Reliability Of Photovoltaic Systems Research Articles

Studying the effects of temperature on InGaP/InGaAs/Ge triple junction solar cells using PyAMS Software

Multi-junction solar cells, particularly those utilizing III–V semiconductor materials, offer high conversion efficiency by employing multiple P-N junctions with different band gap energies to capture specific wavelengths of sunlight. Among these, the triple-junction photovoltaic cell, comprising Indium Gallium Phosphide (InGaP), Indium Gallium Arsenide (InGaAs), and Germanium (Ge) subcells, stands out for its efficiency. However, temperature variations significantly impact the performance of these subcells, necessitating a comprehensive understanding of their thermal behavior. In this study, we employ PyAMS (Python for Analog and Mixed Signals) software to model and simulate the behavior of InGaP/InGaAs/Ge sub cells under varying temperature conditions. Through comparison with experimental data, we validate our model and analyze key performance parameters, including short circuit current density (Jsc), open circuit voltage (Voc), fill factor (FF), and overall efficiency (η). Our findings reveal minimal changes in short circuit current density with temperature, while the open circuit voltage exhibits a substantial decrease beyond 60°C when exposed to concentrated illumination of 10 Suns, significantly impacting fill factor and efficiency. By elucidating the thermal behavior of three-junction solar cells, our study contributes valuable insights for designing and implementing cooling systems, thereby enhancing the performance and reliability of photovoltaic systems in practical applications.

An amended low-cost indirect MPPT strategy with a PID controller for boosting PV system efficiency

Operating a photovoltaic (PV) array at its optimum power point (MPP) despite fluctuating weather conditions is challenging. However, tracking this point enhances system efficiency and energy yields. Various maximum power point tracker (MPPT) techniques have been developed, differing in convergence speed, tracking efficiency, and implementation complexity. The Incremental Conductance (IC) strategy is popular due to its simplicity and reliability, but its fixed perturbation step size results in low tracking efficiency, MPP fluctuation, high power loss, and poor performance. This study proposes an improved IC (IMP-IC) method using indirect control based on a PID controller with an adjustable step size. The IMP-IC approach is simple, cost-effective, and implementable using a low-cost ATmega328 microcontroller on the Arduino Uno board.Simulations in the Proteus environment compared its performance to other MPPT techniques like conventional IC, zone voltage (ZV), fuzzy logic (FL), and novel adjustable step InC (NAS-INC). Results under various weather conditions, including rapid changes and EN50530 testing standards, indicate the IMP-IC approach outperforms existing methods. Specifically, it achieved an average tracking time of 0.12 seconds and tracking efficiencies of 99.40 % (static) and 99.88 % (dynamic). The IMP-IC method showed negligible power losses and fluctuations, a quick convergence speed and significantly lower implementation complexity. These attributes position the IMP-IC method as an effective solution for enhancing the reliability and performance of PV systems.

Autonomous Intelligent Monitoring of Photovoltaic Systems: An In‐Depth Multidisciplinary Review

ABSTRACTThis study presents a comprehensive multidisciplinary review of autonomous monitoring and analysis of large‐scale photovoltaic (PV) power plants using enabling technologies, namely artificial intelligence (AI), machine learning (ML), deep learning (DL), internet of things (IoT), unmanned aerial vehicle (UAV), and big data analytics (BDA), aiming to automate the entire condition monitoring procedures of PV systems. Autonomous monitoring and analysis is a novel concept for integrating various techniques, devices, systems, and platforms to further enhance the accuracy of PV monitoring, thereby improving the performance, reliability, and service life of PV systems. This review article covers current trends, recent research paths and developments, and future perspectives of autonomous monitoring and analysis for PV power plants. Additionally, this study identifies the main barriers and research routes for the autonomous and smart condition monitoring of PV systems, to address the current and future challenges of enabling the PV terawatt (TW) transition. The holistic review of the literature shows that the field of autonomous monitoring and analysis of PV plants is rapidly growing and is capable to significantly improve the efficiency and reliability of PV systems. It can also have significant benefits for PV plant operators and maintenance staff, such as reducing the downtime and the need for human operators in maintenance tasks, as well as increasing the generated energy.

