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Photovoltaic (PV) research is rapidly growing, and the need for controlled environments to validate new MPPT controllers is becoming increasingly important. Currently, researchers face several challenges in testing MPPT algorithms due to the unpredictable nature of solar PV power generation. In this paper, we propose a new photovoltaic emulator (PVE) that could replace solar panels and ensure a highly controllable environment suitable for testing photovoltaic (PV) systems. In this PVE, the complex nonlinear equations of the PV cell/module are fast computed and resolved by a new linearization technique which involves the systematic breakdown of the current-voltage (I-V) curve of the PV into twelve linear segments. Based on input environmental conditions, an artificial neural network (ANN) was constructed to assist the linearization process by predicting the current-voltage boundary coordinates of these segments. Using simple linear equations, with the segment boundary coordinates, a reference voltage was generated for the PVE. A nonlinear backstepping controller was designed to exploit the reference voltage and stabilize the power conversion stage (PCS). The PVE was optimized using particle swarm optimization (PSO). Several tests have shown that the proposed nonlinear controller provides better dynamic and robust performance than the PI controller, the most reputable and recurrent control method in the area of PVE. The PVE was coupled with a recently proposed integral backstepping MPPT controller and analyzed under several dynamic conditions, including the MPPT test specified by EN 50530. It was found that the accuracy of the proposed PVE measured by its relative error is less than 0.5%, with an MPPT efficiency of greater than 99.5%. The attractive results achieved by this PVE make it especially suitable for simulating and validating MPPT controllers.

This paper introduces a dual-objective control framework for standalone photovoltaic (PV) systems that uniquely integrates maximum power point tracking (MPPT) with precise DC load voltage regulation. Unlike existing approaches that concentrate almost exclusively on power optimization, the proposed system simultaneously ensures both efficient energy harvesting and robust output voltage stability under fluctuating climatic and load conditions. The novelty lies in the design of a reference voltage estimator (RVE)—a sensorless MPPT mechanism that fuses explicit irradiance estimation with a radial basis function neural network—combined with two Lyapunov-based nonlinear controllers supervising a two-stage Boost–Buck converter architecture. This coordinated design enables accurate real-time MPP prediction, finite-time load-side voltage stabilization, and decoupled handling of PV-side and load-side dynamics. The system is implemented through microcontroller-in-the-loop experimentation and validated under diverse and extreme disturbances. Results demonstrate exceptionally low mean absolute errors (0.1253 V for MPPT and 0.0793 V for load regulation) with rapid recovery times (< 3 ms), confirming superior efficiency, reliability, and resilience. This work bridges a critical research gap by experimentally proving that stable voltage regulation can be unified with optimal power harvesting in a single architecture, offering a deployable solution for standalone PV systems in real-world conditions.

Maximum power point tracking (MPPT) is becoming more and more important in the optimization of photovoltaic systems. Several MPPT algorithms and nonlinear controllers have been developed for improving the energy yield of PV systems. On the one hand, most of the conventional algorithms such as the incremental conductance (INC) demonstrate a good affinity for the maximum power point (MPP) but often fail to ensure acceptable stability and robustness of the PV system against fast-changing operating conditions. On the other hand, the MPPT nonlinear controllers can palliate the robust limitations of the algorithms. However, most of these controllers rely on expensive solar irradiance measurement systems or complex and relatively less accurate methods to seek the maximum power voltage. In this paper, we propose a new hybrid MPPT based on the incremental conductance algorithm and the integral backstepping controller. The hybrid scheme exploits the benefits of the INC algorithm in seeking the maximum power voltage and feeds a nonlinear integral backstepping controller whose stability was ensured by the Lyapunov theory. Therefore, in terms of characteristics, the overall system is a blend of the MPP-seeking potential of the INC and the nonlinear and robust potentials of the integral backstepping controller (IBSC). It was noted that the hybrid system successfully palliates the conventional limitations of the isolated INC and relieves the PV system from the expensive burden of solar irradiance measurement. The proposed hybrid system increased the operational efficiency of the PV system to 99.94% and was found better than the INC MPPT algorithm and 8 other recently published MPPT methods. An extended validation under experimental environmental conditions showed that the hybrid system is approximately four times faster than the INC in tracking the maximum power with better energy yield than the latter.

