Search Results (298)

Partial shading conditions (PSCs) in photovoltaic (PV) systems generate multiple local maximum power points (LMPPs) and a single global maximum power point (GMPP) in the power–voltage (P–V) characteristics, challenging conventional maximum power point tracking (MPPT) methods. This study presents an FPGA-in-the-Loop (FIL) co-simulation of a Systematic-Sequencing Adaptive Particle Swarm Optimization (SS-APSO) algorithm for MPPT under dynamically varying shading conditions. The proposed method combines deterministic particle initialization, adaptive particle reordering, and switching among wide exploration, re-exploration and exploitation modes to enhance global search capability. The controller is implemented on a Xilinx Artix-7 FPGA using fixed-point arithmetic and a finite-state-machine architecture in VHDL and is evaluated through MATLAB/Simulink–FIL co-simulation for two PV configurations: four series-connected modules (4S) and two parallel-connected strings of two series modules (2S2P). The results demonstrate tracking efficiencies generally exceeding 98% under different shading within 0.181 s for both configurations, while in FIL co-simulation, it reaches the GMPP within 0.203. The close agreement between simulation and FIL co-simulation results demonstrates the effectiveness of the proposed SS-APSO-MPPT controller for PV systems. Full article

In a PV array, mismatch among modules changes the shape of the P–V curve and may create several power peaks. This makes maximum power point tracking (MPPT) more difficult, especially when the tracker needs to distinguish the global maximum power point (GMPP) from local peaks. This paper studies this problem with SVM-ILEA, a hybrid MPPT method that combines support vector machine (SVM) regression and an improved love evolution algorithm (ILEA). The SVM model takes module irradiance and temperature as inputs and predicts a voltage close to the GMPP. ILEA uses this voltage as the search center and avoids scanning the full voltage range. The modified convergence factor and adaptive distance factor further adjust the voltage movement during iteration, giving wider search steps at the early stage and smaller corrections near the optimum to reduce steady-state power oscillations. The simulation setup in MATLAB/Simulink R2019b includes standard test conditions (STC) and static partial shading with non-uniform irradiance and temperature distributions, as well as dynamic operating conditions. Across the four static conditions, SVM-ILEA achieves mean tracking times of 0.0233–0.0303 s and mean steady-state power fluctuations of 0.0111–0.0500 W. Across the three dynamic tests, the mean MPPT efficiency ranges from 97.9057% to 98.2991%. The results obtained demonstrate fast GMPP tracking, small power fluctuation, and stable re-tracking under complex PV operating conditions. Full article

Photovoltaic (PV) pumping systems offer a sustainable alternative to diesel solutions, yet their nonlinearity, intermittent irradiation, and complex motor-pump dynamics challenge energy extraction and reliability. Currently, these systems predominantly rely on classical Maximum Power Point Tracking (MPPT) algorithms such as Perturb and Observe (P&O) and Incremental Conductance (INC), which suffer from slow convergence, steady-state oscillations, and an inability to track Global MPP (GMPP) under uniform irradiance variation conditions. Furthermore, existing studies typically address MPPT optimization and motor control in isolation, without considering their coupled interaction, and rarely incorporate economic viability assessments. To address these limitations, this paper proposes an innovative control architecture integrating four advanced metaheuristic MPPT techniques, namely the Genetic Algorithm (GA), Gray Wolf Optimizer (GWO), Cuckoo Search (CS) algorithm, and Horse Herd Optimization Algorithm (HOA), with Model Predictive Control (MPC) for a Brushless DC (BLDC) motor-driven pumping system, supplemented by battery storage. Comprehensive simulations were conducted under both constant and variable irradiance profiles (1000 to 500 to 1000 W/m²) to evaluate dynamic performance, tracking accuracy, and system robustness. The results demonstrate that HOA and GWO significantly outperform GA and CS, achieving superior DC bus voltage stability with ripple values below 2.4 V, faster convergence times, reduced electromagnetic torque oscillations, and enhanced MPPT efficiency exceeding 99%. Under variable irradiance, HOA exhibits the fastest stabilization with minimal overshoot and superior disturbance rejection, while GA suffers from severe oscillations and CS displays sawtooth ripple patterns. A techno-economic analysis further confirms the economic viability of the proposed system, with HOA and GWO strategies yielding lower lifecycle costs, extended converter lifespans from 5 to over 12 years, and improved return on investment compared to conventional approaches. This integrated framework offers a robust, efficient, and economically sustainable solution for autonomous PV water pumping applications. Full article

