Search Results (181)

This work investigates the application of artificial intelligence techniques for optimizing photovoltaic systems using maximum power point tracking (MPPT) algorithms. Simulation models were developed in MATLAB/Simulink (Version 2024), incorporating conventional and intelligent control strategies such as fuzzy logic, genetic algorithms, neural networks, and Deep Reinforcement Learning. A DC/DC buck converter was designed and tested under various irradiance and temperature profiles, including scenarios with partial shading conditions. The performance of the implemented MPPT algorithms was evaluated using such metrics as Mean Absolute Error (MAE), Integral Absolute Error (IAE), mean squared error (MSE), Integral Squared Error (ISE), efficiency, and convergence time. The results highlight that AI-based methods, particularly neural networks and Deep Q-Network agents, outperform traditional approaches, especially in non-uniform operating conditions. These findings demonstrate the potential of intelligent controllers to enhance the energy harvesting capability of photovoltaic systems. Full article

This paper presents an adaptive Maximum Power Point Tracking (MPPT) strategy for grid-connected photovoltaic (PV) systems that uses an Adaptive Neuro-Fuzzy Inference System (ANFIS) optimized by Particle Swarm Optimization (PSO) to enhance energy extraction efficiency under diverse environmental conditions. The proposed ANFIS-PSO-based MPPT controller performs dynamic adjustment Pulse Width Modulation (PWM) switching to minimize Total Harmonic Distortion (THD); this will ensure rapid convergence to the maximum power point (MPP). Unlike conventional Perturb and Observe (P&O) and Incremental Conductance (INC) methods, which struggle with tracking delays and local maxima in partial shading scenarios, the proposed approach efficiently identifies the Global Maximum Power Point (GMPP), improving energy harvesting capabilities. Simulation results in MATLAB/Simulink R2023a demonstrate that under stable irradiance conditions (1000 W/m², 25 °C), the controller was able to achieve an MPPT efficiency of 99.2%, with THD reduced to 2.1%, ensuring grid compliance with IEEE 519 standards. In dynamic irradiance conditions, where sunlight varies linearly between 200 W/m² and 1000 W/m², the controller maintains an MPPT efficiency of 98.7%, with a response time of less than 200 ms, outperforming traditional MPPT algorithms. In the partial shading case, the proposed method effectively avoids local power maxima and successfully tracks the Global Maximum Power Point (GMPP), resulting in a power output of 138 W. In contrast, conventional techniques such as P&O and INC typically fail to escape local maxima under similar conditions, leading to significantly lower power output, often falling well below the true GMPP. This performance disparity underscores the superior tracking capability of the proposed ANFIS-PSO approach in complex irradiance scenarios, where traditional algorithms exhibit substantial energy loss due to their limited global search behavior. The novelty of this work lies in the integration of ANFIS with PSO optimization, enabling an intelligent self-adaptive MPPT strategy that enhances both tracking speed and accuracy while maintaining low computational complexity. This hybrid approach ensures real-time adaptation to environmental fluctuations, making it an optimal solution for grid-connected PV systems requiring high power quality and stability. The proposed controller significantly improves energy harvesting efficiency, minimizes grid disturbances, and enhances overall system robustness, demonstrating its potential for next-generation smart PV systems. Full article

This paper aims to maximize the performance of photovoltaic generators under varying atmospheric conditions by employing an improved variable-step current perturbation Perturb and Observe (IVSCP-PO) MPPT controller. The proposed approach overcomes the limitations of traditional controllers and significantly enhances tracking efficiency. The IVSCP-PO controller locates the maximum power point (MPP) using current perturbation instead of voltage perturbation and employs a variable step iteration based on input variables such as power, voltage, and current for better adjustment of the boost converter’s duty ratio. Comprehensive simulations demonstrate the tracking effectiveness of the IVSCP-PO approach under varied and severe temperature and solar intensity conditions. The results indicate that the IVSCP-PO controller outperforms traditional and recently published methods by avoiding drift and oscillation and minimizing power loss. This translates to maximized static and dynamic tracking efficiencies, reaching 99.99% and 99.98%, respectively. Additionally, the IVSCP-PO controller boasts a record-breaking average tracking time of just 0.002 s, a substantial improvement over traditional and improved PO methods ranging from 0.036 to 0.6 s. To further validate these results, experiments were conducted using the dSPACE 1104 board, demonstrating the superior accuracy and effectiveness of the approach and providing a promising solution to optimize the performance of photovoltaic panels. Full article