Comparative Evaluation of Traditional and Advanced Algorithms for PV Systems in Partial Shading Conditions

The optimization of photovoltaic (PV) systems is vital for enhancing efficiency and economic viability, especially under Partial Shading Conditions (PSCs). This study focuses on the development and comparison of traditional and advanced algorithms, including Perturb and Observe (P&O), Incremental Conductance (IC), Fuzzy Logic Control (FLC), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Artificial Neural Networks (ANN), for efficient Maximum Power Point Tracking (MPPT). Simulations conducted in the MATLAB/Simulink software package evaluated these algorithms’ performances under various shading scenarios. The results indicate that, while traditional methods like P&O and IC are effective under uniform conditions, advanced techniques, particularly ANN-based MPPT, exhibit superior efficiency and faster convergence under PSCs. This study concludes that integrating Artificial Intelligence (AI) and Machine Learning (ML) into MPPT algorithms significantly enhances the reliability and efficiency of PV systems, paving the way for a broader adoption of solar energy technologies in diverse environmental conditions. These findings contribute to advancing renewable energy technology and supporting green energy transition.

A novel technique to detect and mitigate harmonic during islanding in grid connected PV system

The integration of photovoltaic (PV) systems into the power grid has become increasingly prevalent, providing sustainable and renewable energy solutions. However, the occurrence of islanding events, where a portion of the PV system operates independently from the main grid, presents challenges in maintaining power quality. This research paper investigates the harmonic distortions during islanding in grid-connected PV systems and proposes effective mitigation strategies. The study employs advanced simulation tools to analyse the dynamic behavior of the PV system during islanding scenarios, focusing on the occurrence and characteristics of harmonics. A detailed investigation into the root causes of harmonic distortions is conducted, considering factors such as grid fluctuations, inverter operation, and system impedance. Based on the findings, this paper introduces novel harmonic mitigation techniques tailored for islanding conditions. These techniques encompass advanced control algorithms, filter designs, and modulation strategies to suppress harmonics and enhance the overall system performance during islanded operation. Comparative assessments of different mitigation approaches are presented, evaluating their effectiveness in minimizing harmonic distortions and maintaining grid code compliance. The research contributes valuable insights to the field of grid-connected PV systems, offering a comprehensive understanding of harmonic issues during islanding events and proposing innovative solutions for their mitigation. The outcomes of this study are crucial for advancing the reliability, stability, and power quality of PV systems that are connected to the grid, which makes it possible for green energy sources to be added to the grid without any problems.

Experimental investigation of a nano coating efficiency for dust mitigation on photovoltaic panels in harsh climatic conditions

Dust accumulation on photovoltaic (PV) panels in arid regions diminishes solar energy absorption and panel efficiency. In this study, the effectiveness of a self-cleaning nano-coating thin film is evaluated in reducing dust accumulation and improving PV Panel efficiency. Surface morphology and elemental analysis of the nano-coating and dust are conducted. Continuous measurements of solar irradiances and ambient temperature have been recorded. SEM analysis of dust revealed irregularly shaped micron-sized particles with potential adhesive properties, causing shading effects on the PV panel surface. Conversely, the coating particles exhibited a uniform, spherical shape, suggesting effective prevention of dust adhesion. Solar irradiance ranged from 120 W/m² to a peak of 720 W/m² at noon. Application of the self-cleaning nano-coating thin film consistently increased short circuit current (Isc), with the coated panel averaging 2.8 A, which is 64.7% higher than the uncoated panel’s 1.7 A. The power output of the coated panel ranged from 7 W to 38 W, with an average of approximately 24.75 W, whereas the uncoated panel exhibited a power output between 3 W and 23 W, averaging around 14 W. These findings highlight the substantial potential of nano-coating for effective dust mitigation, particularly in dusty environments, thus enhancing PV system reliability.