This paper presents an energy-management and state-of-charge (SoC) balancing scheme, denoted OEMSS, for a DC microgrid comprising photovoltaic generation, a fuel-cell source, two energy storage systems (ESSs), and six household loads. A demand-driven power-allocation layer first determines whether generation is sufficient, ESS support is required, or priority-based load scheduling must be activated. A supervisory balancing layer then allocates the fleet charging or discharging request by using a capacity-weighted average SoC and separate mode-dependent correction laws. The balancing command is dimensionally expressed as an energy-capacity deviation divided by the control interval and is projected onto the SoC and power limits. A Python simulation driven by recorded generation profiles is used to evaluate four seasonal operating conditions. In the tested equal-capacity case, the maximum inter-ESS SoC deviation is reduced from 18% to 4.8%, synchronization is reached within approximately 2 to 4 h, and simulated over-discharge events are avoided. The reported increase from 45% to approximately 70% is interpreted as a 25-percentage-point increase in the ESS storage contribution rate, rather than an increase in conversion efficiency. During shortage intervals, the retained priority demand is supplied, whereas satisfaction of the original uncurtailed demand is not claimed. A discrete-time Lyapunov analysis gives the nominal convergence condition $0<{\gamma }_b<2$, and the online implementation has $\mathcal{O}(J+K+H)$ time complexity. The study provides simulation evidence for a simple coordinated allocation rule; hardware performance, battery-life extension, converter-level stability, and global optimality remain to be established. Full article

Grid-connected photovoltaic (PV) systems require low-voltage ride-through (LVRT) to function reliably, particularly in the presence of symmetrical and asymmetric disturbances. Conventional PI-based control systems occasionally show limited resilience, particularly in the presence of distorted or imbalanced grid voltage. This paper proposes an enhanced LVRT control strategy for three-phase grid-connected PV systems by integrating a new rapid indirect Global Peak-Oriented Adaptive P&O MPPT method, referred to as (GPOA-P&O), with LADRC and DSOGI-FLL synchronization. The GPOA-P&O algorithm improves maximum power tracking by identifying the global peak and avoiding local maximum points, thereby reducing power fluctuations. Meanwhile, the cascaded LADRC controllers provide accurate voltage and current regulation, effectively suppressing DC-link overvoltage during grid disturbances. DSOGI-FLL ensures accurate positive-sequence phase-locking, enabling compliant reactive current injection even under severe voltage asymmetry, in accordance with grid-code requirements. The proposed method also eliminates second-order power oscillations and maintains constant inverter current regardless of fault severity. Case studies in 2024a MATLAB/Simulink and hardware-in-the-loop experimental platform demonstrate superior stability, fault ride-through capability, and grid-support performance compared to conventional approaches such as PI control and optimized SCSO-tuned PI. Full article

Energy detection is widely used in cooperative spectrum sensing because it requires little prior information about the primary signal, but its performance is sensitive to node-dependent noise power and the signal-to-noise ratio (SNR). This paper proposes a dynamic dual-threshold method under bounded noise power uncertainty. For each sensing node, the local energy statistic is modeled under the idle and occupied hypotheses, and a Bayes-optimal one-sided threshold is evaluated for every admissible noise power value in a multiplicative interval. The lower and upper thresholds are defined as the minimum and maximum of these candidate thresholds. Observations outside the interval are transmitted as one-bit hard decisions, whereas uncertain region observations are normalized, uniformly quantized, and combined at the fusion center by equal gain fusion. Controlled simulations at a matched global false alarm probability show that a well-calibrated fixed dual-threshold benchmark can be competitive near its design point, while node-specific dynamic adaptation becomes advantageous as the uncertainty level increases. The uncertain region reporting probability rises with the uncertainty bound, making the robustness–reporting tradeoff explicit. The results support dynamic threshold adaptation under moderate or relatively large noise power uncertainty and clarify its associated communication cost. Full article

