Performance Assessment of Islanding Detection for Mul-ti-inverter Grid-connected Photovoltaic Systems

Islanding detection is an essential function for safety and reliability in grid-connected Distributed Generation Systems (DGS). Passive and active islanding detection methods have been analyzed in literature considering DGS with only one inverter connected to the utility. With the big scale application of photovoltaic (PV) power systems, islanding detection technology of multi-inverter DGS has been paid more attention. This paper analyzes the performance of diverse islanding detection methods in multiple inverters grid-connected PV systems. Non-Detection Zones (NDZ) of multi-inverter systems in a load parameter space are used as analytical tool. The paper provides guidance for the islanding detection design in multiple grid-connected inverters.

A Distributed Photovoltaics Ordering Grid-Connected Method for Analyzing Voltage Impact in Radial Distribution Networks

In recent years,distributed photovoltaics(DPV)has ushered in a good development situation due to the advantages of pollution-free power generation,full utilization of the ground or roof of the installation site,and balancing a large number of loads nearby.However,under the background of a large-scale DPV grid-connected to the county distribution network,an effective analysis method is needed to analyze its impact on the voltage of the distribution network in the early development stage of DPV.Therefore,a DPV orderly grid-connected method based on photovoltaics grid-connected order degree(PGOD)is proposed.This method aims to orderly analyze the change of voltage in the distribution network when large-scale DPV will be connected.Firstly,based on the voltagemagnitude sensitivity(VMS)index of the photovoltaics permitted grid-connected node and the acceptance of grid-connected node(AoGCN)index of other nodes in the network,thePGODindex is constructed to determine the photovoltaics permitted grid-connected node of the current photovoltaics grid-connected state network.Secondly,a photovoltaics orderly grid-connected model with a continuous updating state is constructed to obtain an orderly DPV grid-connected order.The simulation results illustrate that the photovoltaics grid-connected order determined by this method based on PGOD can effectively analyze the voltage impact of large-scale photovoltaics grid-connected,and explore the internal factors and characteristics of the impact.

Advancements in Photovoltaic Panel Fault Detection Techniques

This paper examines the progression and advancements in fault detection techniques for photovoltaic (PV) panels, a target for optimizing the efficiency and longevity of solar energy systems. As the adoption of PV technology grows, the need for effective fault detection strategies becomes increasingly paramount to maximize energy output and minimize operational downtimes of solar power systems. These approaches include the use of machine learning and deep learning methodologies to be able to detect the identified faults in PV technology. Here, we delve into how machine learning models, specifically kernel-based extreme learning machines and support vector machines, trained on current-voltage characteristic (I-V curve) data, provide information on fault identification. We explore deep learning approaches by taking models like EfficientNet-B0, which looks at infrared images of solar panels to detect subtle defects not visible to the human eye. We highlight the utilization of advanced image processing techniques and algorithms to exploit aerial imagery data, from Unmanned Aerial Vehicles (UAVs), for inspecting large solar installations. Some other techniques like DeepLabV3 , Feature Pyramid Networks (FPN), and U-Net will be detailed as such tools enable effective segmentation and anomaly detection in aerial panel images. Finally, we discuss implications of these technologies on labor costs, fault detection precision, and sustainability of PV installations.

An Efficient YOLOX-Based Method for Photovoltaic Cell Defect Detection

Defect detection technology is crucial for the efficient operation and maintenance of photovoltaic systems.However,the diversity of defect types,scale inconsistencies,and background interference significantly complicate the detection task.Therefore,this paper employs the YOLOX model as the backbone network structure and optimizes various modules to address these issues.First,we adopt a transfer learning strategy to accelerate model convergence and avoid insufficient accuracy due to the limited number of defect samples.Second,we introduce the SENet module into the feature extraction process to enhance the contrast between defects and their background.Then,we incorporate the ASFF strategy at the end of the PAFPN network to adaptively learn and emphasize both high-and low-level semantic features of defects.Furthermore,model accuracy is enhanced by refining the loss functions for positioning,classification,and confidence.Finally,the proposed method achieved excellent results on the Photovoltaic Electroluminescence Anomaly Detection dataset(PVEL-AD),with a mAP of 96.7%and a detection speed of 71.47FPS.Specifically,the detection of small target defects showed significant improvement.

Weather Prediction With Multiclass Support Vector Machines in the Fault Detection of Photovoltaic System

Since the efficiency of photovoltaic(PV) power is closely related to the weather,many PV enterprises install weather instruments to monitor the working state of the PV power system.With the development of the soft measurement technology,the instrumental method seems obsolete and involves high cost.This paper proposes a novel method for predicting the types of weather based on the PV power data and partial meteorological data.By this method,the weather types are deduced by data analysis,instead of weather instrument A better fault detection is obtained by using the support vector machines(SVM) and comparing the predicted and the actual weather.The model of the weather prediction is established by a direct SVM for training multiclass predictors.Although SVM is suitable for classification,the classified results depend on the type of the kernel,the parameters of the kernel,and the soft margin coefficient,which are difficult to choose.In this paper,these parameters are optimized by particle swarm optimization(PSO) algorithm in anticipation of good prediction results can be achieved.Prediction results show that this method is feasible and effective.

