Characteristics Of Photovoltaic Array Research Articles

Study of ash deposition characteristics of photovoltaic arrays taking into account the effect of photovoltaic module spacing and its effect on output characteristics: Indoor experiment

The problem of ash deposition on the surface of photovoltaic (PV) arrays during actual operation seriously affects their power generation efficiency. Because traditional research does not consider the PV module spacing, it cannot reflect the actual PV array ash deposition problem. This study explored the influence of PV module spacing, combined with different tilt angles and inlet wind speeds, on the characteristics of ash deposition and power output of the PV array through indoor experiments. The study designed a set of experimental equipment for simulating the formation process of ash deposition on the surface of PV arrays and set up an experimental platform to measure the influence of ash deposition on the maximum output power (Pmax), open-circuit voltage (Uoc), and short-circuit current (Isc) of the PV modules. The findings indicate that as the tilt angle increases, the PV array experiences reduced ash deposition and increased output power. Conversely, higher inlet wind speeds lead to greater ash deposition and decreased output power. Moreover, widening the spacing between PV modules results in increased ash deposition and reduced output power. Notably, within the PV array, the front PV module accumulates more ash than the rear module, with larger particle sizes. These research findings have significant implications for optimizing PV array design and enhancing power generation efficiency.

Numerical simulation of dust deposition characteristics of photovoltaic arrays taking into account the effect of the row spacing of photovoltaic modules

Dust deposition on the surfaces of Photovoltaic (PV) arrays during their operation markedly affects their power generation efficiency. Previous research has overlooked the impact of the row spacing of PV modules on the actual dust deposition on PV arrays. This study investigates the dust deposition process and its behavior on PV arrays considering variations in row spacings, inlet wind speeds, dust particle sizes, and dust particle counts. By employing commercial Computational Fluid Dynamics (CFD) software, incorporating the SST k-ω turbulence model and discrete particle model, numerical simulations were performed to analyze the airflow field, dust particle trajectories, dust deposition patterns, and deposition rates on the PV array through grid-independent verification and numerical validation. At the same time, we performed a comparative analysis of the deposition rate considering the rebound of dust particles on the PV module surface versus the case where rebound is not considered. Results revealed that the maximum dust deposition rates at different inlet wind speeds were 6.84 %, 8.84 %, 11.0 %, and 14.6 %, corresponding to dust sizes of 50 μm, 100 μm, 120 μm, and 300 μm, respectively. Smaller dust particles exhibited lower deposition rates, while larger particles were influenced more by mass inertia and gravity, leading to predominant deposition on the front row of PV modules. Larger dust particles are more likely to deposit primarily on the front row of PV modules in a PV array. The tilt angles of 30°, 45°, and 60° were chosen to study the effects of different tilt angles on the dust deposition of PV arrays, and the results show that the dust deposition rate decreases as the tilt angle increases, and it is worth noting that the larger the tilt angle, the larger the dust deposition rate is when the dust particles are especially small as 5 μm. When wind speeds are low and dust particles are small, the dust deposition rate gradually rises as the row spacing increases. However, at higher wind speeds with small dust particles, the row spacing has minimal impact on the dust deposition rate. Conversely, with larger dust particles, increasing the row spacing results in a lower dust deposition rate. These findings underscore the significance of optimizing PV array design for enhanced power generation efficiency.

Nonlinear Matlab/Simulink-Based Mathematical Modeling of Solar Photovoltaic Modules for Power Generation

Solar photovoltaic (PV) arrays comprised of modules are the most important power conversion elements of solar PV-generating systems. Because of the nonlinear characteristics of the solar PV array, determining its operating curves under various operating conditions is a laborious and expensive process. To overcome these barriers, engineers have updated multiple engineering software platforms, including Matlab and Simulink, to incorporate standardized and simplified solar panel designs. Nevertheless, these models are unsuitable for implementation in hybrid energy systems due to their intuitive nature and the need to manually adjust specific system parameters. Consequently, this article outlines a systematic process for simulating photovoltaic cells, modules, and arrays utilizing Matlab and Simulink. The reference model utilised is a 100-watt solar panel. Additionally, the operational characteristics of PV arrays under a broad spectrum of physical parameters and operating conditions are investigated. The simulation investigation is conducted for three distinct weather scenario conditions: cloudless days, days with moderate clouds, and days with heavy overcast conditions. When solar irradiation falls from 1 KW/m2 to 100 W/m2, the resulting voltage, current, and output power all decrease. The output power and voltage increase slightly as the temperature decreases, but the output current from solar PV panels remains approximately constant. The I-V and P-V curves of the solar photovoltaic module are significantly influenced when the shunt resistance changes from 1000 Ω to 0.1 Ω, resulting in a noticeable decrease in power output.