Solar energy is a promising and sustainable green energy source, showing significant advancements in photovoltaic (PV) system deployment. To maximize PV efficiency, robust maximum power point tracking (MPPT) methods are essential, as the maximum power point (MPP) shifts with changing irradiance and temperature. This paper proposes a novel MPPT control strategy for a 100 kW grid-connected PV system, based on the incremental conductance (IC) method and enhanced by a cascaded Fractional Order Proportional–Integral (FOPI) and conventional Proportional–Integral (PI) controller. The controller parameters are optimally tuned using the recently introduced RUNge Kutta optimizer (RUN). MATLAB/Simulink simulations have been conducted on the 100 kW benchmark PV model integrated into a medium-voltage grid, with the objective of minimizing the integral square error (ISE) to improve efficiency. The performance of the proposed IC-MPPT-(FOPI-PI) controller has been benchmarked against standalone PI and FOPI controllers, and the RUN optimizer is here compared with recent metaheuristic algorithms, including the Gorilla Troops Optimizer (GTO) and the African Vultures Optimizer (AVO). The evaluation covers five different environmental scenarios, including step, ramp, and realistic irradiance and temperature profiles. The RUN-optimized controller achieved exceptional performance with 99.984% tracking efficiency, sub-millisecond rise time (0.0012 s), rapid settling (0.015 s), and minimal error (ISE: 16.781), demonstrating outstanding accuracy, speed, and robustness. Full article

Overdependence on fossil fuels contributes to global warming and environmental degradation. Solar energy, particularly photovoltaic (PV) power generation, has emerged as a widely adopted clean and renewable alternative. To increase and enhance the efficiency of PV systems, maximum power point tracking (MPPT) technology is essential. However, achieving accurate tracking control while balancing overall performance in terms of stability, dynamic response, and robustness remains a challenge. In this study, an improved MPPT control scheme based on the technique of predicting the reference current at the MPP and regulating the optimal current is proposed. Support vector regression (SVR) endowed with a strong generalization stability was adopted to model the nonlinear relationship between the PV output current and the environmental factors of irradiance and temperature. The sparrow search algorithm (SSA), recognized for its excellent global search capability, was employed to optimize the hyperparameters of SVR to further increase the prediction accuracy. To satisfy the performance requirements for the current-tracking process, a linear quadratic (LQ) optimal control strategy was applied to design the current regulator based on the PV system’s state-space model. The effectiveness and superior performance of the suggested SSA-SVR-LQ control scheme were validated using measured data under real operating conditions. Full article

The power output of a photovoltaic (PV) system is inherently dependent on climatic factors. To maximize the energy harvested from PV arrays, maximum power point tracking (MPPT) algorithms are employed. These algorithms dynamically adjust the operating point of the system to extract the maximum available power. However, under partial shading conditions (PSCs), conventional MPPT algorithms often fail to locate the global maximum power point, leading to suboptimal power extraction. In this study, a robust MPPT technique based on sliding mode control (SMC) is proposed to enhance tracking efficiency and optimize power extraction from PV arrays under PSC. Particle swarm optimization (PSO) is incorporated into the MPPT framework, enabling the dynamic tuning of SMC parameters for improved adaptability and performance. The proposed SMC structure is designed to regulate the duty cycle of a boost converter, ensuring effective power conversion. The system is simulated in Matlab/Simulink for various PSCs. The simulation results demonstrate that the PSO-tuned SMC methodology exhibits superior tracking performance, enabling the PV system to rapidly and accurately converge to the true MPP under varying weather and shading scenarios. The findings indicate that the proposed technique enhances the efficiency and reliability of PV energy harvesting in PSCs. Full article

Measuring solar irradiance is key to assessing the conversion efficiency of photovoltaic (PV) modules. Also, PV modules can be used to estimate irradiance through their electrical response to solar radiation using closed-form models (CFMs). This paper presents a prototype design for irradiance estimation based on evaluating three CFMs by implementing a maximum power point tracking (MPPT) system and a surface temperature measurement system. The system employs an incremental conductance (IC)-based control algorithm, which is optimized to eliminate oscillations at the maximum power point (MPP) and ensure efficient MPP tracking. Experimental validation of the implemented circuits is carried out using Arduino Nano, calibrated sensors, and low-cost electronic devices. Tests in real conditions were performed for four days under different irradiance scenarios, using two monocrystalline PV modules: one with 10 years of use and one new one. The accuracy of the CFMs was evaluated using the mean absolute percentage error (MAPE) and root mean squared error indicators, comparing their estimates with measurements from a Davis Instruments pyranometer. The most accurate CFM obtained a MAPE of 4.38% with the 10-year module and 3.26% with the new module. The results show that the proposed methodology provides estimates with an error of less than 5%, which validates its applicability under various climatic conditions, even with old PV modules. Full article