Ghost-Retina Net:Fast Shadow Detection Method for Photovoltaic Panels Based on Improved Retina Net

Based on the artificial intelligence algorithm of RetinaNet,we propose the Ghost-RetinaNet in this paper,a fast shadow detection method for photovoltaic panels,to solve the problems of extreme target density,large overlap,high cost and poor real-time performance in photovoltaic panel shadow detection.Firstly,the Ghost CSP module based on Cross Stage Partial(CSP)is adopted in feature extraction network to improve the accuracy and detection speed.Based on extracted features,recursive feature fusion structure ismentioned to enhance the feature information of all objects.We introduce the SiLU activation function and CIoU Loss to increase the learning and generalization ability of the network and improve the positioning accuracy of the bounding box regression,respectively.Finally,in order to achieve fast detection,the Ghost strategy is chosen to lighten the size of the algorithm.The results of the experiment show that the average detection accuracy(mAP)of the algorithm can reach up to 97.17%,the model size is only 8.75 MB and the detection speed is highly up to 50.8 Frame per second(FPS),which can meet the requirements of real-time detection speed and accuracy of photovoltaic panels in the practical environment.The realization of the algorithm also provides new research methods and ideas for fault detection in the photovoltaic power generation system.

Deep Learning-Based Model for Defect Detection and Localization on Photovoltaic Panels

The Problem of Photovoltaic(PV)defects detection and classification has been well studied.Several techniques exist in identifying the defects and localizing them in PV panels that use various features,but suffer to achieve higher performance.An efficient Real-Time Multi Variant Deep learning Model(RMVDM)is presented in this article to handle this issue.The method considers different defects like a spotlight,crack,dust,and micro-cracks to detect the defects as well as loca-lizes the defects.The image data set given has been preprocessed by applying the Region-Based Histogram Approximation(RHA)algorithm.The preprocessed images are applied with Gray Scale Quantization Algorithm(GSQA)to extract the features.Extracted features are trained with a Multi Variant Deep learning model where the model trained with a number of layers belongs to different classes of neurons.Each class neuron has been designed to measure Defect Class Support(DCS).At the test phase,the input image has been applied with different operations,and the features extracted passed through the model trained.The output layer returns a number of DCS values using which the method identifies the class of defect and localizes the defect in the image.Further,the method uses the Higher-Order Texture Localization(HOTL)technique in localizing the defect.The pro-posed model produces efficient results with around 97%in defect detection and localization with higher accuracy and less time complexity.

An Efficient Fuzzy Logic Fault Detection and Identification Method of Photovoltaic Inverters

Fuzzy logic control(FLC)systems have found wide utilization in several industrial applications.This paper proposes a fuzzy logic-based fault detection and identification method for open-circuit switch fault in grid-tied photovoltaic(PV)inverters.Large installations and ambitious plans have been recently achieved for PV systems as clean and renewable power generation sources due to their improved environmental impacts and availability everywhere.Power converters represent the main parts for the grid integration of PV systems.However,PV power converters contain several power switches that construct their circuits.The power switches in PV systems are highly subjected to high stresses due to the continuously varying operating conditions.Moreover,the grid-tied systems represent nonlinear systems and the system model parameters are changing continuously.Consequently,the grid-tied PV systems have a nonlinear factor and the fault detection and identification(FDI)methods based on using mathematical models become more complex.The proposed fuzzy logic-based FDI(FL-FDI)method is based on employing the fuzzy logic concept for detecting and identifying the location of various switch faults.The proposed FL-FDI method is designed and extracted from the analysis and comparison of the various measured voltage/current components for the control purposes.Therefore,the proposed FL-FDI method does not require additional components or measurement circuits.Additionally,the proposed method can detect the faulty condition and also identify the location of the faulty switch for replacement and maintenance purposes.The proposed method can detect the faulty condition within only a single fundamental line period without the need for additional sensors and/or performing complex calculations or precise models.The proposed FL-FDI method is tested on the widely used T-type PV inverter system,wherein there are twelve different switches and the FDI process represents a challenging task.The results shows the superior and accurate performance of the proposed FL-FDI method.

Conditional Probability Approach for Fault Detection in Photovoltaic Energy Farms

Detection of electric faults in photovoltaic (PV) farms enhances a sustainable service continuity of farm energy generation. In this paper, a probabilisticfunction is introduced to detect the faults in the PV farms. The conditional probability functions are adopted to detect different fault conditions such as internalstring faults, string-to-string faults, and string-to-negative terminal faults. As thediodes are important to make the PV farms in-service safely during the faults,the distribution currents of these faults are evaluated with different concepts ofdiode consideration as well as without considering any diode installation. Thispart of the study enhances the diode utilization in the PV farms concerning theprotection point of view. The PV string currents are used as inputs to the conditional probability detection algorithms. However, the setting of the fault detectiontechnique is not portable for the other PV systems due to broad ranges of PV system ratings. To accordingly generalize the proposed fault detection algorithm, thePV string currents are first normalized to the total array current for universallyapplying the detection function at different PV string ratings. The limiting faultresistances are evaluated to show the sensitivity of the proposed fault detector.The results ensure the application of the proposed probabilistic detection functionfor PV farms.