Approaches for Maximizing Maximum Power Point Tracking and Evaluating Operational Constraints in Photovoltaic Systems

Abstract: Optimization techniques have broad utility in multiple engineering fields, including activities such as modeling, system identification, parameter estimation, forecasting, and control of intricate systems. This innovation emphasizes the use of optimization approaches to control the performance of Photo-voltaic (PV) array systems. The main difficulty lies in the nonlinear correlation between current and voltage (I-V) characteristics of PV arrays, resulting in variations in the location of the Maximum Power Point under varied climatic circumstances. It is crucial to track this Maximum electricity Point efficiently in order to collect the highest amount of electricity possible from the photovoltaic devices. Maximum Power Point Tracking is a tracking procedure that optimizes power generation by dynamically altering the operating voltage to coincide with the MPP. In order to tackle this difficulty, the paper suggests a unique two-stage Maximum Power Point Tracking technique. The initial phase applies the Weightless Swarm Algorithm to evaluate the Maximum Power Point in an offline manner. The subsequent phase employs an online Adaptive Perturb & Observe technique to continuously track the MPP in real-time. In addition, an Approximate Single Diode Model is created to quickly assess the output power. In order to verify the suggested method, computer simulations are performed utilizing a Maximum Power Point Tracking system that is combined with a Single-Ended PrimaryInductor Converter

A multilayer integrative approach for diagnosis, classification and severity detection of electrical faults in photovoltaic arrays

Early detection of faults in photovoltaic (PV) systems is crucial for improving their lifespan and reliability. Conventional protection devices may not be able to detect electrical faults under critical conditions. This usually occurs due to (i) the current-limiting nature and non-linear output characteristics of PV arrays, (ii) the changing environmental conditions (such as irradiance and temperature variations), and (iii) the influence of the maximum power point tracker (MPPT), particularly in the presence of critical fault impedance values or mismatch levels. Therefore, modern data-driven methods are required for fault detection and classification in PV arrays. However, careful investigation in previous studies reveals the existing gaps and limitations, such as poor accuracy, and incomprehensive models which have not considered various electrical faults, low mismatch levels, and critical fault impedance values. To this end, in this study, a comprehensive multilayer model is proposed which consists of six layers for diagnosis, classification and severity identification of electrical faults in PV arrays. Each layer adopts a weighted ensemble learning (WEL) algorithm consisting of three classifiers namely Support Vector Machine (SVM), Naïve Bayes (NB) and Logistic Regression (LR). Besides, a genetic algorithm (GA) is employed in each layer to find the optimal weights for each classifier (i.e., SVM, NB and LR). The initial dataset is acquired through an investigation into the PV array current–voltage (I-V) characteristic curve and extraction of several features under various faulty and normal conditions. To reduce the dataset dimensionality thus computationally simplifying the training process, the sequential floating forward selection (SFFS) algorithm is utilized in each layer as a powerful feature selection technique. The results show a highly accurate performance in fault diagnosis, classification, and severity assessment, with an average simulation and experimental accuracies of 98.9% and 98.37%, respectively.

Voltage and Reactive Power-Optimization Model for Active Distribution Networks Based on Second-Order Cone Algorithm

To address the challenges associated with wind power integration, this paper analyzes the impact of distributed renewable energy on the voltage of the distribution network. Taking into account the fast control of photovoltaic inverters and the unique characteristics of photovoltaic arrays, we establish an active distribution network voltage reactive power-optimization model for planning the active distribution network. The model involves solving the original non-convex and non-linear power-flow-optimization problem. By introducing the second-order cone relaxation algorithm, we transform the model into a second-order cone programming model, making it easier to solve and yielding good results. The optimized parameters are then applied to the IEEE 33-node distribution system, where the phase angle of the node voltage is adjusted to optimize the reactive power of the entire power system, thereby demonstrating the effectiveness of utilizing a second-order cone programming algorithm for reactive power optimization in a comprehensive manner. Subsequently, active distribution network power quality control is implemented, resulting in a reduction in network loss from 0.41 MW to 0.02 MW. This reduces power loss rates, increases utilization efficiency by approximately 94%, optimizes power quality management, and ensures that users receive high-quality electrical energy.