Gray Wolf Optimization (GWO), inspired by the social hierarchy and cooperative hunting behavior of gray wolves, is a widely used metaheuristic algorithm for solving complex optimization problems in various domains, including engineering design, image processing, and machine learning. However, standard GWO can suffer from premature convergence and sensitivity to parameter settings. To address these limitations, this paper introduces the Hierarchical Multi-Step Gray Wolf Optimization (HMS-GWO) algorithm. HMS-GWO incorporates a novel hierarchical decision-making framework that more closely mimics the observed hierarchical behavior of wolf packs, enabling each wolf type (Alpha, Beta, Delta, and Omega) to execute a structured multi-step search process. This hierarchical approach enhances exploration and exploitation, improves solution diversity, and prevents stagnation. The performance of HMS-GWO is evaluated on a benchmark suite of 23 functions, showing a 99% accuracy, with a computational time of 3 s and a stability score of 0.9. Compared to other advanced optimization techniques such as standard GA, PSO, MMSCC-GWO, WCA, and CCS-GWO, HMS-GWO demonstrates significantly better performance, including faster convergence and improved solution accuracy. While standard GWO suffers from premature convergence, HMS-GWO mitigates this issue by employing a multi-step search process and better solution diversity. These results confirm that HMS-GWO outperforms other techniques in terms of both convergence speed and solution quality, making it a promising approach for solving complex optimization problems across various domains with enhanced robustness and efficiency.

This article investigates an inventive methodology for precisely and efficiently controlling photovoltaic emulating (PVE) prototypes, which are employed in the assessment of solar systems. A modification to the Shift controller (SC), which is regarded as a leading PVE controller, is proposed. In addition to efficiency and accuracy, the novel controller places a high emphasis on improving transient performance. The novel piecewise linear-logarithmic adaptation utilized by the Modified-Shift controller (M-SC) enables the controller to linearly adapt to the load burden within a specified operating range. At reduced load resistances, the transient sped of the PVE can be increased through the implementation of this scheme. An exceedingly short settling time of the PVE is ensured by a logarithmic modification of the control action beyond the critical point. In order to analyze the M-SC in the context of PVE control, numerical investigations implemented in MATLAB/Simulink (Version: Simulink 10.4, URL: https://in.mathworks.com/products/simulink.html ) were utilized. To assess the effectiveness of the suggested PVE, three benchmarking profiles are presented: eight scenarios involving irradiance/PVE load, continuously varying irradiance/temperature, and rapidly changing loads. These profiles include metrics such as settling time, efficiency, Integral of Absolute Error (IAE), and percentage error (epve). As suggested, the M-SC attains an approximate twofold increase in speed over the conventional SC, according to the findings. This is substantiated by an efficiency increase of 2.2%, an expeditiousness enhancement of 5.65%, and an IAE rise of 5.65%. Based on the results of this research, the new M-SC enables the PVE to experience perpetual dynamic operation enhancement, making it highly suitable for evaluating solar systems in ever-changing environments.

Partial shading conditions (PSC) in photovoltaic (PV) systems degrade energy harvest by generating multi-peak power-voltage (P–V) curves, trapping conventional maximum power point tracking (MPPT) algorithms at local maxima. This paper presents a Multi-Peak to Single-Peak Conversion (MSMPPT) framework that enables conventional algorithms like Perturb & Observe (P&O) and Incremental Conductance (INC) to reliably track the global maximum power point (GMPP) under PSC without structural modifications. The framework operates via two stages: dynamic estimation of optimal voltage boundaries to shrink the GMPP search space to under 10% of the original P–V range, and active voltage regulation to enforce operation within this zone, effectively transforming the multi-peak curve into a single-peak profile. The proposed MSMPP-P&O and MSMPP-INC algorithms achieve 50% faster tracking (64 ms vs. 122 ms for P&O) and near-perfect steady-state efficiency under static shading, reducing power losses below 2%. In dynamic shading scenarios with abrupt irradiance shifts, MSMPPT maintains robustness with less than 1.5 W net loss, outperforming conventional methods that incur over 30 W of power losses. By eliminating oscillations and hotspot risks through voltage regulation, the framework retains algorithmic simplicity while enhancing performance under complex shading scenarios. Validated across benchmark shading profiles, MSMPPT demonstrates fidelity without requiring additional hardware or complex optimizers. This innovation bridges the gap between conventional MPPT simplicity and partial shading resilience, offering a cost-effective, scalable solution to boost PV system reliability in shading environments.

