident irradiations may result in oscillations in output and low conversion efficiency. In order to enhance the performance, a suitably designed controller, operating at maximum power point is needed. This paper highlights maximum power point tracking under varying irradiation conditions (viz. Step Variable Irradiance and Ropp's irradiance profile) using two control techniques namely Perturb and Observe (P&O) and Incremental Conductance (IC). The simulation results obtained for two control techniques are compared for system efficiency, tracking of maximum power point and enhancement of voltage. The comparison of results reveals that there is far better improvement in overall efficiency, output power, output voltage and faster regaining of the maximum power point path with IC technique as compared to P&O technique. Thus, IC control technique excellently tracks maximum power point with reduced probability of deviation and better oscillation damping under varying incident irradiations. [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]

Copyright of Journal of Electric Power Science & Technology is the property of Changsha University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The focus of the work is on optimizing the wind power system to generate high-quality power from renewable energy sources. This article describes how to control a stand-alone PMSG wind turbine system using perturb and observe (P&O) maximum power point tracking (MPPT) controller. This aids in the regulation of output voltage levels and the maximum power provided to the load. The present study employs P&O MPPT control algorithm to optimize energy extraction from the wind resource, while simultaneously ensuring a stable voltage throughout the load. The goal of MPPT approaches is to establish a reference speed so that the wind energy conversion system (WECS) control system can follow the MPPT trajectory. The MPPT controllers can keep the system running smoothly irrespective of the wind speed fluctuations. There is a significant power output improvement over conventional controllers when using the proposed MPPT controller, according to the comparison results. The DC-DC boost converter was implemented for enhancing the low AC voltage given by the permanent magnet synchronous generator (PMSG). [ABSTRACT FROM AUTHOR]

An efficient machine learning approach for accomplishing the maximum power point tracking (MPPT) system for photovoltaic (PV) systems is proposed in this work. PV system is one of the most suitable renewable energy sources (RES) for electric vehicles (EV) based operations due to its ubiquitous availability and speed of installation. The deployment of PV-powered EVs reduces the quantity of carbon dioxide emitted into the atmosphere substantially. The primary objective of this research is to integrate a PV system with an EV load and to provide a constant power supply to the EV load with no distortions. A coupled inductor interleaved boost converter is used to raise voltage level of the PV because it has a wide conversions range with low leakage reaction times. Furthermore, the converter produces a consistent output with no fluctuations and high voltage gain. With the application of artificial neural network (ANN) based MPPT and recurrent neural network (RNN) based MPPT, the converter operational performance enhanced with steady dc link voltage is obtained. Consequently, it is stated that the employment of ANN and RNN-based MPPT controllers in PV-based systems offers improved DC link voltage regulation and lower power losses, thereby boosting system effectiveness. The MATLAB platform is used to test every component of the system's performance, and the findings show that the proposed system is more efficient than alternative approaches. [ABSTRACT FROM AUTHOR]

The low operating and maintenance expenses of photovoltaic (PV) power generation make it a popular choice for rural power generation systems. Solar radiation, temperature, and load impedance are the major factors influencing the final output of solar PV. Consequently, the solar PV system experiences oscillations in its operation. These oscillations in the operating point pose a difficulty in transferring maximum power from the source to the load in an efficient way. A method called as "maximum power point tracking" is used to address this problem. This technique eliminates oscillations ensure that stability of operating point at the maximum power point. PV has several maximum power points (MPP) under partial shade situations, which is characterized by its non-linear features. As a result, it is challenging to find actual MPP. While tracking and collecting the maximum power from PV, the cuckoo search optimization (CSO) technique developed by biological intelligence is used in this article. The cuckoo search (CS) has several advantages, including a short tuning process that is efficient as well as fast convergence. The step-up converter steps up the voltage. In order to steady the converter, the counter variable is employed to provide delay. Resistive load is present. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

This research aims to implement an already developed algorithm to obtain the maximum power transfer of a solar generation field based on a dynamic approach. The study addresses the sizing of the load to be supplied, which is a residential building. On the other hand, it also considers the field sizing as a function of the load and the operating characteristics of the selected inverter. The irradiance data correspond to the hourly record of a station that is part of the network of meteorological stations in Quito. Quito was chosen as the location for this research due to the optimization algorithm's practical application and the availability of experimental equipment. The demand sizing is based on the regulations of the distribution company with jurisdiction in the area, which makes it a suitable test bed for the algorithm. The optimization algorithm is developed using Python (version 3.9), and the analysis of the behavior of the solar panels is performed by dynamic modeling using the Vensim software (version 10.1.2). Finally, comparative results are presented between using and not using the investigated circuit and algorithm in the photovoltaic system, obtaining an improvement in the generation over a system without the use of these improvements, validating these results by implementing them in a test system, obtaining ranges higher than 10% of the initially generated power. [ABSTRACT FROM AUTHOR]