Adaptive Particle Swarm Optimization of a Photovoltaic System under Partial Shading

The solar photovoltaic (PV) energy is the most prevalent and popular source of energy. But the PV output characteristics are mainly depending on temperature and irradiance and are nonlinear in nature. Therefore, PV array characteristics greatly vary with change in the atmospheric condition. Under partial shading condition (PSC), PV modules will not receive the same level of incident solar irradiance throughout the system due to some obstructions such as: dust, cloudy weather, shadows of nearby objects: buildings, trees, mountains, birds etc… which causes mismatch in PV module characteristics of the PV array and losses arise in the entire PV configuration. Consequently, power extraction from the PV system is reduced and the PSC on the PV array can be minimized by the proper selection of PV configurations, physical relocation of the PV modules and maximum power point tracking techniques (MPPT) to overcome this problem. The present article studies and compares the MPPT based on the Adaptive particle swarm optimization (APSO) algorithm under partial and completely shaded. The perturbation and observation (P&O) method is widely due to its simplicity and easy implementation but the Intelligent and hybrid control such as: fuzzy logic control (FLC) and adaptive neural fuzzy inference system (ANFIS) can track the MPP with better efficiency but in a long time compared to conventional approaches. In addition, these methods need big data for good results and the data problem is regulated with the evolutionary algorithms and searching the duty cycle (d) in a shorter time than FLC and ANFIS. The principle of PSO, grey wolf optimization (GWO), and APSO techniques is the search for a global solution, and it have good behaviour under PSC but APSO can be classified as best solution between the studied approaches. The simulation results, which are presented in MATLAB/Simulink software, show the effectiveness of the proposed APSO technique.

Research on the output characteristics of photovoltaic arrays under partial shading conditions based on peak point approximate calculation method

Research on the output characteristics of photovoltaic arrays under partial shading conditions based on peak point approximate calculation method

NOVEL SOLAR PHOTOVOLTAIC EMULATION FOR VALIDATING THE MAXIMUM POWER POINT ALGORITHM AND POWER CONVERTER

The photovoltaic (PV) source emulator plays an essential role in evaluating the performance of solar PV arrays, maximum power point (MPPT algorithms), power converters, and control algorithms in the rapidly growing field of solar power generation. This paper presents a novel neural network (NN)--based solar array emulator (SAE) for emulating PV array dynamic characteristics. The proposed SAE reference model, developed using NN, replicates PV array characteristics with a programmable dc power source's support under varying environmental conditions. A 640 W stand-alone PV system is designed and tested using the proposed SAE to validate its performance under various environmental conditions. The performance of the NN-based SAE with the MPPT algorithm is evaluated and compared to the conventional diode-based SAE. The results showed that the proposed NN-based SAE had good accuracy in emulating the dynamic characteristics of the PV array and was faster in execution than the conventional diode-based SAE. The output results of the developed NN-based SAE demonstrate its potential for evaluating MPPT algorithms and power converters.

Classification Method of Photovoltaic Array Operating State Based on Nonparametric Estimation and 3σ Method

Photovoltaic (PV) array, as the key component of large-scale PV power stations, is prone to frequent failure that directly affects the efficiency of PV power stations. Therefore, accurate classification of the operating state of PV arrays is the basis for fault location. Thus, a novel classification method for PV array operating state was designed based on nonparametric estimation and a 3σ method. The actual data analysis proves the hypothesis that performance ratio (PR) distribution characteristics of PV arrays can characterize the operating state of PV arrays. The modeling curve of the PV array with an excellent performance has only one peak and the peak value is large, while the distribution curve of the PV array with a poor performance has a small peak. In this paper, the distribution characteristics of PV arrays are modeled, the peak value is used to classify the operating state of PV arrays, and finally the effectiveness of the proposed method is compared. Overall, this paper makes a valuable contribution by proposing a novel method for accurately classifying the operating state of PV arrays. The proposed method can help improve the efficiency and fault diagnosis of PV power stations.

Comparative Analysis of Maximum Power Point Tracking Algorithms for Standalone PV System Under Variable Weather Conditions

Renewable energy systems are becoming increasingly predominant in the current scenario, and Photovoltaic (PV) arrays are one of the most widely used renewable energy generation sources. The current-voltage characteristics of PV arrays are non-linear, necessitating the need for supervisory techniques in order to ensure that the array functions at maximum efficiency, which is performed by Maximum Power Point Tracking (MPPT) techniques. These techniques are categorized into classical, intelligent and optimization algorithms. This paper performs a comparative analysis between five different MPPT techniques belonging to these categories – Perturb and Observe (P&O), Incremental Conductance (IC), Fuzzy Logic Control (FLC), Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CSA). A standalone PV system interfaced with a Boost converter is simulated on MATLAB Simulink for the performance evaluation of the MPPT techniques. Solar energy is extremely susceptible to changes in local weather conditions, mainly variations in solar insolation levels. The designed system is tested against a varying insolation profile in order to examine the robustness of the MPPT techniques, with their operation efficiencies showcased.

