Search Results (438)

In this research article, a fast and efficient hybrid Maximum Power Point Tracking (MPPT) control technique is proposed for photovoltaic (PV) systems. The method combines two phases—offline and online—to estimate the appropriate duty cycle for operating the converter at the maximum power point (MPP). In the offline phase, temperature and irradiance inputs are used to compute the real-time reference peak power voltage through an Adaptive Neuro-Fuzzy Inference System (ANFIS). This estimated reference is then utilized in the online phase, where the Robust Backstepping Super-Twisting (RBST) controller treats it as a set-point to generate the control signal and continuously adjust the converter’s duty cycle, driving the PV system to operate near the MPP. The proposed RBST control scheme offers a fast transient response, reduced rise and settling times, low tracking error, enhanced voltage stability, and quick adaptation to changing environmental conditions. The technique is tested in MATLAB/Simulink under three different scenarios: continuous variation in meteorological parameters, sudden step changes, and partial shading. To demonstrate the superiority of the RBST method, its performance is compared with classical backstepping and integral backstepping controllers. The results show that the RBST-based MPPT controller achieves the minimum rise time of $0.018$$\mathrm{s}$, the lowest squared error of $0.3015$$\mathrm{V}$, the minimum steady-state error of $0.29$%, and the highest efficiency of $99.16$%. Full article

Partial shading in photovoltaic (PV) systems causes multiple local maximum power points (LMPPs), complicating tracking and reducing energy efficiency. Conventional maximum power point tracking (MPPT) methods, such as Perturb and Observe (P&O), often fail because of oscillations and entrapment at local maxima. To address these shortcomings, this study proposes a hybrid MPPT strategy combining artificial neural networks (ANNs) and the P&O algorithm to enhance tracking accuracy under partial shading while maintaining implementation simplicity. The research employs a detailed PV cell model in MATLAB/Simulink (2019b) that incorporates dynamic shading to simulate non-uniform irradiance. Within this framework, an ANN trained with the Levenberg–Marquardt algorithm predicts global maximum power points (GMPPs) from voltage and irradiance data, guiding and accelerating subsequent P&O operation. In the hybrid system, the ANN predicts the maximum power points (MPPs) to provide initial estimates, after which the P&O fine-tunes the duty cycle optimization in a DC-DC converter. The proposed hybrid ANN–P&O MPPT method achieved relative improvements of 15.6–49% in tracking efficiency, 16–20% in stability, and 14–54% in convergence speed compared with standalone P&O, depending on the irradiance scenario. This research highlights the potential of ANN-enhanced MPPT systems to maximize energy harvest in PV systems facing shading variability. Full article

Tree ferns are ubiquitous in New Zealand forests, but there is limited knowledge of their role in urban plant communities and potential use in restoration. We assessed sixteen sites by measuring 200 m² plots to investigate how tree ferns influence vascular plant composition in Hamilton, North Island, New Zealand. The sixteen plots were assigned to four site type combinations based on restoration status (restored or unrestored) and tree fern presence, each with four plots. Average native plant species richness was higher at sites with tree ferns (36 ± 16; S = 68) than at sites without (19 ± 14; S = 41), with more diverse ground fern and epiphyte assemblages. Higher native plant richness at restored sites (34 ± 18; S = 62) compared to unrestored sites (20 ± 14, S = 44) was partially attributed to increased plant abundances. Multivariate analyses revealed differences in plant community composition among our site types. Angiosperms and conifers were less prevalent in plots with tree ferns, suggesting competitive relationships among these groups. However, tree ferns were associated with some shade-tolerant trees, such as Schefflera digitata J.R.Forst. & G.Forst. Indicator species of sites with tree ferns were mainly ground ferns and epiphytes (e.g., Blechnum parrisiae Christenh. and Trichomanes venosum R.Br.), whereas species with high fidelity to sites without tree ferns were pioneer trees and shrubs (e.g., Pittosporum eugenioides A.Cunn.). Community structure analyses revealed that total basal areas were highest at unrestored sites with tree ferns, but restored sites exhibited more diverse tree communities. Environmental predictors that correlated significantly with the compositional differences among our site types were tree fern basal area and restoration age. Our results highlight the need to reconsider the potential of tree ferns in current restoration practice. Tree ferns were found to augment native plant diversity in our study, indicating their potential to enhance urban ecological restoration projects in New Zealand. Full article

