Search Results (933)

This paper reviews recent progress in fault detection, reliability analysis, and predictive maintenance methods for grid-connected solar photovoltaic (PV) systems. With the rising adoption of solar power globally, maintaining system reliability and performance is vital for a sustainable energy supply. Common faults discussed include panel degradation, electrical issues, inverter failures, and grid disturbances, all of which affect system efficiency and safety. While traditional diagnostics like thermal imaging and V-I curve analysis offer valuable insights, they mostly detect issues reactively. New approaches using Artificial Intelligence (AI), Machine Learning (ML), and Internet of Things (IoT) enable real-time monitoring and predictive diagnostics, significantly enhancing accuracy and reliability. This study represents the introduction of a consolidated decision framework and taxonomy that systematically integrates and evaluates the fault types, symptoms, signals, diagnostics, and field-readiness across both plant types and voltage levels. Moreover, this study provides quantitative benchmarks of performance metrics, energy losses, and diagnostic accuracies of 95% confidence intervals. Adopting these advanced techniques promotes proactive management, reducing operational risks and downtime, thus reinforcing the resilience and sustainability of solar power infrastructure. Full article

Renewable energy sources can be of significant help to rural communities with inadequate electricity access. This study presents a comprehensive techno-economic assessment of a 500 kWp solar Photovoltaic (PV) energy system designed for Ibadan, Nigeria. A novel hybrid modeling framework was developed in which technical performance analysis was employed using PVSyst, whereas economic and optimization analysis was carried out using HOMER. Simulation outputs from PVSyst were integrated as inputs into HOMER, enabling a more accurate and consistent cross-platform assessment. Nigeria’s enduring energy crisis, marked by persistent grid unreliability and limited electricity access, necessitates need for exploration of sustainable alternatives. Among these, solar photovoltaic (PV) technology offers significant promise given the country’s abundant solar irradiation. The proposed system was evaluated using meteorological and load demand data. PVSyst simulations projected an annual energy yield of 714,188 kWh, with a 25-year lifespan yielding a performance ratio between 77% and 78%, demonstrating high operational efficiency. Complementary HOMER Pro analysis revealed a competitive levelized cost of energy (LCOE) of USD 0.079/kWh—substantially lower than the baseline grid-only cost of USD 0.724/kWh, and a Net Present Cost (NPC) of USD 6.1 million, reflecting considerable long-term financial savings. Furthermore, the system achieved compelling environmental outcomes, including an annual reduction of approximately 160,508 kg of CO₂ emissions. Sensitivity analysis indicated that increasing the feed-in tariff (FiT) from USD 0.10 to USD 0.20/kWh improved the project’s financial viability, shortening payback periods to just 5.2 years and enhancing return on investment. Overall, the findings highlight the technical robustness, economic competitiveness, and environmental significance of deploying solar-based energy solutions, while reinforcing the urgent need for supportive energy policies to incentivize large-scale adoption. Full article

This study presents the design and techno-economic optimization of a hybrid renewable energy system (HRES) for a coastal hotel in Manavgat, Türkiye. The system integrates photovoltaic (PV) panels, wind turbines (WT), pumped hydro storage (PHS), hydrogen storage (electrolyzer, tank, and fuel cell), batteries, a fuel cell-based combined heat and power (CHP) unit, and a boiler to meet both electrical and thermal demands. Within this broader optimization framework, six optimal configurations emerged, representing grid-connected and standalone operation modes. Optimization was performed in HOMER Pro to minimize net present cost (NPC) under strict reliability (0% unmet load) and renewable energy fraction (REF > 75%) constraints. The grid-connected PHS–PV–WT configuration achieved the lowest NPC ($1.33 million) and COE ($0.153/kWh), with a renewable fraction of ~96% and limited excess generation (~21%). Off-grid PHS-based and PHS–hydrogen configurations showed competitive performance with slightly higher costs. Hydrogen integration additionally provides complementary storage pathways, coordinated operation, waste heat utilization, and redundancy under component unavailability. Battery-only systems without PHS or hydrogen storage resulted in 37–39% higher capital costs and ~53% higher COE, confirming the economic advantage of long-duration PHS. Sensitivity analyses indicate that real discount rate variations notably affect NPC and COE, particularly for battery-only systems. Component cost sensitivity highlights PV and WT as dominant cost drivers, while PHS stabilizes system economics and the hydrogen subsystem contributes minimally due to its small scale. Overall, these results confirm the techno-economic and environmental benefits of combining seawater-based PHS with optional hydrogen and battery storage for sustainable hotel-scale applications. Full article