Effect of Building Height on Wind Load Characteristics of Photovoltaic Arrays

The present study is carried out by FLUENT software, and the wind load characteristics of photovoltaic arrays installed on different building heights are analyzed. The analysis results show that the higher the building height, the larger the radius of the main vortex formed above the photovoltaic array. When H=0 m, the absolute value of the overturning moment coefficient of the photovoltaic array is much larger than those of the array installed on the roof, and it’s along the decreasing trend of the incoming flow. The shape coefficient of the photovoltaic array shows an obvious gradient change, and as the height of the building increases, the shape coefficient of the building also follows decreasing.

Application of Artificial Intelligence in PV Fault Detection

The rapid revolution in the solar industry over the last several years has increased the significance of photovoltaic (PV) systems. Power photovoltaic generation systems work in various outdoor climate conditions; therefore, faults may occur within the PV arrays in the power system. Fault detection is a fundamental task needed to improve the reliability, efficiency, and safety of PV systems, and, if not detected, the cost associated with the loss of power generated from PV modules will be quite high. Moreover, maintenance staff will take more time and effort to fix undetermined faults. Due to the current-limiting nature and nonlinear output characteristics of PV arrays, fault detection is not that easy and the application of artificial intelligence is proposed for the sake of fault detection in PV systems. The idea behind this approach is to compare the faulty PV module with its accurate model (factory fingerprint) by checking every PV array’s I–V and P–V curves using the Artificial Neural Network (ANN) logarithm as a subsection of the Artificial Intelligence’s (AI) techniques. This proposed approach achieves a high performance of fault detection and gives the advantage of determining what type of fault has occurred. The results confirm that the proposed logarithm performance becomes better as the number of distinguishing points extend, providing great value to the Solar PV industry.

Health status evaluation of photovoltaic array based on deep belief network and Hausdorff distance

Photovoltaic (PV) arrays, as the core part of PV plants, are sensitive to the complex environment that can lead to fluctuations in their power generation performance. The health status evaluation (HSE) of PV arrays is beneficial for routine maintenance and economic value evaluation. In this paper, a method for evaluating the health status of PV array based on deep belief network (DBN) and Hausdorff distance (HD) is proposed. First, the I–V curves of the PV array are preprocessed, including curve filtering and points redistribution. Then, the practical features of I–V characteristics are extracted by DBN. Next, the health indicator (HI) of the PV array is constructed by HD and Logistic function. Finally, the triangular fuzzy membership function is used to build the mapping relationship between the HI values and the health grades of the PV array. The proposed method enables fully extracting the features from the I–V characteristics of PV arrays and gives an accurate evaluation of different states of PV arrays. The experimental results show that the proposed HSE method can realize the expected objectives.

Power output control method of small power photovoltaic grid connected power generation under complex illumination

Due to the irregular change of lighting conditions, there is a large error in the control of photovoltaic grid connected power output. Therefore, this paper proposes proposes a power output control method for low-power photovoltaic grid-connected power generation considering complex illumination. Based on the analysis of the basic characteristics of photovoltaic grid connected power generation, the output characteristics of photovoltaic array under complex lighting conditions are studied. Combined with its variation characteristics, based on fuzzy control strategy, the output of photovoltaic grid connected energy storage system is adjusted by filter control with variable time constant, and economic operation and stable operation are taken as constraints. The test results show that the design method has a high degree of fit between the control of photovoltaic grid connected power output and the actual results, and the error is within a reasonable range, which is obviously due to the two groups of comparison methods.

Uncertainty analysis based on non-parametric statistical modelling method for photovoltaic array output and its application in fault diagnosis

Fault diagnosis is crucial for photovoltaic (PV) power generation, but the output uncertainty characteristics of PV arrays caused by different failures and their fluctuations make them facing new challenges. The parameter estimation (PE) method is widely used for uncertainty analysis, but there exist big differences between the PE results and the real output distributions. To solve this problem, this paper proposes a method for acquiring the fault diagnosis threshold based on non-parametric kernel density estimation (NKDE). First, the distribution characteristics of PV arrays output are statistically analysed, it is found that current and power are mainly affected by the solar irradiance, resulting in strong volatility and uncertainty, which makes it hard to get the threshold for fault diagnosis, thus we propose new three status indicators to finish it. Then, the probability models of the three indicators are built based on the kernel density estimation (KDE) method, by assigning the confidence values of the models, the fault diagnosis threshold intervals can be obtained. Verification shows that NKDE method performed better than traditional PE method in fitting the distribution of the PV arrays output, it does not need prior knowledge of the probability density function. And the proposed fault diagnosis method proves it is feasible to apply the uncertainty analysis method to PV array fault diagnosis. The paper provides a new idea for setting the dynamic threshold for PVarray fault diagnosis.