Partial shading is one of the main factors that degrade the output performance and operational reliability of photovoltaic (PV) arrays. It not only causes power loss and multi-peak P–V characteristics, but also induces current mismatch, reverse bias, and local hotspot formation. In this study, an electro-thermal PV module model under partial shading conditions is developed and validated, and an improved sparrow search algorithm (ISSA) is proposed for maximum power point tracking (MPPT) of PV arrays under static and dynamic complex operating conditions. The electrical model is established based on the single-diode model with irradiance, temperature, and Bishop reverse bias corrections, while the thermal model considers solar absorption, heat generation, convection, radiation, and heat conduction. The coupled model is validated against published experimental and numerical results. The predicted peak hotspot temperature is 111.9 °C, corresponding to a relative error of 2.7%; the average absolute errors of current and voltage are 0.20–0.25 A and approximately 0.3 V, respectively, and the maximum relative error of peak temperature is 3.7%. Based on the validated model, a MATLAB/Simulink MPPT platform is constructed to compare particle swarm optimization (PSO), the standard sparrow search algorithm (SSA), and the proposed ISSA. The results show that SSA achieves better global tracking performance than PSO under severe partial shading and dynamic irradiance transitions. Furthermore, by introducing Tent chaotic initialization and random walk perturbation, ISSA significantly improves the convergence speed and reduces steady-state power fluctuation while maintaining high tracking efficiency. Under static shading conditions, ISSA reduces the convergence time from 0.44 s to 0.25 s, 0.24 s to 0.15 s, and 0.44 s to 0.26 s for light, moderate, and severe shading cases, respectively. Under dynamic conditions, ISSA also shortens the post-transition convergence time and suppresses output power oscillation. These results demonstrate that the proposed ISSA-based MPPT method is suitable for PV arrays operating under partial shading and dynamic weather conditions. Full article

This paper presents a novel approach to designing and implementing an Artificial Neural Network (ANN) for maximum power point tracking (MPPT), trained solely on unshaded photovoltaic (PV) manufacturer datasheets and capable of tracking and predicting the maximum power point (MPP) under changing shading conditions. This is also known as partial shading conditions (PSC). PSC arises when shade covers sections of the PV panel due to clouds, trees, dust, or man-made objects such as tall buildings. The proposed ANN-based MPPT technique addresses a common issue faced by conventional MPPT methods under PSC: inaccurate MPPT. PSC induces oscillations on the power-to-voltage curve, resulting in multiple local maxima (LMPPs). However, existing ANN-based MPPT methods are developed and trained on shaded PV datasets. This Neural Network (NN) tracking method complicates the training, development, and implementation processes. It increases the cost of development and requires physical, real-world data collection that requires hardware and a lot of time. All this can be avoided with unshaded PV datasheets. The input parameters used to train the model are temperature (T) and irradiance (G), and the output parameters are maximum power (Pmp) and maximum voltage (Vmp). The ANN-based MPPT technique demonstrated strong performance, accurately predicting the global MPP (GMPP) under PSC with high correlation and low prediction error. Full article

As the search for sustainable energy solutions increases, Photovoltaic (PV) panels have emerged as a crucial technology, harnessing solar energy to meet the growing global demand. These devices require maximum power point tracking (MPPT) for efficient operation as a consequence of their nonlinear electrical behavior. These nonlinearities cause traditional algorithms to be less than fully effective, thus creating room for improvement that can be filled by intelligent algorithm proposals, such as Particle Swarm Optimization (PSO). In this context, a new variant of the PSO algorithm based on evolutionary behavior and voltage window restrictions is presented, implemented, and validated with the aim of developing an advanced control system to operate in a real PV system for MPPT. The study covers several experiments comparing its performance with other PSO variants found in the literature. The proposed algorithm exhibits smoother transitions with fewer power shocks due to a restricted voltage window, ensuring rapid convergence through its evolutionary feature. These improvements lead to a significant reduction in energy losses during the search process, dropping from about 3.76% with the standard PSO to only 2.56%, while also halving the convergence time. Full article