This paper presents a variable step-size efficient learning modified P&O (LMPO) MPPT algorithm and adaptive proportional–integral (API)-based control techniques for a two-stage three-phase grid-integrated photovoltaic (TS-GIPV) system using an LCL filter. The proposed novel controlled technique introduces two-stage systems under different meteorological conditions and load deviations. The two-stage system with the presented control technique includes maximum power point tracking (MPPT) techniques, intermediate DC-link voltage, and grid current synchronization with a voltage source converter (VSC), respectively. This technique is implemented to improve the extract MPP of the solar PV generator system. An innovative grid-side VSC control technique addresses DC link regulation. Furthermore, this method regulates DC link voltage with an outer voltage loop and an inner current loop controller. Distinctively, the proposed technique regulates the inner loop while avoiding the outer loop. A control mechanism uses an API controller to regulate DC link voltage, distribute power, and synchronize grid current in the face of different scenarios. The fluctuating voltage of the DC link will be kept stable through power balancing. Hence, this technique improves the system stability, dynamic response, and component longevity by effectively reducing oscillations in the fluctuating DC link voltage at twice the grid frequency. The total harmonic distortion (THD%) of the grid currents of the PV power generated in the grid is maintained within the recommended limits. The proposed technique is simulated and verified through MATLAB/Simulink 2019b under different scenarios. Full article

Operating PV panels at the Maximum Power Point (MPP) is crucial for increasing efficiency and reducing the payback period of the system. However, the voltage and current characteristics of PV panels are nonlinear and depend on environmental conditions like temperature and irradiance. This paper presents a comparative analysis of Fuzzy Logic Control (FLC) and Model Predictive Control (MPC) for Maximum Power Point Tracking (MPPT) applied to a photovoltaic generation system. The study focuses on FLC due to its rapid response and robustness against circuit parameter variations. MPC, known for its predictive capabilities, is also investigated for comparison. A PI control strategy is employed to maintain the desired current and voltage during battery charging. The results show that, under standard test conditions (1000 W/m² irradiance and 25 °C temperature), the FLC-based MPPT achieved an average efficiency of 98.298%, with a response time of 12 ms. In comparison, the MPC-based MPPT achieved 96.598% efficiency and a 25 ms response time. During dynamic irradiance changes, FLC demonstrated faster adaptation with a peak tracking error of 2.398%, while MPC had a tracking error of 4.598%. These findings highlight the superior dynamic performance of FLC in real-time PV tracking and the stability of MPC for long-term optimization. Full article

The rapid rise in the power conversion efficiency (PCE) of perovskite solar cells (PSCs) has opened the door for diverse potential applications in powering indoor Internet of Things (IoT) devices. An energy harvesting system (EHS) powered by a PSC module with a backup Li-ion battery, which stores excess power at moments of high irradiances and delivers the stored power to drive the load during operation scenarios with low irradiances, has been designed. A DC-DC boost converter is engaged to match the voltage of the PSC and Li-ion battery, and maximum power point tracking (MPPT) is achieved by a perturb and observe (P&O) algorithm, which perturbs the photovoltaic (PV) system by adjusting its operating voltage and observing the difference in the output power of the PSC. Furthermore, the charging and discharging rate of the battery storage is controlled by a DC-DC buck–boost bidirectional converter with the incorporation of a proportional–integral (PI) controller. The bidirectional DC-DC converter operates in a dual mode, achieved through the anti-parallel connection of a conventional buck and boost converter. The proposed EHS utilizes DC-DC converters, MPPT algorithms, and PI control schemes. Three different case scenarios are modeled to investigate the system’s behavior under varying irradiances of 200 W/m², 100 W/m², and 50 W/m². For all three cases with different irradiances, MPPT achieves tracking efficiencies of more than 95%. The laboratory-fabricated PSC operated at MPP can produce an output power ranging from 21.37 mW (50 W/m²) to 90.15 mW (200 W/m²). The range of the converter’s output power is between 5.117 mW and 63.78 mW. This power range can sufficiently meet the demands of modern low-energy IoT devices. Moreover, fully charged and fully discharged battery scenarios were simulated to study the performance of the system. Finally, the IoT load profile was simulated to confirm the potential of the proposed energy harvesting system in self-sustainable IoT applications. Upon review of the current literature, there are limited studies demonstrating a combination of EHS with PSCs as an indoor power source for IoT applications, along with a bidirectional DC-DC buck–boost converter to manage battery charging and discharging. The evaluation of the system performance presented in this work provides important guidance for the development and optimization of new-generation PV technologies like PSCs for practical indoor applications. Full article

Solar photovoltaic (PV) is a crucial renewable energy source that converts sunlight into electricity using silicon-based semiconductor materials. However, due to the non-linear characteristic behavior of the PV module, the module’s output power varies according to the solar radiation and the ambient temperature. To address this challenge, maximum power point tracking (MPPT) techniques are employed to extract the maximum amount of power from the PV modules. This paper offers a comprehensive review of widely used traditional and advanced MPPT approaches in PV systems, along with current developments and future directions in the field. Under uniform insolation, these methods are compared based on their strengths and weaknesses, including sensed parameters, circuitry, tracking speed, implementation complexity, true MPPT, accuracy, and cost. Additionally, MPPT algorithms are evaluated in terms of their performance in reaching maximum power point (MPP) under partial shading condition (PSC). Existing research clearly demonstrates that the advanced techniques exhibit superior efficiency in comparison to traditional methods, although at the cost of increased design complexity and higher expenses. By presenting a detailed review and providing comparison tables of widely used MPPT techniques, this study aims to provide valuable insights for researchers and practitioners in selecting appropriate MPPT approaches for PV applications. Full article

