Operation Of Photovoltaic System Research Articles

Investigating the Impact of Partial Shading on Photovoltaic Panels and Enhancing their Efficiency using the Python

This research project focuses on investigating the impact of partial shading on photovoltaic (PV) panels and proposes methods to enhance their efficiency using Python programming. Partial shading can significantly reduce the performance of PV panels by creating imbalances in current and voltage outputs. By leveraging Python's computational capabilities, this study aims to develop simulation models and algorithms that accurately capture the behavior of shaded PV panels. The objectives of this research include building a comprehensive understanding of partial shading effects, developing a Python-based simulation framework, analyzing the impact of different shading patterns on PV panel efficiency, investigating novel techniques to enhance efficiency using Python, and evaluating proposed approaches through simulations and experimental validation. Through this investigation, we aim to contribute to the development of improved strategies for the design and operation of PV systems. By mitigating the negative effects of shading and enhancing PV panel efficiency, we can further promote the adoption of sustainable solar energy solutions. The outcomes of this research have the potential to advance the field of solar energy and facilitate a greener and cleaner future. Also, this paper aims to investigate the impact of partial shading on photovoltaic (PV) panels and explores methods to enhance their efficiency using Python. Partial shading can significantly reduce the overall output of PV systems, leading to suboptimal performance. By analyzing shading patterns and implementing intelligent algorithms, we can optimize PV panel configuration and improve their efficiency. In this study, the Python programming language will be utilized to develop simulation models, perform data analysis, and propose solutions for increasing the efficiency of shaded PV panels.

A comprehensive analysis of environmental factors affecting solar cells: Dust accumulation, ambient temperature, and humidity

This paper aims to investigate the impact of environmental factors on solar cells, focusing on dust accumulation, ambient temperature, and humidity. The utilization of solar cells as a renewable source of energy has garnered significant attention in recent years, with three main types of solar cells: silicon-based, thin-film, and calcium titanium ore solar cells. While each type possesses unique strengths and weaknesses, understanding the impact of environmental factors is essential for monitoring quality and assessing their performance. Dust accumulation on photovoltaic (PV) panels reduces output power by blocking solar radiation transmission, causing uneven shading and reducing heat dissipation. High temperatures also negatively impact PV system operation by reducing the operating voltage of the cells, while humidity can block or diffract sunlight, reducing the output power of the system. This paper provides an analysis of existing literature and empirical studies, aiming to provide valuable insights into improving the efficiency and durability of solar cells and contributing to the widespread adoption of this renewable energy source.

Concurrent UPQC Schemes for the Power Quality Improvement in Solar Photovoltaic Systems

Aims: Aim of this research is to propose a novel concurrent UPQC scheme for improving the power quality issues in grid integrated solar photovoltaic (PV) systems. Background: The power quality is a major issue for the grid integration of renewable energy sources. Issues like voltage sag, voltage swell, harmonics & non-linear load variations are certainly observed in the distributed energy system and it is mandated that any system has to depend on advanced controllers to improve power quality and stabilize the electrical parameters. Controller related power quality improvements are a bit easier to design but the tuning of power is difficult in this aspect. Objective: In order to overcome the aforesaid limitation, this particular paper proposes a new concurrent UPQC scheme for improving the four different power quality issues in grid integrated solar photovoltaic (PV) system such as voltage sag, voltage swell, non-linear load variations and current harmonics. Methods: The operating regions of each power quality issues are examined in the I-V curves of PV specifications and the new operating modes of PV systems are mapped for every quality improvement considering the power, current and frequency of the grid and load as well. The pool of solutions is developed from the real power, reactive power and converter duty cycle and verified with the proposed solutions. Additionally, the designed switching frequency of the proposed system has a 5% variation for practical irradiance. The PV uniform irradiance profile matches the real power for various proposed concurrent UPQC schemes. Results: Finally, the simulation results are presented to validate the operation of the proposed concurrent UPQC schemes for PV system. The comparative study of the proposed concurrent UPQC scheme for PV system with appropriate literature is presented. The superiority of the proposed schemes infers studying the odd harmonic components up to 100th order after implementing the proposed concurrent UPQC scheme for PV system. Conclusion: From the measured results, it is concluded that a new concurrent optimization scheme enhances the operation of solar PV system that integrates with the grid. The power quality issues like voltage & current swell, voltage & current sag, voltage imbalance, and harmonics are reduced compared to existing methods. The validation of the schemes is achieved through the group constraints and the operating slopes in each region.