Solar energy solutions have become increasingly popular worldwide due to the growing need for renewable energy. This article presents a photovoltaic (PV) system connected to a three-phase power grid, modeled under varying climatic conditions. It consists of two conversion stages, a DC-DC Boost converter and a DC-AC inverter. The former uses a variable-step P&O based on fuzzy logic control to maximize the power of the photovoltaic panels, allowing for greater tracking accuracy than traditional P&O techniques. Inverters with phase-locked loop technology improve the performance of grid-connected PV systems by using a conventional PI controller that has a faster response. Using Matlab/Simulink environments, the entire system and control techniques are evaluated and verified. The simulation results confirm the effectiveness and robustness of the proposed system. Full article

A multi-port conversion system that connects photovoltaic (PV) arrays, battery energy storage (BES), and an electric vehicle (EV) to a single-phase grid offers a flexible solution for smart homes. By integrating Vehicle-to-Grid (V2G) and Vehicle-to-Home (V2H) technologies, the system supports bidirectional energy flow, optimizing usage, improving grid stability, and supplying backup power. The proposed four-port converter consists of an interleaved bidirectional DC-DC converter for high-voltage BES, a bidirectional buck–boost DC-DC converter for EV charging and discharging, a DC-DC boost converter with MPPT for PV, and a grid-tied inverter. Its non-isolated structure ensures high efficiency, compact design, and fewer switches, making it suitable for residential applications. A state-of-charge (SoC)-based power management strategy coordinates operation among PV, BES, and EV in both on-grid and off-grid modes. It reduces reliance on EV energy when supporting V2G and V2H, while SoC balancing between BES and EV extends lifetime and lowers current stress. A 7.5 kVA system was simulated in MATLAB/Simulink to validate feasibility. Two scenarios were studied: PV, BES, and EV with V2G supporting the grid and PV, BES, and EV with V2H providing backup power in off-grid mode. Tests under PV fluctuations and load variations confirmed the effectiveness of the proposed design. The system exhibited a fast transient response of 0.05 s during grid-support operation and maintained stable voltage and frequency in off-grid mode despite PV and load fluctuations. Its protection scheme disconnected overloads within 0.01 s, while harmonic distortions in both cases remained modest and complied with EN50610 standards. Full article

This research presents an optimized control strategy for the coordinated operation of parallel grid connected photovoltaic (PV) plants and an On Load Tap Changer (OLTC) transformer. The proposed framework integrates inverter-level active and reactive power dispatch with OLTC tap control through an Energy Management System (EMS) based on an improved Teaching Learning Based Optimization (TLBO) algorithm. The EMS minimizes operational costs while maintaining voltage stability and respecting electrical and mechanical constraints. Comparative analyses with Monte Carlo, fmincon, and conventional TLBO methods demonstrate that the optimized TLBO achieves up to two orders of magnitude faster convergence and higher robustness, enabling more reliable performance under variable irradiance and load conditions. Simulation and Hardware-in-the-Loop (HIL) results confirm that the coordinated OLTC inverter control significantly enhances reactive power capability and voltage regulation. The proposed optimized TLBO based EMS offers an effective and computationally efficient solution for dynamic energy management in medium scale PV systems, supporting grid reliability and maximizing renewable energy utilization. Full article