A lot of previous studies have shown a great interest in connecting photovoltaic (PV) arrays to electric power grids. A Maximum Power Point Tracker (MPPT) controlled 100 kW PV array to boost its power that feeds the grid with the help of a three-level inverter. This paper investigated the effects of open-circuit faults of three-level inverter IGBT switches on the performance of the PV array system. This paper proposes a new diagnostic method called the Double Threshold-Trigonometric Coordinates (DT-TC) that relies on the double threshold technique and the trigonometric coordinates, respectively, for early detection and the precise location of open-circuit faults. To the best knowledge of the authors, two simultaneous open-circuit faults were not considered in the previous studies. The obtained simulation results prove the great success of the proposed diagnosis method even under noticeable changes in irradiation, temperature, and the number of PV array connected in series and in parallel. In this study, the reconfigurable fault-tolerant inverter was included to ensure the perfect continuity of the PV array system and its recovery in an extremely very short time up to 7 ms. [ABSTRACT FROM AUTHOR]

The fuel cell stack power mismatch problem limits the large-scale application of fuel cells. In order to guarantee the normal operation of the series fuel cell bank under fault condition, this paper proposes a multi-port DC converter for fuel cell distributed maximum power tracking based on the coupled inductor principle. The converter is based on the voltage equalization method and consists of a series fuel cell bank with a diode clamping circuit and a Boost converter in sequential cascade. In contrast to the degraded usage method of fuel cell stacks under fault conditions, the voltage equalization strategy does not affect the lifetime of non-faulty fuel cell units, so the overall system is more efficient. The proposed converter combines high output voltage gain and cell voltage equalization due to the use of only one switching tube. Simulation models are used to verify the feasibility and advancement of the proposed converter. [ABSTRACT FROM AUTHOR]

This paper presents a novel maximum power point tracking control for a stand-alone photovoltaic (PV) system based on a robust polynomial static output feedback control law subject to input saturation. In detail, a DC/DC boost converter is used to regulate the load and extract the maximum power from the photovoltaic panel. First, a polynomial fuzzy model is used to represent the photovoltaic system. Then, as this control method is based on a reference model, a regression plane is used to generate the desired trajectory representing the optimal dynamics where the PV system supplies maximum power. Then, in order to reduce the number of required sensors, a polynomial output feedback controller was developed, in which the problem of converter performance degradation resulting from duty cycle saturation was avoided by using a saturated control approach. The controller gains have been obtained by solving a sum-of-squares optimization problem, where the H ∞ performance criterion is applied to guarantee the stability of the closed-loop system while achieving an optimal rejection level of external disturbances. To evaluate the performance of the suggested controller, a number of simulations and comparisons were carried out in MATLAB/Simulink environment and under various scenarios of weather conditions. [ABSTRACT FROM AUTHOR]

One of the most known and usable renewable energy is photovoltaic solar energy. Its properties such as recursivity, cleanness, limitlessness, and reliability make it the most useful source of energy. Some undesirable natural (irradiance and temperature) variation affects the photovoltaic solar modules' characteristics. However, photovoltaic PV solar power needs to be monitored accurately, effectively, and efficiently in order to keep the global maximum power point (GMPP) steady. Therefore, this paper presents a modified multi-stepped constant current based on the gray wolf optimization (MMSCC-GWO) algorithm for photovoltaic systems. The proposed technique utilizes a multi-step constant current to the pack of wolves by monitoring the inside and the outside step of the hierarchical wolves. This proposed (MMSCC-GWO) algorithm accurately tracks the maximum power point from the photovoltaic systems under different partial shading conditions (PSCs). A comparative result with other conventional methods is investigated and the simulation results carried out in the MATLAB/Simulink environment reveal that the proposed approach outperforms the existing techniques in terms of efficiency, tracking time, and accuracy. [ABSTRACT FROM AUTHOR]