Fast fault detection method for photovoltaic arrays with adaptive deep multiscale feature enhancement

Photovoltaic (PV) arrays have output characteristics such as randomness and intermittency, and faults can seriously affect the safe operation of the power system. In order to improve the comprehensive performance of the PV array fault diagnosis model, a new intelligent online fault monitoring method for PV arrays is proposed in this paper. (1) a three-dimensional channel feature map based on I, V, and P features is constructed because the I-V and P curves of the PV array have significantly different effects under different fault conditions. (2) The PV array fault diagnosis model based on a multi-source information fusion network (MIFNet) is proposed, and Channel Mixing Convolution (CMC) module, three-dimensional feature attention enhancement (TDFAE) module, and Channel normalized scaling (CNS) module are designed to improve the comprehensive performance of the model. (3) An adaptive nonlinear mutual sparrow search algorithm (ANMSSA) is proposed to optimize the hyperparameter configuration of the MIFNet network. The experimental results show that the average recognition accuracy, prediction accuracy, and sensitivity of the ANMSSA-MIFNet network proposed in this paper are 99.64%, 99.64%, and 99.71% respectively. When facing single-component faults and multi-component faults, the model has stronger diagnostic accuracy, robustness, anti-noise ability, and stability, and can efficiently diagnose different faults of PV arrays, providing the scientific basis and theoretical support for the operation of PV systems.

Modeling and Experimental Studies of the Photovoltaic System Performance in Climate Conditions of Poland

The polycrystalline silicon photovoltaic system located in Poland has been investigated from a modeling and an experimental perspective. The five-parameter single-diode (SD) model was used to compute the current–voltage (I-V) characteristics of the PV modules for weather conditions measured during one year (2022) of PV system operation. Based on the I-V curves, the PV power output, monthly energy yields, and performance were simulated. Besides the single-diode method, the Osterwald model (OM) was used to estimate the power output of the PV system under scrutiny. The modeling results were compared to the experimental data. The determination coefficient (R2), root mean square error (RMSE), mean bias error (MBE), and relative error (RE) were utilized to quantify the quality of both models. The highest R2 value of 0.983 (power output) was found for March, a relatively cold and sunny month in the analyzed period. The lowest values of the RMSE and the MBE were found to be 5% and 1%, respectively. A high correlation between the modeled and the experimental daily yield was noticed in June, which was the sunniest month of the year. Median values were found to be 5.88 kWh/kW (measurement), 5.87 kWh/kW (SD), and 5.87 kWh/kW (OM). The RE of the monthly array yield was found to be below 1% (summer half-year) in terms of the single-diode method. The strong correlation between the simulated and the experimental findings was also noticed for the medians of the DC performance ratio (PRDC). The median values of the PRDC from May to July were found to be in the range between 0.88 and 0.94.

Parameter identification of photovoltaic converter models

The work aims to develop a simple and effective method for identifying the parameters of photovoltaic converter (PV-cell) models for their wide practical application. An exponential model having one diode and five parameters is adopted as the basic model of the photovoltaic converter to facilitate its easy integration into MatLab/Simulink simulation software. To identify the parameters of models of photovoltaic converters, an original method based on finding the minimum of the root-mean-square error between model and experimental volt-ampere characteristics was used. The effectiveness of the method was confirmed by determining the parameters of different models of photovoltaic modules based on various technologies: thin-film Shell ST40, monocrystalline Shell SP70, and polycrystalline Kyocera KC200GT. A developed technique for constructing mathematical models of photovoltaic converters in order to reproduce their electrical characteristics is presented. The solution is easily implemented in Excel spreadsheet software with the “Search for Solution” add-on installed. The proposed universal method for identifying the parameters of photoconverter models can be used to build models of a wide range of photovoltaic modules and solar cells. The model current-voltage characteristics of the considered photovoltaic modules are shown to be in good agreement with experimental characteristics across a wide range of temperature and insolation conditions corresponding to their actual operation. Higher accuracy is achieved when using the proposed identification method to determine the electrical parameters of mathematical models of photovoltaic converters than when using other well-known analytical and numerical methods. The result is a simple and effective method for constructing mathematical models of photovoltaic converters, which does not require the use of programming or the development of specialized numerical algorithms, allowing it to be used to solve many technical problems related to the design and operation of photovoltaic systems.