Unplanned islanding and off-grid issues of photovoltaic (PV) power stations caused by tie-line faults have seriously undermined the power supply reliability and operational stability of PV plants. Furthermore, it takes a relatively long time to restore normal operation after an off-grid event, leading to substantial power losses. To address this problem, this paper proposes a tie-line fault ride-through control strategy based on the coordinated control of on-site energy storage units. After a fault on the tie-line occurs, the control mode of PV inverters is switched to achieve source–load balance, and the control mode of energy storage inverters is switched to VF control mode, which supports the stability of voltage and frequency in the islanded system. Subsequently, the strategy coordinates with the tie-line recloser device to perform synchronous checking and grid reconnection. Simulation results show that, for transient tie-line faults, the proposed method can achieve stable control of the islanded system and grid reconnection within 2 s after a fault on the tie-line occurs. It successfully realizes fault ride-through within the operation time limit of anti-islanding protection, effectively preventing the PV plant from disconnecting from the grid. Finally, a connection scheme for the control strategy of a typical PV plant is presented, providing technical reference for on-site engineering. Full article

To address the challenge of coordinating economic cost control and low-carbon objectives in microgrid scheduling, while overcoming the performance limitations of the traditional Zebra Optimization Algorithm (ZOA) in complex problems, this paper proposes a Synergistic Zebra Optimization Algorithm (SZOA) and integrates it with innovative management concepts to enhance the microgrid scheduling process. The SZOA incorporates three core strategies: a multi-population cooperative search mechanism to strengthen global exploration, a vertical crossover–mutation strategy to meet high-dimensional scheduling requirements, and a leader-guided boundary control strategy to ensure variable feasibility. These strategies not only improve algorithmic performance but also provide technical support for innovative management in microgrid scheduling. Extensive experiments on the CEC2017 (d = 30) and CEC2022 (d = 10, 20) benchmark sets demonstrate that the SZOA achieves higher optimization accuracy and stability compared with those of nine state-of-the-art algorithms, including IAGWO and EWOA. Friedman tests further confirm its superiority, with the best average rankings of 1.20 for CEC2017 and 1.08/1.25 for CEC2022 (d = 10, 20). To validate practical applicability, the SZOA is applied to grid-connected microgrid scheduling, where the system model integrates renewable energy sources such as photovoltaic (PV) generation and wind turbines (WT); controllable sources including fuel cells (FC), microturbines (MT), and gas engines (GS); a battery (BT) storage unit; and the main grid. The optimization problem is formulated as a bi-objective model minimizing both economic costs—including fuel, operation, pollutant treatment, main-grid interactions, and imbalance penalties—and carbon emissions, subject to constraints on generation limits and storage state-of-charge safety ranges. Simulation results based on typical daily data from Guangdong, China, show that the optimized microgrid achieves a minimum operating cost of USD 5165.96, an average cost of USD 6853.07, and a standard deviation of only USD 448.53, consistently outperforming all comparison algorithms across economic indicators. Meanwhile, the SZOA dynamically coordinates power outputs: during the daytime, it maximizes PV utilization (with peak output near 35 kW) and WT contribution (30–40 kW), while reducing reliance on fossil-based units such as FC and MT; at night, BT discharges (−20 to −30 kW) to cover load deficits, thereby lowering fossil fuel consumption and pollutant emissions. Overall, the SZOA effectively realizes the synergy of “economic efficiency and low-carbon operation”, offering a reliable and practical technical solution for innovative management and sustainable operation of microgrid scheduling. Full article

High-precision photovoltaic (PV) power generation prediction models are essential for ensuring secure and stable grid operation and optimized dispatch. Existing models often ignore the significant variations in PV grid-connected inverter loss distributions and exhibit inadequate data decomposition processing, which influences the accuracy of the prediction models. This paper proposes a PSO-VMD-LSTM prediction model that includes PV converter loss characteristics. Firstly, the Particle Swarm Optimization (PSO) algorithm is employed to optimize the parameters of Variational Mode Decomposition (VMD), enabling effective decomposition of data under different weather conditions. Secondly, the decomposed sub-modes are individually fed into Long Short-Term Memory (LSTM) networks for prediction, and the results are subsequently reconstructed to obtain preliminary predictions. Finally, a neural network-based equivalent model for inverter losses is constructed; the preliminary predictions are fed into this model to obtain the final prediction results. Simulation case studies demonstrate that the proposed PSO-VMD-LSTM-based model can comprehensively consider the impact of uneven converter loss distribution and effectively improve the accuracy of PV power prediction models. Full article