Long-term performance analysis of a large-scale photoVoltaic plant in extreme desert conditions

This study comprehensively evaluates the performance and operational challenges of a 9 MW grid-connected photovoltaic (PV) system in Timmimoun, southern Algeria, after eight years of operation. We identified a significant linear correlation (R2 = 0.73) between increased ambient temperatures and decreased PV module efficiency, underscoring the need for effective thermal management strategies. The investigation also examines potential solutions, such as cooling mechanisms, optimization of panel orientation and tilt angles, and development of temperature-resistant materials. Adhering to the International Energy Agency IEC 61724 standard, our analysis reveals a yield factor of 5.04 h/day, a yield ratio of 7.04 h/day, a performance ratio of 73 %, and a capacity factor of 21 % in 2023. Over its operational lifespan, the system has generated 133.43 GW h of energy, mitigating 1.01 105 tons of CO2 emissions. This long-term study provides critical insights into the performance and reliability of PV systems in hot desert climates, offering valuable guidance for future large-scale solar installations and contributing to the transition towards a sustainable energy future.

A Deep Learning-Based Approach for Identifying Defects in Solar Panels

In the solar energy sector, the task of monitoring and maintaining large photovoltaic (PV) system portfolios is essential for ensuring optimal performance and reliability. Prominent solar energy companies face challenges with their current fault detection methods, which are inefficient and resource- intensive. This paper addresses the critical need for improved fault detection in solar PV systems to maximize uptime and minimize maintenance costs. We employed advanced data preprocessing and augmentation techniques using Roboflow and developed a YOLOv8 segmentation model in Google Colab with GPU. This model was then deployed using Streamlit, providing a robust solution for identifying faulty solar modules. The proposed approach significantly enhances fault detection accuracy, achieving a minimum accuracy rate of 85%, thus ensuring reliable operation of the PV systems. Additionally, the implementation of this model contributes to a 15% reduction in system downtime and a 10% reduction in maintenance costs. By leveraging advanced machine learning techniques, our solution transforms the maintenance process, making it more efficient and cost-effective. Consequently, this work not only improves the reliability and performance of solar PV systems but also supports the broader goal of sustainable energy through more efficient resource usage.

Reliability and criticality analysis of a large-scale solar photovoltaic system using fuzzy-fault tree analysis approach

Over time, solar Photovoltaic (PV) systems experience a decline in performance and reliability due to various environmental factors. Fault Tree Analysis (FTA) can be used to assess the reliability of these systems and identify faults and failure modes that can significantly impact the entire PV system’s performance. However, in practice, obtaining accurate failure probability values for the components of a solar PV system is challenging since systems operate in an ever-changing environment, resulting in a scarcity of data for statistical estimation. This paper proposes a fuzzy theory-based FTA approach to obtain the failure probabilities of the faults more accurately. The Fuzzy-FTA methodology converts experts’ subjective opinions expressed in linguistic terms into a Failure Possibility Score (FPS), which is then converted into a failure probability value. The results of the proposed approach are compared to those obtained through the conventional FTA to assess its effectiveness, applicability, feasibility, and efficiency. Soiling, dust accumulation, inadequate system maintenance, bad system configuration, bird dropping, delamination, improper installation, shading, grounding, and discoloration are the most critical faults and impact on the performance and reliability of solar PV systems.