Quality control test for unreliable meteorological and electrical photovoltaic measurements

<p>Meteorological and electrical measurements using predictive computational techniques have been used in the analysis of photovoltaic system operation and maintenance. International standards establish general and no standardized criteria on the quality control and validation of these measurements. In the present work, a methodology has been developed to correct erroneous photovoltaic experimental measurements: radiation, temperature, current, and voltage. We validated the proposed approach with 12 case studies with more than 5,000 meteorological and electric measurements from an experimental 3 kWp photovoltaic system. The approach is based on a set of non-intrusive criteria developed from the one diode model, the approach allowed to correct about 80% of the erroneous data, 30% more using polynomial regression. As for the regression methodology, we have shown that the proposed methodology includes 4 meteorologicalelectrical variables allowing a more rigorous analysis. For 75% of the cases evaluated, the proposed methodology achieves a better data correction.</p>

Anomaly detection using K-Means and long-short term memory for predictive maintenance of large-scale solar (LSS) photovoltaic plant

Detecting and monitoring faults in solar photovoltaic (PV) systems is crucial to ensure optimal efficiency and prevent safety and fire hazards. However, the conventional operation and maintenance (O&M) of solar PV systems do not utilize machine learning for fault detection and classification. This poses challenges for plant operators, especially those managing large-scale solar (LSS) PV plants, who typically rely on manual approaches to screen large amounts of electrical data and inspect numerous string panels. Consequently, the cost of O&M is high. This study aims to use advanced machine-learning techniques to detect anomalies in a Large-Scale Photovoltaic (LSSPV) plant. The study collects data from the plant which located in the central of Peninsular Malaysia and employs K-Means for clustering and Long-Short Term Memory (LSTM) for anomaly detection in the predicted electrical current of string modules. The model is developed using Jupyter Notebook from the Python Package Index. To validate the accuracy of the proposed model, the study compares LSTM with Artificial Neural Network (ANN) using relative error as the evaluation metric. The results indicate that the LSTM method identifies anomalies in the predicted output current of the string modules more accurately and with lower relative error than the conventional ANN technique. The proposed model could help plant operators perform predictive maintenance of LSSPV plants at a minimal cost and time.

Exploring Optimal Charging Strategies for Off-Grid Solar Photovoltaic Systems: A Comparative Study on Battery Storage Techniques

This paper presents a comparative analysis of different battery charging strategies for off-grid solar PV systems. The strategies evaluated include constant voltage charging, constant current charging, PWM charging, and hybrid charging. The performance of each strategy is evaluated based on factors such as battery capacity, cycle life, DOD, and charging efficiency, as well as the impact of environmental conditions such as temperature and sunlight. The results show that each charging strategy has its advantages and limitations, and the optimal approach will depend on the specific requirements and limitations of the off-grid solar PV system. This study provides valuable insights into the performance and effectiveness of different battery charging strategies, which can be used to inform the design and operation of off-grid solar PV systems. This paper concludes that the choice of charging strategy depends on the specific requirements and limitations of the off-grid solar PV system and that a careful analysis of the factors that affect performance is necessary to identify the most appropriate approach. The main needs for off-grid solar photovoltaic systems include efficient energy storage, reliable battery charging strategies, environmental adaptability, cost-effectiveness, and user-friendly operation, while the primary limitations affecting these systems encompass intermittent energy supply, battery degradation, environmental variability, initial investment costs, fluctuations in energy demand, and maintenance challenges, emphasizing the importance of careful strategy selection and system design to address these factors. It also provides valuable insights for designing and optimizing off-grid solar PV systems, which can help to improve the efficiency, reliability, and cost-effectiveness of these systems.