The increasing importance of photovoltaic (PV) systems in the context of the energy transition, together with the need to improve their efficiency, has driven the adoption and development of intelligent and advanced maximum power point tracking (MPPT) techniques. Among these approaches, the Particle Swarm Optimization (PSO) algorithm stands out due to its simplicity, ease of implementation, low number of control parameters, robustness, and fast convergence capability, making it widely applied in modern MPPT systems. However, the performance of PSO in MPPT applications depends on the appropriate selection of both algorithm control parameters and implementation/configurations parameters. The control parameters include the cognitive (C₁) and social (C₂) learning factors, as well as the inertia factor (w), which directly influence swarm dynamics and the balance between exploration and exploitation mechanisms, that is, between global and local search. On the other hand, configuration parameters such as the number of particles and the initialization strategy affect the initial population diversity, the convergence speed toward the maximum power point, and the computational cost of the algorithm, defining the trade-off between speed and accuracy. Despite the extensive research in this field, there is still no clear consensus regarding the most suitable PSO parameter configuration for MPPT applications. This paper presents a statistical analysis of PSO parameter selection in MPPT applications, identifying the most frequently adopted parameter configurations and trends reported in the literature. The findings provide useful guidelines for researchers to select the PSO parameters according to different operating conditions, particularly under partial shading and irradiance variations. From a sustainability perspective, improving MPPT performance contributes to maximizing PV energy harvesting, reducing energy losses, and enhancing the reliability of PV systems, thereby supporting the transition toward more sustainable energy generation. Full article

The rapid advancement of photovoltaic (PV) technologies has transformed solar energy systems into intelligent, high-efficiency platforms. This review systematically examines next-generation PV materials, hybrid system architectures, and intelligent control strategies. Key technologies include perovskite-based tandem cells, N-type TOPCon, bifacial, heterojunction (HJT), and photovoltaic-thermal (PVT) systems. These innovations overcome the intrinsic limitations of conventional P-type silicon panels by reducing recombination losses, mitigating light- and temperature-induced degradation, and enhancing energy yield under real-world operating conditions. At the system level, AI-enabled inverters, adaptive maximum power point tracking (MPPT), predictive maintenance, and real-time grid interaction enable dynamic optimization under variable irradiance, thermal stress, and load fluctuations. A critical comparison across diverse deployment environments highlights current challenges, including manufacturing complexity, material stability, and AI data-quality limitations. Despite higher upfront costs and system complexity, these advanced PV systems offer superior long-term performance, improved reliability, and reduced levelized cost of electricity through lower degradation rates and enhanced operational resilience. Collectively, intelligent, material-optimized PV technologies represent a scalable, sustainable, and grid-compatible solution for solar energy deployment across diverse climates, supporting the global transition toward low-carbon energy infrastructures. Full article

Shadows falling on photovoltaic (PV) modules result in partial shading conditions (PSCs). These conditions affect the power generation of a PV system because of their varying nature. As a result of PSCs, multiple peaks are created; therefore, it is important to identify the global maximum power point (GMPP) for optimal output power. Several maximum power point tracking (MPPT) techniques have been proposed in the literature; however, they face challenges such as oscillation at steady state, long convergence time, high complexity, and low accuracy. In this study, an improved musical chairs algorithm with local adaptive exploration is proposed for MPPT of PV systems under partial shading conditions. The proposed method combines the population-based exploration capability of the musical chairs algorithm with a localized duty-cycle adjustment mechanism around the best operating point. Unlike an offline exhaustive scan, the proposed local exploration stage uses only a small set of neighboring duty-cycle candidates, making the method more suitable for online MPPT implementation. The results are analyzed using the MATLAB/Simulink tool for a 4 × 4 PV array under PSCs. The IMCA-LAE algorithm is compared against the perturb and observe (P&O) algorithm, the incremental conductance (INC) algorithm, the musical chairs algorithm (MCA), and the gray wolf and whale optimization algorithm (GWWA) to illustrate the effectiveness of the suggested hybrid MPPT approach. The efficacy is further examined regarding five performance criteria: generated output power, convergence time, mismatch power loss, efficiency, and fill factor. The proposed IMCA-LAE outperformed the other algorithms. Full article