In order to enhance the support capability of photovoltaic inverters for new energy microgrid systems, grid-forming control technology has attracted widespread attention, with Virtual Synchronous Generator (VSG) emerging as a research frontier. This paper integrates hybrid energy storage systems with photovoltaic generation to provide stable voltage support and power compensation for the system. In addition, leveraging the variability of the virtual parameters J and D in traditional VSGs, an adaptive grid-forming photovoltaic inverter control strategy based on fuzzy algorithm is proposed. Furthermore, to reduce the frequency deviation caused by load transients during islanding operation, an adaptive droop coefficient based on frequency power limits is introduced. Finally, simulations are conducted under grid-connected and islanded conditions to compare three control methods: fixed parameter control, traditional adaptive control, and the proposed adaptive with fuzzy control. The results demonstrate that integrating adaptive droop coefficients with fuzzy control can effectively improve the frequency stability and dynamic response capability of microgrids. [ABSTRACT FROM AUTHOR]

Shading in photovoltaic (PV) systems can reduce power output, cause hot spots, efficiency losses, maintenance needs, and impact economic returns. Traditional shading solutions for PV systems are complex and expensive, requiring advanced sensors, imaging technologies, and complicated algorithms. This study presents a cost-effective, simple method that does not rely on sensors, cameras, or complex systems. It involves a site assessment to identify shading sources and precise shadow length calculations using astronomical equations. By combining site analysis with tracking mechanisms, the system proactively avoids shading with minimal energy loss, only 2.7% of the generated energy. Unlike other techniques that react to shading, this approach focuses on predicting and preventing shading, ensuring optimal performance. A prototype of the system demonstrated efficient sun tracking and shading avoidance. This innovative approach improves PV efficiency, reduces costs, and promotes adoption due to its simplicity and scalability. [ABSTRACT FROM AUTHOR]

This paper presents the design of a digital twin for a 6U CubeSat electrical power system, including the solar arrays, solar array regulators, battery, power distribution unit, and load subsystems. The digital twin is validated by comparing its real-time outputs with those of the physical system. Experimental tests confirm its feasibility, showing that the digital twin's real-time outputs closely match those of the physical system. Additionally, the digital twin can be used for control-hardware-in-the-loop and power-hardware-in-the-loop tests, allowing the real-time integration of simulated subsystems with hardware. This capability facilitates testing of new subsystems and optimization during the project's development phases. Additionally, to demonstrate the advanced capabilities of this model, the digital twin is used to simulate the CubeSat electrical power system behavior in real time throughout a complete orbital cycle in low Earth orbit conditions. This simulation provides valuable insights into the CubeSat operation by capturing the transient and steady-state responses of the EPS components under real orbital conditions. The results obtained indicate that the digital twin significantly enhances the testing and optimization process of new subsystems during the development phases of the project. Moreover, the capabilities of the digital twin can be further augmented by incorporating real-time telemetry data from the CubeSat, resulting in a highly accurate replication of the satellite's in-orbit behavior. This approach is crucial for identifying and diagnosing failures or malfunctions in the electrical power system, ensuring the robust and reliable operation of the CubeSat. [ABSTRACT FROM AUTHOR]

The "double carbon" policy promotes an increasing ratio of new energy installation and generation capacity annually, reducing system inertia and rendering the grid-forming (GFM) power grid increasingly susceptible to frequency fluctuations and other issues. This paper designs a power–frequency controller for grid-forming distributed photovoltaic systems by integrating LADRC and VSG control, aiming to enhance system robustness and stability under disturbances or faults. Furthermore, this study introduces the particle swarm optimization (PSO) algorithm based on LADRC principles to optimize LADRC, thereby improving the robustness and stability of grid-forming distributed photovoltaic systems to a certain extent. The feasibility and effectiveness of these two proposed control strategies are analyzed and validated separately using the MATLAB/Simulink R2022a simulation platform. [ABSTRACT FROM AUTHOR]

Integrating photovoltaic systems (PV) into the grid has garnered significant attention due to increasing interest in renewable energy sources. Maximizing the PV systems output power is crucial for improving energy efficiency and reducing operating costs. This paper presents a comparative analysis of two different techniques of maximum power point tracking (MPPT): perturb and observe (P&O) and artificial neural network (ANN) MPPT, focusing on their application in grid-connected PV systems. The paper evaluates their performance under various operating conditions, including changes in irradiance and temperature, that are discussed in three cases. The comparative analysis includes metrics such as voltage regulation and power loss. MATLAB Simulink is utilized to implement P&O and ANN MPPT methods, which include a PV cell connected to an MPPT-controlled boost converter. The simulation demonstrates the power loss of the PV model as well as the voltage regulation in the three cases for the two methods. The results obtained in simulation and implementations show that the ANN method outperforms the P&O in the three cases discussed in terms of power loss, voltage regulation, and efficiency. The results also show that the change in output power from PV is noticeable when compared to changes in radiation, while the change is slight when temperatures change. [ABSTRACT FROM AUTHOR]