Modeling and simulation of a 16.20 kWp on-grid solar Photovoltaic System (PV) using PVSyst at Malaysia

Renewable energy is very useful alternative source to replace the usage of conventional energy. Solar energy is one of popular energy that has been utilised as its irradiance can generate electricity when strike with the photovoltaic solar (PV) panels. This paper presents a design and simulation of 16.20 kWp of 405W Si-mono PV system connected to the grid. It is very crucial to do the simulation in order to do the analysis in terms of performance, efficiency, power losses, irradiance and other related factors. The design, operation, and maintenance of new grid-connected solar PV systems may benefit from performance analysis. PVsyst is used as the design and simulation tools for the PV solar system simulation. It was developed by the University of Geneva as a pedagogical tool to explain in detail the procedures and models used, as well as to provide an ergonomic approach to project development. The site involves is Kampung Bukit Tinggi. From the simulation results, the array nominal energy is 25.81 MWh. The output inverter energy available is 21.59 MWh which fed to the grid. The performances ratio is 80.21% due to the several losses. This study may be useful for future grid-connected design and operation.

Improved Secant-Based Global Flexible Power Point Tracking in Photovoltaic Systems Under Partial Shading Conditions

Flexible power point tracking (FPPT) control algorithms have been introduced in the literature to provide frequency support in low-inertia grid-connected photovoltaic (PV) systems. This is achieved by regulating the PV output power to a power reference obtained from the energy management system. Partial shading of PV arrays is a frequent phenomena that needs to be investigated for FPPT operation of PV systems. This paper proposes a simple and improved secant-based global FPPT algorithm that tracks the power reference during partial shading conditions. Guaranteed and fast convergence with flexibility to operate on both sides of the global maximum power point are the main contributions of the proposed algorithm. Experimental validation is carried out to demonstrate the efficacy of the proposed algorithm.

Solar irradiance estimation and optimum power region localization in PV energy systems under partial shaded condition

The efficient operation of PV systems relies heavily on maximum power point tracking (MPPT). Additionally, such systems demonstrate complex behavior under partial shading conditions (PSC), with the presence of multiple maximum power points (MPP). Among the existing MPPT algorithms, the conventional perturb and observe, and incremental conductance stand out for their high simplicity. However, they are specialized in single MPP problems. Thus, due to the existence of multiple MPPs under PSC, they fail to track the global MPP. Compared with the conventional schemes, the modified conventional algorithms, and several existing MPPT variants introduce a trade-off between complexity and performance. To enhance the simplicity of the PV system, it is crucial to adapt the operation of the simple conventional algorithm to scenarios under PSC. To achieve such an adaptation, the power-voltage curve that conventionally admits multiple MPPs under PSC must be converted to an equivalent curve having only a single MPP. To address such a requirement, this paper introduces a novel approach to the fast determination of the MPP. A consistent methodology for reducing the complex multiple MPP problem of PV systems under PSC, to a single MPP objective, is put forward. Thus such reduction enhances the tracking environment for simple conventional MPPT algorithms under partial shading. Studies of the PV array behavior for 735 partial shading patterns revealed an interesting possibility of reducing the classical PV curve to 8.2620% of its actual area. The newly established area is an optimum power region that accommodates a single MPP. To arrive at such a reduction, an intelligent neural network-based predictor, incorporating a cost-effective and reliable solar irradiance estimator is put forward. Unlike existing methods, the approach is free from the direct and expensive measurement of solar irradiance. The predictor relies on the PV array current and voltage only to precisely determine the optimum power region of the PV system.

Salp swarm optimization algorithm based MPPT design for PV-TEG hybrid system under partial shading conditions

This paper proposes an innovative strategy to integrate thermoelectric generator (TEG) and photovoltaic (PV) systems, aiming to enhance energy production efficiency by addressing the significant waste heat generated during traditional PV system operation. Additionally, photovoltaic-thermoelectric generator (PV-TEG) hybrid system encounters the dual challenge of partial shading conditions (PSC) and non-uniform temperature distribution (NTD). Thus, salp swarm optimization (SSA) is introduced to simultaneously tackle the negative impacts of PSC and NTD. In contrast to alternative meta-heuristic algorithms (MhAs) and conventional mathematical approaches, the streamlined and effective optimization mechanism inherent to SSA affords a shorter optimization time, while mitigating the risk of the PV-TEG hybrid system’s optimization outcomes being confined to local maximum power points (LMPP). Furthermore, the optimization performance of SSA for PV-TEG hybrid systems is assessed via four case studies, including start-up test, stepwise variations in solar irradiation at constant temperature, stochastic change in solar irradiation, and field measured data for typical days in Hong Kong, in which simulation results show that SSA evinces unparalleled global exploration and local search capabilities, yielding heightened energy output (up to 43.75%) and effectively suppressing power fluctuations in the PV-TEG hybrid system (as evidenced by ΔVavg and ΔVmax).