This paper presents a performance evaluation of a 140 kW solar array installed on the rooftop of the Mountain Line Transit Authority (MLTA) building in Morgantown, West Virginia (WV), USA, covering the period from 2013 to 2024. The grid-connected photovoltaic (PV) system consists of 572 polycrystalline PV modules, each rated at 245 watts. The study examines key performance parameters, including annual electricity production, average daily and annual capacity utilization hours (CUH), current array efficiency, and performance degradation. Monthly ambient temperature and global tilted irradiance (GTI) data were obtained from the NASA POWER website. During the assessment, observations were made regarding the tilt angles of the panels and corrosion of metal parts. From 2013 to 2024, the total electricity production was 1588 MWh, with an average annual output of 132 MWh. Over this 12-year period, the CO₂ emissions reduction attributed to the solar array is estimated at 1,413,497 kg, or approximately 117,791 kg/year, compared to emissions from coal-fired power plants in WV. The average daily CUH was found to be 2.93 h, while the current PV array efficiency in April 2024 was 10.70%, with a maximum efficiency of 14.30% observed at 2:00 PM. Additionally, an analysis of annual average performance degradation indicated a 2.28% decline from 2013 to 2016, followed by a much lower degradation of 0.17% from 2017 to 2023, as electricity production data were unavailable for most summer months of 2024. Full article

With growing urban populations and rapid technological advancement, major cities worldwide are facing pressing challenges from surging energy demands. Interestingly, substantial unused space within residential buildings offers potential for installing renewable energy systems coupled with energy storage. This study innovatively proposes a grid-connected photovoltaic (PV) system integrated with pumped hydro storage (PHS) and battery storage for residential applications. A novel optimization algorithm is employed to achieve techno-economic optimization of the hybrid system. The results indicate a remarkably short payback period of about 5 years, significantly outperforming previous studies. Additionally, a threshold is introduced to activate pumping and water storage during off-peak nighttime electricity hours, strategically directing surplus power to either the pump or battery according to system operation principles. This nighttime water storage strategy not only promises considerable cost savings for residents, but also helps to mitigate grid stress under time-of-use pricing schemes. Overall, this study demonstrates that, through optimized system sizing, costs can be substantially reduced. Importantly, with the nighttime storage strategy, the payback period can be shortened even further, underscoring the novelty and practical relevance of this research. Full article

In this study, a novel comparison between single phase 7-Level Packed U—Cell (PUC) inverter and single phase 9-Level Cross Switches Cell (CSC) inverter with Model Predictive Controller (MPC) for solar grid-tied applications is presented. Our innovation introduces a unique approach by integrating PV solar panels in PUC and CSC inverters in their two DC links rather than just one which increases power density of the system. Another key benefit for the proposed models lies in their simplified design, offering improved power quality and reduced complexity relative to traditional configurations. Moreover, both models feature streamlined control architectures that eliminate the need for additional controllers such as PI controllers for grid reference current extraction. Furthermore, the implementation of Maximum Power Point Tracking (MPPT) technology directly optimizes power output from the PV panels, negating the necessity for a DC-DC booster converter during integration. To validate the proposed concept’s performance for both inverters, extensive simulations were conducted using MATLAB/Simulink, assessing both inverters under steady-state conditions as well as various disturbances to evaluate its robustness and dynamic response. Both inverters exhibit robustness against variations in grid voltage, phase shift, and irradiation. By comparing both inverters, results demonstrate that the CSC inverter exhibits superior performance due to its booster feature which relies on generating voltage level greater than the DC input source. This primary advantage makes CSC a booster inverter. Full article