Partial shading (PS) is a common issue in photovoltaic systems (PVs), and it can significantly reduce the system's output power. This paper presents the advanced particle swarm optimization (APSO) algorithm. APSO is designed to alleviate the challenges posed by PS in PVs in from where of effectiveness and stability speed so that it works to achieve and maintain the global maximum power point (GMPP) under PS conditions. It leverages persistent variables to store and track system states and iterations; it also includes checks to ensure that the duty cycle remains within specified bounds facilitating more effective optimization. Additionally, APSO optimizes solar panel duty cycles and velocities to converge toward an optimal solution to improve overall power generation efficiency and settling time. The results evaluation involves testing the performance of photovoltaic panels under three different shading scenarios and comparative analysis against recent Heuristic-optimization-based GMPP techniques, this study and comparative analyses demonstrate APSO's effectiveness and superiority in terms of high efficiency that reaches 99.85% and fast settling time of GMPP at less than 0.01 second across all test cases. APSO presents a promising solution for maximizing PV power output in the presence of partial shading. [ABSTRACT FROM AUTHOR]

The system performance of grid-connected photovoltaic (PV) has a serious impact on the grid stability. To improve the control performance and shorten the convergence time, a predefined-time controller based on backstepping technology and dynamic surface control is formulated for the inverter in the grid-connected photovoltaic. The time-varying tuning functions are introduced into state-tracking errors to realize the predefined-time control effect. To address the “computational explosion problem” in the design process of backstepping control, dynamic surface control is adopted to avoid the analytical calculations of virtual control. The disturbances of the PV system are estimated and compensated by adaptive laws. The control parameters are chosen and the global stability of the closed-loop is ensured by Lyapunov conditions. Simulation results confirm the effectiveness of the proposed controller and ensure the predefined time control in the photovoltaic inverter. [ABSTRACT FROM AUTHOR]

This research article presents a novel approach to Maximum Power Point Tracking (MPPT) for photovoltaic systems, employing a modified bacterial foraging algorithm with dynamically adjustable mutation rates. This method is specifically tailored to address the challenges presented by partial shading conditions, ensuring efficient and rapid tracking of the MPP while preventing local optima entrapment. To evaluate the performance of this innovative technique, a comparative analysis is conducted against the original bacterial foraging algorithm and the grey wolf optimization algorithm, both commonly employed in MPPT applications. The modified algorithm incorporates a unique strategy that dynamically adapts mutation rates based on the algorithm's convergence behavior, enhancing the tracking accuracy from 81.31% to 89.39%. To validate the effectiveness of the proposed technique, extensive simulations are carried out using MATLAB Simulink, considering various partial shading scenarios commonly encountered in practical photovoltaic applications. It's worth noting that the shading scenario data were extracted from the NASA Worldwide Prediction of Energy website, specifically from the city of Ain El Ibel Djelfa irradiance records. The simulation results unequivocally demonstrate the superiority of the modified bacterial foraging MPPT technique over both algorithms in terms of tracking efficiency (0.4s to 0.9s) and robustness under partial shading conditions. The findings of this research offer valuable insights into the potential advantages of employing a modified bacterial foraging approach for MPPT applications. This innovative techniques with its ability significantly enhance its performance in real-world scenarios involving partial shading, positioning it as a promising choice for optimizing photovoltaic system efficiency and power output. [ABSTRACT FROM AUTHOR]

The performance of photovoltaic (PV) modules is affected by environmental factors such as irradiance and temperature, which can lead to a decrease in output performance or even damage. This study proposes an improved formula for calculating the real maximum power of PV modules by analysing the influence of irradiance and temperature. A simulation model is developed using PLECS software to simulate the global maximum power of PV modules under different environmental conditions and the results are compared with the calculated real maximum power. A power optimization scheme for PV modules is then proposed based on current equalization and constant voltage control. This scheme employs a single-switch multi-winding forward–flyback converter to equalize the mismatched currents between cell strings, thereby enhancing the output performance. Traditional proportional–integral controllers are utilized to achieve constant voltage control and obtain the real maximum power of PV modules. Simulation models are built in the PLECS simulation platform to evaluate the performance of a global maximum power point tracking scheme based on the traditional perturb-and-observe (TPO) algorithm with current equalization, a segment perturb-and-observe algorithm without current equalization, and the proposed power optimization scheme. The simulation results demonstrate that the proposed constant voltage control has greater efficiency than the TPO algorithm. The proposed scheme achieves a significant improvement in efficiency, with a 27.87% increase compared with the segment perturb-and-observe algorithm without current equalization. [ABSTRACT FROM AUTHOR]