Improved fault detection and classification in PV arrays using stockwell transform and data mining techniques

The growing integration of photovoltaic (PV) systems into the power grid necessitates reliable fault detection and classification mechanisms to ensure operational efficiency and safety. Fault detection in photovoltaic (PV) arrays is crucial for maintaining optimal system performance and ensuring the reliability of solar power generation. This paper proposes a novel approach for fault detection in PV arrays by employing the Stockwell transform in combination with various data mining techniques. The Stockwell transform is an advanced time-frequency analysis tool that allows for enhanced feature extraction from time-series data. By applying the Stockwell transform to the PV array's operational data, valuable frequency-domain information is extracted, enabling the identification of subtle fault signatures. To effectively detect and classify various faults, different data mining techniques, such as support vector machines, decision trees, random forests, and k-nearest neighbours, are applied to the transformed data. Each technique's effectiveness in identifying faults is evaluated and compared, enabling the selection of the most suitable algorithm for the specific application. Experimental results demonstrate the effectiveness of the proposed fault detection approach, exhibiting high accuracy, sensitivity, and specificity in identifying various types of faults in PV arrays. Extensive simulations and experimental validations were conducted on various fault conditions, including partial shading, open-circuit faults, and degradation. The results demonstrate the proposed method's superior performance, achieving an accuracy of 99.61 %, precision of 99.75 % and F1 score of 98.73 %. These metrics significantly surpass traditional fault detection techniques, highlighting the method's potential for real-world deployment. The approach not only enhances the reliability of PV systems but also contributes to reducing maintenance costs and improving system efficiency. The combination of the Stockwell transforms with data mining techniques proposed here provides a robust and efficient framework for early detection of faults, enabling timely maintenance and minimizing energy losses.

Polymer encapsulation impact on potential-induced degradation in PV modules revealed by a multi-modal field study

Potential-induced degradation is a significant issue affecting the performance and reliability of photovoltaic systems, particularly in large-scale installations. Understanding the mechanisms underlying the formation of potential-induced degradation is crucial for developing effective mitigation strategies and ensuring the long-term sustainability of solar energy technologies. During the inspection of a 1.2 MWp photovoltaic power plant, various imaging methods, electrical measurements, spectroscopic characterization methods, and monitoring data analyses were utilized and combined. We observed that two percent of the photovoltaic modules at the string ends exhibited the characteristic checkerboard pattern in infrared or electroluminescence imaging, implicating issues on twelve percent of all strings. This uneven distribution of potential-induced degradation-affected modules suggests additional key factors are at play. Investigations of the backsheet and ethylene-vinyl acetate using near-infrared reflectance analysis revealed that one backsheet type and two different ethylene-vinyl acetate types were present in the installed solar panels. Yet, only one type of ethylene-vinyl acetate correlated with potential-induced degradation. The same type also exhibited a higher rate of oxidative degradation. Scanning electron microscopy and energy-dispersive X-ray spectroscopy revealed a significantly increased sodium ion content in the ethylene-vinyl acetate and glass of the potential-induced degradation-affected modules. Our findings highlight the crucial role of polymer encapsulation in the development of potential-induced degradation and underscore the importance of careful polymer selection in the design of photovoltaic module technologies.

Photovoltaic Energy Anomaly Detection using Transformer Based Machine Learning

This study uses the Anomaly Transformer model to find anomalies in photovoltaic energy generation in Malang, Indonesia. The main background of this study is the lack of satellite monitoring in this region and the importance of annual data for electricity generation forecasting. Temperature scattered direct solar radiation, and hourly electricity production are all part of the dataset used which is only available since 2019. Anomalies were detected at 05.00 and 16.00 WIB, indicating instability in the energy supply due to high temperatures in the morning and heavy rain in the afternoon. Detection of these anomalies is important to improve the efficiency and reliability of photovoltaic systems, reduce operational costs, and reduce the risk of system failure. Indonesia has many challenges for photovoltaic energy generation due to its unique location, with many islands located close to the equator. The use of the Anomaly Transformer algorithm improves the accuracy of anomaly detection over conventional methods. This algorithm helps to find complex patterns in very large time series. The results show that the anomaly transformer model can effectively detect anomalous patterns. It offers ideas to improve the stability and efficiency of photovoltaic systems in Malang and other areas with comparable environmental conditions. Improved energy efficiency and environmental sustainability are the results of anomaly pattern detection.