In the parallel operation of islanded microgrids, line impedance mismatches and random load fluctuations, along with the dynamic response lag and difficulty in multidimensional parameter tuning of traditional control strategies, lead to power sharing imbalances and instability in frequency and voltage. To address these issues, this paper proposes a hierarchical cooperative control strategy based on consistency that integrates load-observation-based dynamic reference feedforward (LODRF) and adaptive particle swarm optimization (APSO). First, an improved adaptive virtual impedance (IAVI) strategy based on consistency is introduced into the virtual synchronous generator control framework. Second, an LODRF mechanism is applied at the secondary control layer to actively reconstruct the power baseline by observing the load status at the point of common coupling (PCC) in real time. Furthermore, an APSO algorithm utilizing the integral of time-weighted absolute error (ITAE) as a global performance index is constructed to optimize key proportional–integral controller parameters cooperatively. Simulation results from a four-unit heterogeneous parallel system in MATLAB/Simulink demonstrate that the IAVI strategy enables stable convergence of frequency and voltage and proportional power sharing. Compared with the system without LODRF, the proposed strategy reduces maximum frequency and voltage dynamic deviations under load disturbances by 78.5% and 53.3%, respectively, and shortens effective recovery times by 0.01 s and 0.09 s, respectively. Moreover, compared with the standard PSO algorithm, the APSO-optimized system reduces maximum frequency and voltage deviations by 3.1% and 36.4%, respectively. Additionally, average active and reactive power sharing errors in the steady state are kept below 0.9%, verifying the significant advantages of the strategy in improving dynamic disturbance rejection and steady-state precision. Full article

By 2024, global photovoltaic (PV) capacity exceeded 2000 GW, corresponding with a decline in levelized costs of approximately 90% since 2010. Artificial intelligence (AI) and machine learning (ML) are enabling novel approaches to solar energy system design and implementation. This survey offers a detailed evaluation of AI/ML methodologies utilized across the solar energy value chain, with a focus on solar irradiance forecasting, maximum power point tracking (MPPT), fault identification, and the expeditious discovery of system materials. The distinction between AI as the broader paradigm and ML as its data-driven subset is drawn and maintained throughout. The primary results cite forecasting improvements via deep learning architectures (LSTM, CNN, Transformer) of 10–40% over traditional methods, while hybrid numerical weather prediction and deep learning models achieve mean absolute error reductions of 15–25%. Reinforcement learning-based MPPT achieves tracking efficiencies in excess of 99% under partial shading, CNN-based fault classification reaches accuracies above 95%, and ML-based screening of materials accelerates perovskite optimization by a factor of 5–10×. Promising paradigms such as explainable AI, federated learning, digital twins, and physics-informed neural networks are evaluated alongside technical, economic, and regulatory constraints. This survey provides a consolidated reference and practical roadmap for the advancement of AI-driven solar energy technologies. Full article

This study proposes a novel state of health (SOH) estimation method by extracting two-level hierarchical features linked to fundamental degradation mechanisms. At the module level, the length of the incremental power curve during constant current charging is extracted, capturing cumulative effects of subtle changes. At the cell level, a combined temperature-weighted voltage inconsistency curve is constructed. The state of charge (SOC) at its distinct knee point within the high-SOC range is a key indicator, signifying the accelerated failure stage where polarization and thermoelectric feedback intensify. This knee-point SOC quantitatively reflects the degree of SOH degradation, making it a valid feature for accurate SOH estimation. The proposed Temporal Convolutional Network–Transformer–Squeeze-and-Excitation (TCN–Transformer–SE) model assigns weights to these features via Squeeze-and-Excitation (SE) and uses Temporal Convolutional Network (TCN) and Transformer branches for parallel local and global temporal decisions. Aging experiments demonstrate the method’s superiority through multi-feature comparison, ablation studies, and benchmark evaluation, achieving a maximum mean absolute error (MAE) of 0.0031, a root mean square error (RMSE) of 0.0038, a coefficient of determination (R²) of 0.9937 and a mean absolute percentage error (MAPE) of 0.3820. The work provides a fusion estimation framework with enhanced interpretability grounded in electrochemical analysis. Full article