Electricity production from photovoltaic (PV) panels is maximized when the operating point is located at the maximum power point thanks to dedicated controllers. These controllers are driven to track the maximum power by using various algorithms within distributed or centralized architectures accounting for factors such as partial irradiation and temperature changes. The effect of irradiance on the optimal panel voltage is weak or even negligible, while it is strong and quasi-linear-dependent on temperature. Based on this observation, this article introduces a straightforward method for tracking the maximum power of a PV panel by using an optimizer, focusing solely on its temperature response as an input variable. The proposed approach hinges on linearizing the relationship between panel temperature and operating voltage. This relationship enables the estimation of the maximum power point through temperature measurement alone. Thus, after determination of the linear temperature coefficient of the voltage requiring only the knowledge of two optimal voltages at different temperatures, for example from the datasheet of the panel, the power tracking involves only one temperature sensor placed on the panel alongside a voltage sensor for regulation. The principle, modelling, and validation post-panel ageing of the method are detailed in this paper. Simulation, conducted using real experimental irradiation and temperature data, attests to the effectiveness of the control. Results indicate an average effectiveness of the method of >99.1% in tracking the maximum power, with the panel generating 2.33 kWh out of a possible 2.35 kWh. This performance is comparable to that of tracking devices employing more complex algorithms. The simplicity and efficiency of the method make it a promising option for maximizing the power production at low cost from PV systems in small or residential, on- or off-grid connected applications. [ABSTRACT FROM AUTHOR]

Under partial shading conditions (PSCs), photovoltaic (PV) systems exhibit non-uniform P-V characteristic curves with multiple local maximum power points (LMPP). Consequently, conventional maximum power point tracking (MPPT) algorithms often fail to accurately track the global maximum power point (GMPP), becoming trapped at local maxima and resulting in power loss. This paper introduces a novel MPPT controller based on the weighted mean of vectors (INFO) optimization algorithm to address these challenges. The INFO algorithm integrates three key operators: updated law, vector combination, and local search, offering advantages such as rapid convergence, high efficiency, and optimized power extraction under both PSCs and temperature changes (TCs). To validate the effectiveness of the INFO algorithm under PSCs and TCs, a partial shading platform featuring three PV modules with identical characteristics but varying radiation levels is utilized. Simulation studies compare the INFO algorithm against traditional perturb and observe (P&O), particle swarm optimization (PSO), and overall distribution particle swarm optimization (OD-PSO) algorithms in terms of MPPT performance. Results demonstrate that the INFO algorithm achieves a 60% reduction in tracking time and a 0.02% increase in MPPT controller efficiency compared to these methods. Overall, the proposed INFO algorithm achieves a balanced approach between exploration and exploitation stages, enhancing robustness, efficiency, and reducing tracking time in MPPT applications. [ABSTRACT FROM AUTHOR]

This paper presents an innovative AI-based method for autonomous inspection, designed to enhance energy production efficiency by optimizing cleaning strategies for soiled photovoltaic panels, using advanced artificial intelligence algorithms to analyze panel conditions and environmental factors in real-time, allowing for targeted cleaning interventions. Based on the advanced YOLOv8 deep learning algorithm and computer vision approach, the proposed method offers distinct advantages in real-time detection and classification of various types of soiling and dust accumulation compared on solar panels to traditional methods, and underwent satisfactory testing across diverse scenarios. The NVIDIA Jetson Nano, the Raspberry Pi4 embedded devices, and the Raspberry Pi4 combined with NCS2 accelerator are used for implementing our approach. A comparison aims to provide a detailed exploration of the most suitable embedded platform for deploying our advanced system was discussed. This comparison considers processing speed and accuracy, energy consumption, and overall performance in executing the computationally intensive tasks. The results demonstrate that our model achieves high accuracy in detecting soiling and enhancing the model's detection speed. With an average precision of 99.5%, this approach ensures accurate fault identification, underscoring the effectiveness of computer vision using deep learning algorithms for detection tasks across a wide range of scenarios. [ABSTRACT FROM AUTHOR]