Impact of dust and temperature on photovoltaic panel performance: A model-based approach to determine optimal cleaning frequency

Enhancing the reliability of photovoltaic (PV) systems is of paramount importance, given their expanding role in sustainable energy production, carbon emissions reduction, and supporting industrial growth. However, PV panels commonly encounter issues that significantly impact their performance. Specifically, the accumulation of dust and the rise in internal temperature lead to a drop in energy production efficiency. The primary issue addressed in this paper is using mathematical modeling to determine the optimal cleaning frequency. This paper first focuses on stochastic modeling for dust accumulation and temperature changes in PV panels, considering varying environmental conditions and proposing a model-based approach to determine the optimal cleaning frequency. Dust accumulation is described using a Non-homogeneous compound Poisson process (NHCPP), while temperature evolution is modeled using Markov chains. Within this framework, we consider the impact of wind speed and rainfall on dust accumulation and temperature. These factors, treated as covariates, are modeled using a two-dimensional time-continuous Markov chain with a finite state space. A Condition-based cleaning policy is proposed and assessed based on the degradation model. Optimal preventive cleaning thresholds and cleaning frequency (periodic and non-periodic) are determined to minimize the long-term average maintenance cost. The gain achieved by non-periodic inspections compared to periodic inspections ranges from 3.83% to 9.37%. Numerical experiments demonstrate the performance of the proposed cleaning policy, highlighting its potential to improve PV system efficiency and reliability.

Economics of Snow Accumulation on Photovoltaic Modules

The growth in photovoltaic (PV) module installations over the past decade has prompted a critical need to examine the economic implications of snow accumulation on solar energy production. The aim of this study is to quantify the economic impact of snow accumulation on PV modules in different regions and environmental conditions and to identify effective mitigation strategies for enhancing power generation efficiency and reliability of PV systems. It was found that snow accumulation on PV modules can lead to annual losses of 1% to 12% depending on the environmental conditions and geographic location. A financial analysis related to maintenance costs associated with snow accumulation on PV modules is also presented. A two-fold methodology of quantitative data analysis and interviews conducted with PV system operators is used for this purpose. In addition, the extent of snow accumulation financial losses in the U.S. is categorized based on the snowfall amount and solar market segment, suggesting an annual loss of at least USD 313M in utility and residential solar sectors. Furthermore, various currently employed active and passive snow mitigation strategies are presented in detail, describing their shortcomings and advantages. Finally, prospects on the need for developing reliable and cost-effective snow mitigation strategies for solar panels are discussed, paving the path for future studies.

Economic Consequences Based on Reversible and Irreversible Degradation of PV Park in the Harsh Climate Conditions of Iraq

Photovoltaic (PV) system reliability and durability investigations are essential for industrial maturity and economic success. Recently, PV systems received much interest in Iraq due to many reasons—for instance, power shortage, global warming and pollution. Despite this interest, the precise economic implications of PV system reliability in harsh climates like Iraq remain uncertain. This work assesses the economic implications of PV system component reliability and soiling in Iraq using field experience and historical data. This study identifies the most common failure modes of PV systems installed in Iraq and similar climatic regions, and also ranks their severity. Simulations explore scenarios of PV module degradation rates, inverter lifetimes, soiling rates, and cleaning intervals, revealing that soiling has the most detrimental effect, with cleaning frequency leading to Levelized Cost of Electricity (LCOE) losses of over 30%, depending on the location. Inverter lifetime contributes to LCOE losses between 4 and 6%, depending on the PV system’s location. This study also evaluates the impact of tilt angle as a mitigation strategy for reducing soiling loss and its economic implications, finding that installing PV modules at higher tilt angles could reduce the economic impact of soiling by approximately 4.4%. Additionally, the optimal cleaning strategy identified is fully automated dry cleaning with robots, outperforming other strategies economically. Overall, the findings highlight that the LCOE in Iraq is relatively high compared to the global weighted average for utility-scale PV systems, primarily due to high soiling and degradation rates. The LCOE varies within the country, influenced by different degradation rates. This study aims to assist PV stakeholders in Iraq and the Middle East and North Africa (MENA) region in accurately estimating solar bankability; moreover, increasing reliability by minimizing the technical and financial risks by considering key parameters specific to these regions.