Insulation degradation in photovoltaic (PV) cables can cause electrical faults and fire hazards, thereby compromising system reliability and safety. Early detection of precursor signals is crucial for preventive maintenance. However, conventional diagnostic techniques are limited to static assessments and fail to capture early-stage electrical anomalies in real-time. This study investigates the time-series behavior of voltage, current, and temperature in PV cables under thermal stress conditions. Experiments were conducted using TFR-CV cables installed in a vertically stacked and tight-contact configuration. A gas torch was applied for localized heating to induce insulation degradation. A grid-connected testbed with six series-connected PV modules was constructed. Each module was instrumented with PV-M sensors, temperature sensors, and an infrared camera. Data were acquired at 1 Hz intervals. Results showed that cable surface temperature exceeded 280 °C during degradation. The output voltage exhibited transient surges of up to +13.3% and drops of −68%, while the output current decreased by over 20%, particularly in the PV-M3 module. These anomalies, such as thermal imbalance, voltage spikes/dips, and current drops, were closely associated with critical degradation points and are interpreted as precursor signals. This work confirms the feasibility of identifying fire-related precursors through real-time monitoring of PV cable electrical characteristics. The observed correlation between electrical responses and thermal expansion behaviors suggests a strong link to the stages of insulation degradation. Future work will focus on quantifying the relationship between degradation and electrical behavior under controlled environmental conditions. Full article

This paper presents a DC microgrid architecture with autonomous decentralized control that exhibits high resilience against increasingly common threats, such as natural disasters and cyber-physical attacks, as well as its operational characteristics under normal circumstances. The proposed system achieves autonomous decentralized cooperative control by combining a battery-integrated DC baseline, in which multiple distributed small-scale batteries are directly connected to the grid baseline, with a weakly coupled grid architecture in which each power device is loosely coupled via the grid baseline. Unlike conventional approaches that assign grid formation, inertial support, and power balancing functions to DC/DC converters, the proposed approach delegates these fundamental grid roles to the distributed batteries. This configuration simplifies the control logic of the DC/DC converters, limiting their role to power exchange only. To evaluate system performance, a four-family DC microgrid model incorporating a typical Japanese home environment, including an EV charger, was constructed in MATLAB/Simulink R2025a and subjected to one-year simulations. The results showed that with approximately 5 kW of PV panels and a 20 kWh battery capacity per household, a stable power supply could be maintained throughout the year, with more than 50% of the total power consumption covered by solar energy. Furthermore, the predicted battery life was over 20 years, confirming the practicality and economic viability of the proposed residential microgrid design. Full article

This review presents a synthesis of fuzzy logic-based (FL) controllers applied to photovoltaic (PV) systems over the last decade, with a specific focus on maximum power point tracking (MPPT) and power management. These subsystems are critical for improving the efficiency of PV energy conversion, as they directly address the nonlinear, time-varying, and uncertain behavior of solar generation under dynamic environmental conditions. FL-based control has proven to be a powerful and versatile tool for enhancing MPPT accuracy, inverter performance, and hybrid energy management strategies. The analysis concentrates on three main categories, namely, Mamdani, Takagi–Sugeno (T-S), and Type-2, highlighting their architectures, operational characteristics, and application domains. Mamdani controllers remain the most widely adopted due to their simplicity, interpretability, and effectiveness in scenarios with moderate response time requirements. T-S controllers excel in real-time high-frequency operations by eliminating the defuzzification stage and approximating system nonlinearities through local linear models, achieving rapid convergence to the maximum power point (MPP) and improved power quality in grid-connected PV systems. Type-2 fuzzy controllers represent the most advanced evolution, incorporating footprints of uncertainty (FOU) to handle high variability, sensor noise, and environmental disturbances, thereby strengthening MPPT accuracy under challenging conditions. This review also examines the integration of metaheuristic algorithms for automated tuning of membership functions and hybrid architectures that combine fuzzy control with artificial intelligence (AI) techniques. A bibliometric perspective reveals a growing research interest in T-S and Type-2 approaches. Quantitatively, Mamdani controllers account for 54.20% of publications, T-S controllers for 26.72%, and Type-2 fuzzy controllers for 19.08%, reflecting the balance between interpretability, computational performance, and robustness to uncertainty in PV-based MPPT and power management applications. Full article