Development of an Advanced Optimization and Optimal Control Mathematical Model for Energy-Efficient Operations of Renewable Energy

The potential for developing energy-efficient operations for renewable energy systems has increased with the expansion of renewable energy sources. As a result, creating advanced optimization and optimum control mathematical models will be necessary to handle the difficulties related to their integration into the grid. This study develops an enhanced optimization and optimal control model for the energy-efficient operation of renewable energy systems based on photovoltaic (PV) and hydropower. The created model takes into account the economic element while integrating an optimization method to address the energy management issue of PV systems. PV system operation is optimized taking into account a variety of operational restrictions using economic objectives like profit maximization and cost minimization. To further simplify energy balancing and lower risk, a hydropower energy system is also taken into consideration. Furthermore, by forecasting the ideal PV output and hydropower generation for various economic purposes, a dynamic optimum control approach is created to discover the best operational strategy for the systems. Case studies are used to evaluate this sophisticated optimization and optimum control model, and the findings show how effective it is at lowering operating expenses for the operation of renewable energy systems in an energy-efficient manner. Keywords: Advanced Optimization, Optimization Techniques, Control Strategies, Energy Management, Optimal control, Mathematical model, Energy-efficient operations, Renewable energy

Topological Analysis and Solar Grid-Tied Integration of Level Building Network Based Extendable High Power Multilevel Converter

This paper presents a new solar multilevel converter (SMLC) with closed-loop control to integrate solar PV arrays with medium voltage 3.3 kV grid. This multilevel converter produces higher number of levels in the output voltage with less usage of semiconductor switches. This solar converter utilizes only ten switches and three PV sources with equal voltage to have seven levels in the output voltage. The main motive is to have minimum switches, which reduces the count of driver circuits for a power converter. Moreover, a reduced switch converter requires less space in terms of installing the power and auxiliary components. This solar power converter comprises a solar photovoltaic (SPV) array fed level building network (LBN) nested within the six switches connected in a rectangular arrangement. The converter has two PV sources at its upper and lower ends. This solar converter is also ideal for asymmetric configurations. Moreover, the increase in solar input power is possible with more count of the LBNs. The converter switching states and operating path demonstrate the working operation of the new solar PV system. A comparison of the proposed solar converter is made with existing multilevel topologies. The system is modeled and executed in closed-loop control to showcase its effectiveness for solar power conversion. The solar converter transfers the power in varying solar conditions successfully.

Stability Analysis for DC-Link Voltage Controller Design in Single-Stage Single-Phase Grid-Connected PV Inverters

The dc-link voltage control is vitally important to ensure the operation of photovoltaic (PV) system at the maximum power voltage, where its performance affects the power quality injected into the grid. In this work, we propose a method, based on the Lyapunov function, for investigating the control system stability, during the design of a nonlinear dc-link voltage controller for single-stage single-phase grid-connected PV inverters. Furthermore, we demonstrate analytically that the non-linearity of the PV string's power-voltage (P-V) curve leads to system instability. Consequently, this article proposes an adaptive dc-link voltage controller, built from the P-V curve, as an application in order to establish a stability criterion. Simulations and experimental validations have been carried out on a grid-connected single-stage single-phase PV inverter test platform. The results confirm the feasibility of the analytical study developed in this article, as well as the proposed dc-link voltage controller with and without the stability analysis.