To maintain a dependable and sustainable power supply in a microgrid system, it is crucial to combine renewable energy sources with hybrid energy backup. To achieve maximum output power from solar source, a high gain DC-DC boost converter is managed using the dual Kalman filter based perturb and observe approach. A sliding mode current controller at a three phase inverter with an LC filter is intended to follow an undisturbed reference voltage. To effectively manage the power flow and optimize the utilization of available resources, a robust power management algorithm is required. The novel power management algorithm for a solo operated renewable distribution generation unit with hybrid energy backup in a microgrid is introduced. The algorithm aims to dynamically allocate power among various sources, storage systems, and loads, considering their characteristics and the overall system constraints. The algorithm utilizes sliding mode control techniques to regulate the current flow from the renewable generator and effectively manage the power allocation among different energy sources, storage systems, and loads. [ABSTRACT FROM AUTHOR]

This paper explores the challenges associated with the control of a two-stage three-phase electrical grid connected to a photovoltaic (PV) system. The objectives encompass: i) maximizing the available PV power, ii) controlling the DC-link voltage to a predetermined setpoint, and iii) considering that power quality has become an important measure in a distribution electrical network where different loads are connected, the third objective will mainly focus on ensuring power factor correction (PFC). To achieve these objectives, two loops of nonlinear controller are developed. In the outer loop, the duty cycle of a boost converter is controlled using a hybrid technique of backstepping technique and the perturb & observe (P&O) algorithm. In addition, the inner loop employs a hybrid automaton approach to tackle the challenges of a three-phase shunt active power filter (SAPF). The results have been verified through numerical simulation using MATLAB/Simulink power systems environment. [ABSTRACT FROM AUTHOR]

The paper presents a normalized, varying step size-based least mean square-based control for a standalone microgrid and Ant Lion optimization-based maximum power point tracking. This modified version of the incremental conductance algorithm addresses issues like slow dynamic response, fixed step size issues, and steady state oscillations. Comparative analysis with well-known techniques shows that Ant Lion optimizes the tracking of maximum power points more accurately, with fewer oscillations and increased efficiency. Power management and power quality are maintained through proposed adaptive voltage source converter control. The proposed voltage control shows better performance under various dynamic and steady conditions. [ABSTRACT FROM AUTHOR]

An improved DC-Link voltage controller (IPOVR), which is built upon the perturb & observe-based voltage regulator (POVR), is proposed in this paper. The aim is to achieve the best balance between tracking time and steady-state oscillation while maintaining good tracking accuracy. This is achieved by implementing a dynamic step-size (∆D) changing strategy and operating region detection technique. Quick tracking time is produced with large initial ∆D, and low steady-state oscillation is obtained through continuous ∆D reduction. Additionally, through the operating region determination technique, the IPOVR operates over a much wider range, typically with lower PV current (IPV), which helps in reducing the losses of the system. It is found that the IPOVR has the lowest steady-state ripple and the highest tracking accuracy, which are averaged at 0.44% and 99.98%, respectively. In terms of tracking time, the IPOVR is averaged at 0.15 s, which is only 0.07 s slower compared to the POVR with high ∆D but is 0.19 s faster than the POVR with low ∆D. Furthermore, the IPOVR recorded higher efficiencies under all tested conditions compared to the POVR. The IPOVR is also demonstrated to be working decently when being connected to a multilevel inverter to generate AC power. [ABSTRACT FROM AUTHOR]

Parameter identification of solar photovoltaic (PV) cells is crucial for the PV system modeling. However, finding optimal parameters of PV models is an intractable problem due to the highly nonlinear characteristics between currents and voltages in different environments. To address this problem, whale optimization algorithm (WOA)-based meta-heuristic algorithm has turned out to be a feasible and effective approach. As a highly promising optimization algorithm, different enhanced WOA variants have been proposed. Nevertheless, there has been no comparative study of WOA and its variants for parameter identification of PV models so far. To further investigate and analyze the performance of WOA in the studied problem, this work applied and compared WOA and ten enhanced WOA variants for identifying five PV model parameters. Different evaluation indices including solution accuracy, search robustness, and convergence curve were employed to reveal their performance variation. Based on the simulation results, a multi-model statistical analysis with the Friedman test at a confidence level 0.05 was conducted to rank all algorithms. EWOA that hybridizes the sorting-based differential mutation operator and the Lévy flight strategy ranked first and its performance was further verified. Besides, according to the simulation results, possible effective improvement directions for WOA in tackling this intractable problem are concluded to guide future work. [ABSTRACT FROM AUTHOR]