Improved Mathematical Modelling and Statistical Assessment of Performance of Photovoltaic System under Non-Linear Operational Condition

It is study presents an advanced mathematical model that captures the non-linear operational characteristics of photovoltaic (PV) systems, enhancing the understanding and optimization of their performance under varying environmental conditions. By integrating principles of non-linear dynamics and statistical analysis, the model precisely simulates the performance responses of PV systems to non-linear inputs such as irradiance fluctuations, temperature variations, and load changes. We utilized a comprehensive dataset collected from multiple PV installations to validate our model, ensuring robustness and applicability. The results demonstrate a significant improvement in predicting system behaviors, which facilitates more effective management and optimization strategies. Statistical tools were employed to assess the reliability and efficiency of PV systems, revealing key insights into their operational dynamics. Our findings contribute to the development of more resilient and efficient solar energy systems, offering a valuable resource for researchers and practitioners in the field of renewable energy technologies.

A Reliability and Risk Assessment of Solar Photovoltaic Panels Using a Failure Mode and Effects Analysis Approach: A Case Study

Solar photovoltaic (PV) systems are becoming increasingly popular because they offer a sustainable and cost-effective solution for generating electricity. PV panels are the most critical components of PV systems as they convert solar energy into electric energy. Therefore, analyzing their reliability, risk, safety, and degradation is crucial to ensuring continuous electricity generation based on its intended capacity. This paper develops a failure mode and effects analysis (FMEA) methodology to assess the reliability of and risk associated with polycrystalline PV panels. Generalized severity, occurrence, and detection rating criteria are developed that can be used to analyze various solar PV systems as they are or with few modifications. The analysis is based on various data sources, including field failures, literature reviews, testing, and expert evaluations. Generalized severity, occurrence, and detection rating tables are developed and applied to solar panels to estimate the risk priority number (RPN) and the overall risk value. The results show that the encapsulant, junction box, and failures due to external events are the most critical components from both the RPN and risk perspectives. Delamination and soiling are the panels’ most critical FMs, with RPN values of 224 and 140, respectively, contributing 16.2% to the total RPN. Further, moderately critical FMs are also identified which contribute 56.3% to the RPN. The encapsulant is the most critical component, with RPN and risk values of 940 (40.30%) and 145 (23.40%), respectively. This work crucially contributes to sustainable energy practices by enhancing the reliability of solar PV systems, thus reducing potential operational inefficiencies. Additionally, recommendations are provided to enhance system reliability and minimize the likelihood and severity of consequences.

Enhancing photovoltaic systems using Gaussian process regression for parameter identification and fault detection

In recent years, significant efforts have been made to enhance the power-harnessing capacity of photovoltaic (PV) systems. Despite advancements, PV systems remain less efficient. Faults occurring in the PV system are one of the major reasons for reduced output power. Improving PV system efficiency reduces overall costs and extends the system’s operational lifespan. This research utilizes a machine learning-based non-linear Gaussian posterior distribution (GPR) algorithm for detecting, classifying, and localizing various faults in PV systems. The proposed technique uses solar cell parameters to classify the faults based on the severity levels. The experimental results validate the effectiveness of the proposed approach, emphasizing its potential to improve PV system performance and reliability. The model statistics show that training time for squared exponential GPR is shorter than exponential and rational quadratic GPR models, with squared exponential GPR taking 2771.5 s, while exponential GPR and rational quadratic GPR took 4227.6 s and 6660.5 s, respectively. Despite longer training time, rational quadratic GPR has the lowest root mean squared error (RMSE) validation, ranging from 0.016598 to 0.025616, among the three approaches. Exponential GPR, with an RMSE range of 363.59 to 397.1 across various faults, was chosen for its superior performance when compared to the other approaches.