This paper introduces an innovative, adaptive Fractional Open-Circuit Voltage (FOCV) algorithm combined with a robust Improved Model Reference Adaptive Controller (IMRAC) for Maximum Power Point Tracking (MPPT) in standalone photovoltaic (PV) systems. The proposed two-stage control strategy enhances energy efficiency, simplifies system operation, and addresses limitations in conventional MPPT methods, such as slow convergence, high oscillations, and susceptibility to environmental fluctuations. The first stage dynamically estimates the Maximum Power Point (MPP) voltage using a novel adaptive FOCV method, which eliminates the need for irradiance sensors or physical disconnection of PV modules. This stage incorporates a real-time adjustment of the kv factor based on variations in PV power, ensuring precise voltage estimation. In the second stage, the IMRAC controller ensures accurate tracking of the MPP by adapting swiftly to changes in irradiance and temperature, while minimizing ripple and power loss. Validation of the proposed system was carried out using Processor-in-the-Loop (PIL) testing on an Arduino Due microcontroller, showcasing real-world applicability. Comparative analysis with state-of-the-art MPPT controllers, including P&O-PI, InC-SMC, FLC, and VS P&O Backstepping, demonstrates superior tracking efficiency exceeding 99.49% under EN 50,530 standard test conditions. The system also maintains exceptional performance with minimal efficiency loss across a wide range of temperature and irradiance variations. By combining simplicity, robustness, and sustainability, this work establishes a cutting-edge solution for standalone PV systems, paving the way for more efficient and reliable renewable energy applications.

This study introduces an advanced mathematical methodology for predicting energy generation and consumption based on temperature variations in regions with diverse climatic conditions and increasing energy demands. Using a comprehensive dataset of monthly energy production, consumption, and temperature readings spanning ten years (2010–2020), we applied polynomial, sinusoidal, and hybrid modeling techniques to capture the non-linear and cyclical relationships between temperature and energy metrics. The hybrid model, which combines sinusoidal and polynomial functions, achieved an accuracy of 79.15% in estimating energy consumption using temperature as a predictor variable. This model effectively captures the seasonal and non-linear consumption patterns, demonstrating a significant improvement over conventional models. In contrast, the polynomial model for energy production, while yielding partial accuracy (R² = 0.65), highlights the need for more advanced techniques to fully capture the temperature-dependent nature of energy production. The results indicate that temperature variations significantly affect energy consumption, with higher temperatures driving increased energy demand for cooling, while lower temperatures affect production efficiency, particularly in systems like hydropower. These findings underscore the necessity for integrating sophisticated models into energy planning to ensure resilience in energy systems amidst climate variability. The study offers critical insights for policymakers to optimize energy generation and distribution in response to changing climatic conditions.

This voltage imbalance in four-wire, three-phase distribution networks gives rise to negative-sequence and zero-sequence voltage components which increases the total apparent power received from the network. This also increases the energy losses from the network. Traditional methods employed for load compensation provide partial fixes at the local area without any form of system-wide solution. This work presents a new decentralized control strategy for the inverter of a photovoltaic-based three-phase power source (DPS) aimed at instantaneously correcting phase voltage imbalances. The method does not require load current measurement because it depends entirely on real-time voltage measurements at the point of common coupling (PCC). The capability to mitigate the unbalance depends on the available power of the DPS. To test how effective the proposed method is, simulations have been conducted using MATLAB/SIMULINK on a distribution network with a four-leg inverter connected to a line with cascading single and three-phase loads, where a four-leg inverter enables independent phase control and mitigation of neutral current disturbances. The results show that this control enables the comparison of balancing for three-phase powers with a 96.4% improvement. The phase-to-phase voltage deviation was also reduced by around 8 V (3.6% of nominal voltage). Furthermore, the total harmonic distortion (THD) of the output current from the inverter did not rise about 3.75%, hence improving the power quality. Its real-time applicability in decentralized renewable energy integration is possible due to the method’s effectiveness in reducing voltage imbalances even when network conditions are extremely distorted.

The design of a power electronic interface for high voltage difference DC buses is a key aspect in DC microgrid applications. A multi-port non isolated interleaved high-voltage gain bidirectional converter, which facilitates bidirectional power transfer and islanded operation in a DC microgrid, is presented in this paper. The forward high-voltage transfer ratio is achieved using a voltage multiplier circuit, and the high-gain step-down power conversion is performed using a resonant power module. A novel power transfer selection algorithm is proposed to control power flow among the interfaces of the RES, ESS, and DC grid converters, which utilizes the net power difference as the basis for switching the converter. The proposed converter is simulated for a 24 V PV source, 12 V battery, and 400 V DC grid interface using MATLAB/SIMULINK. A 200 W hardware prototype is implemented. The simulation results for voltages, currents, and power flow among RES, ESS, and microgrid DC bus proved an excellent voltage regulation, efficient power conversion, and a feasible duty cycle range with high voltage gain. These observations are validated through equivalent experimental results. A comparison is made regarding achieved gain, component sizing, achievable power transfer modes, efficiency, and control complexity with existing converters for DC microgrid applications. The presented topology proved to be a better interface with multiple-mode support with high efficiency.