Analysis of Photovoltaic Systems Through Simulation and Design Using MATLAB

During the operation of photovoltaic systems, a large amount of converted energy, according to various parameters, does not participate in the consumer power supply. As a result, the efficiency of solar installations and the reliability of electric power supply are reduced. This happens for various reasons, but primarily due to the imperfect operation of automatic control systems for photovoltaic installations. These systems are controlled by the information equipment of the entire installation based on software and control algorithms. The imperfection of these algorithms does not allow the full use of solar generation of photovoltaic panels and degrades the quality of operation of all the main and auxiliary equipment of the power generating complex. In the presented work, studies were carried out to improve the operation of the solar power supply system and increase its reliability. The proposed solutions were based on the use of new methods for tracking the maximum power point using high-tech algorithms to control the photovoltaic generating system. In this paper, a generalized Matlab-Simulink solar cell operation model was proposed. The basis of the proposed model is a simple mathematical equation to simulate the operation of a solar photovoltaic cell, since the output characteristics of photovoltaic modules are non-linear. The output I-V and P-V properties of a photovoltaic cell depend on the temperature of the cell and solar radiation. The physical characteristics of a particular solar photovoltaic cell can be modeled as a function of temperature and solar radiation. Numerous models use various software platforms that are available in the literature. This simulation model is very simple and easy to use. Simulation results in Matlab Simulink demonstrate the functionality and dynamic behavior of the provided solar module. Based on the study, it was found that the proposed algorithms can significantly improve the efficiency of photovoltaic installations, taking into account the use of new control methods. The power generated from solar energy converters was used more efficiently, and the photovoltaic system operated at a high conversion rate.

Selection of the photovoltaic system power for the electric vehicle

The article presents a method of a photovoltaic system power selecting for proper electric vehicle. At the beginning, the characteristics of the electric vehicle itself and its traction batteries were made in order to determine the method of its charging. In the presented studies, measurement data from the operation of real photovoltaic systems was recorded and processed. The algorithm used includes examining the energy consumption profile of the owner's residential building. The combined demand for electricity of the electric vehicle and the building made it possible to determine the planned photovoltaic system. The authors presented three possibilities of its location. It can be installed on the roof of the building, on the ground next to the building or on the carport under which an electric vehicle can be parked. Finally, the Metalog family of probability distributions was used to analytically validate the power choice of the photovoltaic system. The authors have developed an algorithm using human and artificial intelligence that helps to properly select the power of the photovoltaic system for the vehicle.

Https://www.anmb.ro/buletinstiintific/buletine/2023_Issue1/02_EEA/169-177.pdf

The Photovoltaic (PV) systems are more present in the communities’ landscape providing energy to the consumers, public buildings, municipalities, and industry, smoothen the electricity prices fluctuations and reducing the dependency on the public grid. They are reliable energy sources for boats and ships as some of the PV technologies are flexible and can be located on plane surfaces or even on the water surface especially when the ships dock at sea or at the seashore. However, the operation of PV systems depends on several weather factors, and it is important to predict their operation to manage the controllable load. Furthermore, it is essential to know if the PV systems generate in surplus or additional energy is required to cover the load. The surplus can be offered for local trading or aggregated and offered for centralized markets. Therefore, in this paper, we aim to predict the output of the PV systems using machine learning algorithms and recurrent neural networks (RNN), especially a multivariate Long Short-Term Memory (LSTM) model. Data extraction, feature engineering, and forecast of the PV power are depicted and the simulations are performed using 4 PV systems located in Constanta County. The results are assessed with prediction performance metrics such as RMSE, MAPE, etc.

Photovoltaic power forecasting with a long short-term memory autoencoder networks

In many developed countries, photovoltaic solar power, which is considered the most cost-effective renewable energy source, accounts for a major portion of electricity production. The photovoltaic (PV) power generation is unpredictable and imprecise due to its high variation that can be caused of meteorological elements, to reduce the negative influence of the use of PV power, accurate PV power prediction is of crucial significance for the secure and efficient operation of photovoltaic power system operation. In light of this, we propose a long short-term memory (LSTM) autoencoder (AE) for photovoltaic power forecasting. Initially, to generate encoded sequences the LSTM-encoder extracts the characteristics from the input data. Then the LSTM-decoder decoded the encoded sequences to advance them to the last dense layer for photovoltaic power prediction. Furthermore, we conducted experiments using a 23.40 kW PV power plants dataset from DKASC in Australia. The results have confirmed that the LSTM-AE model can achieve better prediction accuracy than the benchmark deep learning methods, in terms of mean absolute error (MAE), mean square error (MSE), mean bias error (MBE), root mean square error (RMSE) and coefficient of determination (R $$^2$$ ) performance measures. When the results are analyzed, the LSTM-AE model gives the best results with average RMSE, MAE, and R $$^2$$ to 0.0762 kW, 0.0389 kW, and 99.93 $$\%$$ , respectively. The experimental results confirm that proposed method with the highest R $$^2$$ values and minimum forecasting errors compared to the benchmark models can effectively improve stable performance and achieve better accurate photovoltaic power forecasting.