Photovoltaic (PV) systems are swiftly expanding within the building sector, offering significant benefits such as renewable energy integration, yet introducing challenges due to mismatches between local generation and demand. With the increasing availability of data and advanced modeling tools, stakeholders are increasingly motivated to adopt energy management and optimization techniques, where accurate forecasting of PV generation is essential. While the existing literature provides valuable insights, a comprehensive review of methodologies specifically tailored for the forecast of PV generation in buildings remains scarce. This study aims to address this gap by analyzing the forecasting methods, data requirements, and performance metrics employed, with the primary objective of providing an in-depth review of previous research. The findings highlight the critical role of improving PV energy generation forecasting accuracy in enhancing energy management and optimization for individual buildings. Additionally, the study identifies key challenges and opportunities for future research, such as the limited exploration of localized environmental and operational factors (such as partial shading, dust, and dirt); insufficient data on building-specific PV output patterns; and the need to account for variability in PV generation. By clarifying the current state of PV energy forecasting methodologies, this research lays essential groundwork for future advancements in the field. Full article

The main objective of this paper is to develop a maximum power point tracker (MPPT) for a photovoltaic module array (PVMA) under conditions of partial shading and sudden changes in solar irradiance. PVMAs exhibit nonlinear characteristics with respect to temperature and solar irradiance conditions. Therefore, when some modules in the array are shaded or when there is a sudden change in solar irradiance, the maximum power point (MPP) of the array will also change, and the power–voltage (P-V) characteristic curve may exhibit multiple peaks. Under such conditions, if the tracking algorithm employs a fixed step size, the time required to reach the MPP may be significantly prolonged, potentially causing the tracker to converge on a local maximum power point (LMPP). To address the issues mentioned above, this paper proposes a novel MPPT technique based on the nature-inspired idols algorithm (IA). The technique allows the promotion value (PM) to be adjusted through the anti-fans weight (afw) in the iteration formula, thereby achieving global maximum power point (GMPP) tracking for PVMAs. To verify the effectiveness of the proposed algorithm, a model of a 4-series–3-parallel PVMA was first established using MATLAB (2024b version) software under both non-shading and partial shading conditions. The voltage and current of the PVMAs were fed back, and the IA was then applied for GMPP tracking. The simulation results demonstrate that the IA proposed in this study outperforms existing MPPT techniques, such as particle swarm optimization (PSO), cat swarm optimization (CSO), and the bat algorithm (BA), in terms of tracking speed, dynamic response, and steady-state performance, especially when the array is subjected to varying shading ratios and sudden changes in solar irradiance. Full article

Partial shading poses a significant challenge to photovoltaic (PV) systems by degrading power output and overall efficiency, especially under non-uniform irradiance conditions. This paper proposes an advanced time-sharing maximum power point tracking (MPPT) control strategy implemented through a non-isolated single-phase multi-input microinverter architecture. The system enables individual power regulation for multiple PV modules while preserving their voltage–current (V–I) characteristics and eliminating the need for additional active switches. Building on the concept of distributed MPPT (DMPPT), a flexible full power processing (FPP) framework is introduced, wherein a single MPPT controller sequentially optimizes each module’s output. By leveraging the slow-varying nature of PV characteristics, the proposed algorithm updates control parameters every half-cycle of the AC output, significantly enhancing controller utilization and reducing system complexity and cost. The control strategy is validated through detailed simulations and experimental testing under dynamic partial shading scenarios. Results confirm that the proposed system maximizes power extraction, maintains voltage stability, and offers improved thermal performance, particularly through the integration of GaN power devices. Overall, the method presents a robust, cost-effective, and scalable solution for next-generation PV systems operating in variable environmental conditions. Full article

Photovoltaic (PV) systems are increasingly integral to global energy solutions, but their long-term reliability is challenged by various operational faults. In this article, we propose an advanced hybrid diagnostic framework combining a Deep Belief Network (DBN) for feature pattern extraction and a Light Gradient Boosting Machine (LightGBM) for classification to detect and diagnose PV panel faults. The proposed model is trained and validated on the QASP PV Fault Detection Dataset, a real-time SCADA-based dataset collected from 255 W panels at the Quaid-e-Azam Solar 100 MW Power Plant (QASP), Pakistan’s largest solar facility. The dataset encompasses seven classes: Healthy, Open Circuit, Photovoltaic Ground (PVG), Partial Shading, Busbar, Soiling, and Hotspot Faults. The DBN captures complex non-linear relationships in SCADA parameters such as DC voltage, DC current, irradiance, inverter power, module temperature, and performance ratio, while LightGBM ensures high accuracy in classifying fault types. The proposed model is trained and evaluated on a real-world SCADA-based dataset comprising 139,295 samples, with a 70:30 split for training and testing, ensuring robust generalization across diverse PV fault conditions. Experimental results demonstrate the robustness and generalization capabilities of the proposed hybrid (DBN–LightGBM) model, outperforming conventional machine learning methods and showing an accuracy of 98.21% classification accuracy, 98.0% macro-F1 score, and significantly reduced training time compared to Transformer and CNN-LSTM baselines. This study contributes to a reliable and scalable AI-driven solution for real-time PV fault monitoring, offering practical implications for large-scale solar plant maintenance and operational efficiency. Full article