Renewable energy sources are particularly significant in global energy production, with wind and solar being the most prevalent sources. Managing the simultaneous connection of wind and solar energy generators to the smart grid as distributed generators involves complex control and stabilization due to their inherent uncertainties, making their management more intricate than traditional power plants. This study focuses on enhancing the speed and efficiency of the maximum power point tracking (MPPT) system in a solar power plant. A hybrid network is modeled, comprising a wind turbine with a doubly-fed induction generator (DFIG), a solar power plant with photovoltaic (PV) cells, an MPPT system, a Z-source converter, and a storage system. The proposed approach employs a motion detection-based method, utilizing image-processing techniques to optimize the MPPT of PV cells based on shadow movement patterns within the solar power plant area. This method significantly reduces the time required to reach the maximum power point (MPP), lowers the computational load of the control system by predicting shadow movements, and enhances the MPPT speed while maintaining system stability. The approach, which is suitable for relatively large solar farms, is implemented without the need for any additional sensors and relies on the system’s history. The simulation results show that the proposed approach improves the MPPT system’s efficiency and reduces the pressure on the control circuits by more than 70% in a 150,000 m² solar farm under shaded conditions. Full article

Solar energy is one of the most favorable renewable energy sources and has undergone significant development in the past few years. This paper investigates a novel concept of harvesting the maximum power of a photovoltaic (PV) system using a long-short term memory (LSTM) to forecast the irradiance value and a feedforward neural network (FNN) to predict the maximum power point (MPP) voltage. This study paves a way to mitigate avoidable inefficiencies that hinder the optimal performance of a PV system, due to the intermittent nature of solar energy. MATLAB/Simulink software platform was used to validate the proposed algorithm with real irradiance data from different geographical and weather conditions. Furthermore, the maximum power point tracking (MPPT) algorithm was implemented in a laboratory setup. The simulation results portray the superiority of the proposed method in terms of tracking performance and dynamic response through a comprehensive case study conducted with five other state-of-the-art MPPT methods selected from conventional, AI based, and bio-inspired MPPT categories. In addition to that, faster response time and lesser oscillations around the MPP were observed, even during volatile weather conditions and partial shading. Full article

The coupling of solar cells and Li-ion batteries is an efficient method of energy storage, but solar power suffers from the disadvantages of randomness, intermittency and fluctuation, which cause the low conversion efficiency from solar energy into electric energy. In this paper, a circuit model for the coupling system with PV cells and a charge controller for a Li-ion battery is presented in the MATLAB/Simulink environment. A new three-stage charging strategy is proposed to explore the changing performance of the Li-ion battery, comprising constant-current charging, maximum power point tracker (MPPT) charging and constant-voltage charging stages, among which the MPPT charging stage can achieve the fastest maximum power point (MPP) capture and, therefore, improve battery charging efficiency. Furthermore, the charge controller can improve the lifetime of the battery through the constant-current and constant-voltage charging scheme. The simulation results indicate that the three-stage charging strategy can achieve an improvement in the maximum power tracking efficiency of 99.9%, and the average charge controller efficiency can reach 96.25%, which is higher than that of commercial chargers. This work efficiently matches PV cells and Li-ion batteries to enhance solar energy storages, and provides a new optimization idea for hybrid PV/Li-ion systems. Full article

This paper focuses on the distributed generation (DG) controller of a PV-based microgrid. An independent DG controller (IDGC) is designed for PV applications to improve Maximum-Power Point Tracking (MPPT). The Extreme-Learning Machine (ELM)-based MPPT method exactly estimates the controller’s reference input, such as the voltage and current at the MPP. Feedback controls employ linear PI schemes or nonlinear, intricate techniques. Here, the converter controller is an IDGC that is improved by directly measuring the converter duty cycle and PWM index in a single DG PV-based MG. It introduces a fast-learning Extreme-Learning Machine (ELM) using the Moore–Penrose pseudo-inverse technique and online sequential ridge methods for robust control reference (CR) estimation. This approach ensures the stability of the microgrid during PV uncertainties and various operational conditions. The internal DG control approach improves the stability of the microgrid during a three-phase fault at the load bus, partial shading, irradiance changes, islanding operations, and load changes. The model is designed and simulated on the MATLAB/SIMULINK platform, and some of the results are validated on a hardware-in-the-loop (HIL) platform. Full article