Enhancing grid-connected photovoltaic system performance with novel hybrid MPPT technique in variable atmospheric conditions

This paper proposes an innovative approach to improve the performance of grid-connected photovoltaic (PV) systems operating in environments with variable atmospheric conditions. The dynamic nature of atmospheric parameters poses challenges for traditional control methods, leading to reduced PV system efficiency and reliability. To address this issue, we introduce a novel integration of fuzzy logic and sliding mode control methodologies. Fuzzy logic enables the PV system to effectively handle imprecise and uncertain atmospheric data, allowing for decision-making based on qualitative inputs and expert knowledge. Sliding mode control, known for its robustness against disturbances and uncertainties, ensures stability and responsiveness under varying atmospheric conditions. Through the integration of these methodologies, our proposed approach offers a comprehensive solution to the complexities posed by real-world atmospheric dynamics. We anticipate applications in grid-connected PV systems across various geographical locations and climates. By harnessing the synergistic benefits of fuzzy logic and sliding mode control, this approach promises to significantly enhance the performance and reliability of grid-connected PV systems in the presence of variable atmospheric conditions. On the grid side, both PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) algorithms were employed to tune the current controller of the PI (Proportional-Integral) current controller (inverter control). Simulation results, conducted using MATLAB Simulink, demonstrate the effectiveness of the proposed hybrid MPPT technique in optimizing the performance of the PV system. The technique exhibits superior tracking efficiency, achieving a convergence time of 0.06 s and an efficiency of 99.86%, and less oscillation than the classical methods. The comparison with other MPPT techniques highlights the advantages of the proposed approach, including higher tracking efficiency and faster response times. The simulation outcomes are analyzed and demonstrate the effectiveness of the proposed control strategies on both sides (the PV array and the grid side). Both PSO and GA offer effective methods for tuning the parameters of a PI current controller. According to considered IEEE standards for low-voltage networks, the total current harmonic distortion values (THD) obtained are considerably high (8.33% and 10.63%, using the PSO and GA algorithms, respectively). Comparative analyses with traditional MPPT methods demonstrate the superior performance of the hybrid approach in terms of tracking efficiency, stability, and rapid response to dynamic changes.

Numerical Simulation and Mathematical Analysis of Meta Heuristic MPPT System for Solar Photovoltaic Applications Under Non-Linear Operational Conditions

As a sustainable energy source, the use of solar photovoltaic (PV) systems has significantly increased. Environmental elements, especially partial shadowing circumstances, have a considerable impact on how well solar PV systems function. In such cases, the various local maxima in the power-voltage curve make it difficult for conventional Maximum Power Point Tracking (MPPT) methods to maintain peak efficiency. Heuristic Maximum Power Point Tracking (MPPT) system for solar photovoltaic (PV) applications, employing the cuckoo search algorithm. The primary objective of the research is to enhance the efficiency and reliability of solar PV systems by optimizing the MPPT process, which is crucial for maximizing energy extraction under varying environmental conditions. The model is used to simulate the performance of the system under various environmental conditions, such as changes in temperature and irradiance levels. The simulation results are then statistically analyzed to evaluate the effectiveness of the cuckoo search algorithm in tracking the maximum power point accurately and rapidly. The algorithm demonstrates a robust ability to converge to the maximum power point efficiently, thereby enhancing the overall energy yield of the solar PV system.. The performance of the hybrid PSO-CSA MPPT algorithm in contrast to traditional MPPT techniques is assessed through simulations and tests under various shading circumstances. The findings show that the hybrid strategy regularly outperforms conventional methods by enhancing the total energy production of partially shadowed solar PV installations through faster convergence, less oscillations, and greater tracking accuracy. The proposed hybrid algorithm also demonstrates stability and flexibility in real-world settings, making it a potential option for boosting the dependability and efficiency of solar PV systems when shade is present. This study advances the field of renewable energy and prepares the path for the use of sophisticated optimization methods to the problems of solar PV power generation under changing environmental circumstances.