A newly developed hybrid maximum power point tracker (MPPT) utilizes a modified simulated annealing (SA) algorithm in conjunction with an adaptive hill climbing (HC) technique to optimize the extraction of the maximum power point (MPP) from photovoltaic (PV) systems. This innovative MPPT improves the ability to harvest maximum power from the PV system, particularly under rapidly fluctuating weather conditions and in situations of partial shading. The controller combines the rapid local search abilities of HC with the global optimization advantages of SA, which has been modified to retain and retrieve the maximum power achieved, thus ensuring the extraction of the global maximum. Furthermore, an adaptive HC algorithm is implemented with a variable step size adjustment, which accelerates convergence and reduces steady-state oscillations. Additionally, an offline SA algorithm is utilized to fine-tune the essential parameters of the proposed controller, including the maximum and minimum step sizes for duty cycle adjustments, initial temperature, and cooling rate. Simulations performed in Matlab/Simulink, along with experimental validation using Imperix-Opal-RT, confirm the effectiveness and robustness of the proposed controller. In the scenarios that were tested, the suggested HC–SA reached the global maximum power point (GMPP) of approximately 600 W in about 0.05 s, whereas the traditional HC stabilized at a local maximum close to 450 W, and the fuzzy-logic MPPT attained the GMPP at a slower rate, taking about 0.2 s, with a pronounced transient dip before settling with a small steady-state ripple. These findings emphasize that, under the operating conditions examined, the proposed method reliably demonstrates quicker convergence, enhanced tracking accuracy, and greater robustness compared with the other MPPT techniques. Full article

Photovoltaic energy has become a key pillar in the transition to sustainable energy systems, driven by the need for efficient energy conversion and the reduction of dependency on fossil fuels. Maximum Power Point Tracking (MPPT) is central to optimizing the performance of photovoltaic systems by ensuring the maximum extraction of solar energy, even under fluctuating environmental conditions. This review provides a comprehensive analysis of MPPT algorithms developed and refined over the past decade (2015–2025), highlighting major breakthroughs in algorithmic approaches, from conventional methods such as Perturb and Observe (P&O) and Incremental Conductance (IncCond) to more advanced techniques incorporating artificial intelligence, fuzzy logic, and hybrid systems. The paper evaluates the evolution of MPPT techniques, focusing on their effectiveness in real-world applications, particularly in optimizing photovoltaic output under diverse operating conditions such as partial shading, temperature variations, and rapid irradiance changes. Furthermore, it discusses the ongoing challenges in the field and the promising directions for future research, aiming to further enhance the reliability and efficiency of solar power systems worldwide. Full article

As photovoltaic (PV) deployment has expanded from rural sites to the built environment, rooftops are increasingly used for electricity generation. In these settings, the visible sky is often partially obstructed by adjacent walls, producing shading that reduces energy yield. This study quantifies the effect of wall shading on incident solar radiation and system losses, and contrasts it with inter-row (mutual) shading experienced by PV arrays in open fields. Systems installed near obscuring walls are subject to both phenomena. To our knowledge, the specific impact of wall shading on PV systems has not been examined comprehensively. We characterize how wall height governs shadow geometry, determine the resulting numbers of shaded and unshaded cells and modules, and assess how shaded modules influence the performance of the remaining modules in a series string. For the parameter set analyzed, annual energy losses are 7.7% due to wall shading and 4% due to inter-row shading, yielding a combined loss of 10.2%. The methods and results provide a practical basis for designers to estimate shading losses and expected energy production for PV systems sited near obscuring walls. Full article

The pressing global transition to sustainable energy has intensified interest in overcoming the efficiency bottlenecks of conventional solar technologies. Hybrid photovoltaic–thermoelectric generator (PV–TEG) systems have recently emerged as a compelling solution, synergistically harvesting both electrical and thermal energy from solar radiation. By converting both sunlight and otherwise wasted heat, these integrated systems can substantially enhance total energy yield and overall conversion efficiency—mitigating the performance limitations of standalone PV panels. This review delivers a comprehensive, systematic assessment of maximum-power-point tracking (MPPT) methodologies specifically tailored for hybrid PV–TEG architectures. MPPT techniques are meticulously categorized and critically analyzed within the following six distinct groups: conventional algorithms, metaheuristic approaches, artificial intelligence (AI)-driven methods, mathematical models, hybrid strategies, and novel emerging solutions. For each category, we examine operational principles, implementation complexity, and adaptability to real-world phenomena such as partial shading and non-uniform temperature distribution. Through thorough comparative evaluation, the review uncovers existing research gaps, highlights ongoing challenges, and identifies promising directions for technological advancement. This work equips researchers and practitioners with an integrated knowledge base, fostering informed development and deployment of next-generation MPPT solutions for high-performance hybrid solar–thermal energy systems. Full article