The Design and Investigation of Space Photovoltaic Array Hardware-simulator

A hardware part of space photovoltaic array simulation system is designed in this paper, to realize the actual output characteristics of photovoltaic array. In this paper, the design of a PV array emulator (PVAE) is presented. By analyzing the V-I characteristic curve of solar cell given by the previous software, the simulation scheme is developed. Secondly, the control scheme of the proposed emulator based on DC-DC converter is discussed in detail, than the transfer function of the whole system is established, at last the digital controller based on DSP is designed. A prototype 200W PVAE based on full bridge DC-DC converter has been made, the experimental results show the validity of the controller design. The application of the PVAE can greatly shorten the research and design cycle of photovoltaic array power supply system of spacecraft, improve the research efficiency and credibility of research results.

Research on General Model and Parameter Characteristics of Photovoltaic Array

Under partial shading conditions, the P–U curve of PV (photovoltaic) array shows multiple local peaks. The traditional PV model cannot reflect this change. It is necessary to re-establish the mathematical model of the PV array suitable for complex lighting conditions. Based on the mathematical model of double diode PV cells, combined with the series–parallel theory of circuits, a detailed analysis of photovoltaic arrays under partial shading conditions is carried out, and the mathematical model of PV arrays under partial shading conditions is theoretically deduced by piecewise functions. The proposed general model is tested for irradiance intensity, temperature, and diode ideality factor and parallel resistance variable parameter sensitivity test in MATLAB. The output characteristics is tested and compared under uniform light and local shading conditions under random environment. The results show that the proposed model has good versatility and accuracy, and it can quickly and accurately reflect the I–U and P–U characteristics of PV arrays under complex lighting conditions under varying operating conditions, which provides strong support for related research on large-scale PV power generation systems.

Research on Accurate Engineering Mathematical Model of PV Cells and Simulation Characteristics

It is an important basis for PV power generation and related technology research to establish an efficient and accurate photovoltaic (PV) array engineering mathematical model. For the difficult problem of traditional mathematical model of PV array to be solved, the engineering mathematical model of PV array is derived based on PV cell single diode model. The diode ideal factor, series resistance, temperature and radiation intensity of PV array on the output characteristics of PV array influences are analyzed. The model is improved and the parameters are optimized, an efficient iterative method is proposed to calculate the series resistance and parallel resistance. On Matlab platform, the comparison of 4 solar cell material property parameters is tested to verify the accuracy of the proposed solution. The results are compared with the traditional Newton iterative method and approximate fitting line method in 5 random environments, the error can be within 6%. The simulation results show that the proposed model has better versatility and sensitivity, quickly and accurately reflects the I–U and P–U output characteristics of PV arrays under variable working conditions; it meets the requirements of engineering accuracy, and provides strong support for large-scale PV power generation system and related research.

A novel PSO strategy for improving dynamic change partial shading photovoltaic maximum power point tracker

ABSTRACT Photovoltaic (PV) energy systems are very important electric generation sources in modern power systems. The generated power from the PV array is a function in its terminal voltage. Tracking the maximum power needs a DC/DC converter to control the PV array terminal voltage. The boost converter is used in this paper for this purpose. Due to the multiple peaks in the power versus voltage (P-V) characteristics of PV array a smart optimization technique is required to work as a maximum power point tracker (MPPT). The particle swarm optimization (PSO) is a superior technique to track the global peak (GP) and avoid getting trapped in one of the local peaks (LPs). Despite the superiority of PSO, it suffers from some shortcomings in the application of MPPT of PV systems such as its sluggishness convergence, its inability to catch the new GP in case of acute change in shading pattern, and the possibility of getting trapped in one of the LPs. All these shortcomings are solved in this paper using a new adaptive PSO (NA-PSO) strategy. This new strategy solved these problems by starting the duty ratio at an equal distance between each other and force the particles with lower generated power to work around the one with the highest generated power. This newly proposed technique reduced the convergence time by 50% and reduced the failure rate to zero. Also, the generated energy is increased by 10.4% compared to the conventional PSO. The results collected from the NA-PSO strategy show its superiority in reducing the convergence time and failure rate and increasing the generated power, and the system efficiency, especially in the dynamic variation of the shading pattern compared to the conventional PSO.