The growing deployment of photovoltaic (PV) power plants has made reliable fault detection and classification a critical challenge for ensuring operational efficiency, safety, and economic viability. Faults on the direct current (DC) side, especially during the commissioning phase, can significantly affect power output and maintenance costs. This paper proposes a fault classification methodology for the direct current (DC) side of PV power plants, using the MATLAB/Simulink 2023b simulation environment for system modeling and dataset generation. The method accounts for different environmental and operational conditions—including irradiance and temperature variations—to enhance fault identification robustness. The main electrical faults—such as open circuit (OC), short circuit (SC), connector faults, and partial shading—are analyzed based on features extracted from current–voltage (I–V) and power–voltage (P–V) curves. The proposed classification system achieved 100% accuracy by applying the One-Versus-One (OVO) and One-Versus-Rest (OVR) techniques, using a dataset with 704 samples for one string and 2480 samples for three strings. The lowest accuracies were observed with the OVO technique: 99.03% for 1024 samples with one string, and 97.35% for 880 samples with three strings. The study also highlights the performance of multiclass machine learning techniques across different dataset sizes. The results reinforce the relevance of using machine learning integrated into the commissioning phase of PV systems, with the potential to improve reliability, reduce losses, and optimize the operational costs of solar plants. Future work should explore the application of this method to real-world data, as well as its deployment in the field to support companies and professionals in the sector. Full article

Partial shading conditions cause current mismatches between series-connected panels in photovoltaic (PV) arrays, significantly reducing power efficiency. To mitigate this limitation, reconfiguration methods based on dynamically changing the electrical connections within the PV array have been proposed. In recent years, adaptive and dual-adaptive PV connection structures, which particularly balance the line currents and aim to restore current symmetry under irregular shading conditions, have gained prominence due to their notable efficiency improvements. The dual nature of these structures inherently supports this symmetry by enabling balanced reconfigurations on both sides of the array. However, the dual-adaptive structure expands the solution space due to the exponential growth of the connection combinations with the increasing number of lines, and this makes real-time optimization difficult. In fact, this structure has been optimized with genetic algorithm (GA) before; however, the convergence time of GA exceeds acceptable limits in large arrays. In this study, a Particle Swarm Optimization (PSO) algorithm is applied to solve the dual-adaptive PV array reconfiguration problem. Particle Swarm Optimization (PSO) is a metaheuristic algorithm that utilizes swarm intelligence to efficiently explore large solution spaces. PSO’s fast convergence capability and low computational cost enable real-time applications by enabling optimization in acceptable times even for larger PV arrays. Simulation results reveal that PSO successfully manages the exponential growth in the solution space and significantly increases the real-time applicability of the reconfiguration process by effectively increasing the efficiency. In this respect, PSO is considered a powerful and practical solution for reconfiguration problems in large-scale PV arrays. Full article

This article examines the advantages and disadvantages of deploying photovoltaic power plants in residential areas, considering both their current development status and specific operational risks, such as the unpredictability associated with potential faults. It highlights that errors of existing PV technologies can pose risks, including the potential for fire and electrocution. To improve efficiency and address these identified issues, the paper emphasizes the benefits of using additional electronic equipment, called “optimizers”, which, in conjunction with the inverters, can provide arc-fault circuit interruption and rapid shutdown of the photovoltaic systems. These technologies are designed to reduce faults and enhance operational safety, thereby reducing the risk of electrocution for maintenance personnel. They are recommended especially for rooftop PV systems that are affected by shading conditions. Furthermore, experimental results indicate that the use of such optimizers can lead to a power gain of up to 50% in partial shading. Full article

Bypass diode faults are among the most hard-to-detect but impactful anomalies in photovoltaic (PV) systems, especially under partial shading conditions, where their electrical signatures often resemble those caused by non-critical irradiance variations. This study presents a systematic simulation-based investigation into how different bypass diode fault types—short-circuited, open-circuited, and healthy—affect the electrical behavior of PV strings under diverse irradiance profiles. A high-resolution MATLAB/Simulink model is developed to simulate 27 unique diode fault configurations across multiple shading scenarios, enabling the extraction of key features from resulting I–V curves. These features include global and local maximum power point parameters, open-circuit voltage, and short-circuit current. To address the challenge of feature redundancy and classification ambiguity, a preprocessing step is applied to remove near-duplicate instances and improve model generalization. An artificial neural network (ANN) model is then trained to classify the number of faulty bypass diodes based on these features. Comparative evaluations are conducted with support vector machines and random forests. The results indicate that the ANN achieves the highest test accuracy (93.57%) and average AUC (0.9925), outperforming other classifiers in both robustness and discriminative power. These findings highlight the importance of feature-informed, data-driven approaches for fault detection in PV systems and demonstrate the feasibility of diode fault classification without precise fault localization. Full article