Evaluation of the Performances of the First Grid-Connected Photovoltaic System in Sénégal

This document presents the evaluation and the monitoring of the performances of the first grid-connected photovoltaic system installed in the Center of Studies and Researches on the Renewable Energies (CERER) inaugurated on December 4th, 2012 by the governmental authorities of Senegal and Tenerife. This mini power plant of 3.15 kWc is a perfect example of the political will of the government which is to reduce the production cost of the electricity, with the diversification of the sources of production, and the greater use of the other sources such as the natural gas, the coal, the renewable energies. The evaluation of the performances of the installation is realized by using the indicators of efficiency and performance as the photovoltaic surface yield, the ratio of photovoltaic performance, the photovoltaic specific yield, and the losses of captures. The obtained results show that a big part of the energy shone during the period of observation was not able to be used further to circumstances such as the losses of conductivity, the heat losses or for example the defects on components. The analysis also shows that a large part of the produced energy is not injected because of the dilapidation of the network, the defects of landing but especially one disjunction sees frequently at the level of the point of injection.

Study on Image Recognition Algorithm for Residual Snow and Ice on Photovoltaic Modules

The accumulation of snow and ice on PV modules can have a detrimental impact on power generation,leading to reduced efficiency for prolonged periods.Thus,it becomes imperative to develop an intelligent system capable of accurately assessing the extent of snow and ice coverage on PV modules.To address this issue,the article proposes an innovative ice and snow recognition algorithm that effectively segments the ice and snow areas within the collected images.Furthermore,the algorithm incorporates an analysis of the morphological characteristics of ice and snow coverage on PV modules,allowing for the establishment of a residual ice and snow recognition process.This process utilizes both the external ellipse method and the pixel statistical method to refine the identification process.The effectiveness of the proposed algorithm is validated through extensive testing with isolated and continuous snow area pictures.The results demonstrate the algorithm’s accuracy and reliability in identifying and quantifying residual snow and ice on PV modules.In conclusion,this research presents a valuable method for accurately detecting and quantifying snow and ice coverage on PV modules.This breakthrough is of utmost significance for PV power plants,as it enables predictions of power generation efficiency and facilitates efficient PV maintenance during the challenging winter conditions characterized by snow and ice.By proactively managing snow and ice coverage,PV power plants can optimize energy production and minimize downtime,ensuring a sustainable and reliable renewable energy supply.

Comprehensive Modulation and Classification of Faults and Analysis Their Effect in DC Side of Photovoltaic System

The first step in automatic supervision, condition monitoring and fault detection of photovoltaic system is recognition, exploration and classification of all possible faults that maybe happen in the system. This paper aims to perceive, classified, simulate and discus all electrical faults in DC side of photovoltaic system, regarding electrical voltage and current inspections. For that, simplified hybrid model of photovoltaic panel in MATLAB environment is used. Investigation and classification of each type of faults is down and the effects of the faults are illustrated in this paper. Flash test are applied to improved electrical model. Current-Voltage curves signature are interpreted and investigated in simulation environment.

New Islanding Detection Method with Better Performance in Presence of Non-resistive Load

Islanding detection is a mandatory component in grid-connected photovoltaic （PV） inverters. It is also a key issue in the photovoltaic agriculture. In this work, an overview on the islanding effect in grid-connected PV power systems was provided. Various islanding detection methods were introduced and their strength and weakness were dicussed. An improved islanding detection method was proposed based on active frequency drift （AFD）. The new method tolerated capacitive and inductive loads, because its perturbation signal was not offset by the non-resistive load. The new method through simulation in MATLAB/Simulink was evaluated and the advantages of the new method were demonstrated.

Large-Scale Distributed Photovoltaic Power Dispatching and Operation Management Review

Distributed photovoltaic power (PV) is the main development model of distributed generation. It is necessary to research on dispatching and operation management with large-scale distributed PV connected. This paper analyzes development status, technical requirement and dispatching and operation management situation of distributed PV in Germany and China. Then introduce the preparation of distributed PV dispatching and operation management criterion. Through summarizing the experiences and lessons of large-scale distributed PV development in Germany, it gives advice to the development of distributed PV dispatching and operation management in China.

Research on Control Method of Inverters for Large-scale Grid Connected Photovoltaic Power System

A grid-connected inverter controlling method to analyze dynamic process of large-scale and grid-connected photovoltaic power station is proposed. The reference values of control variables are composed of maximum power which is the output of the photovoltaic array of the photovoltaic power plant, and power factor specified by dispatching, the control strategy of dynamic feedback linearization is adopted. Nonlinear decoupling controller is designed for realizing decoupling control of active and reactive power. The cascade PI regulation is proposed to avoid inaccurate parameter estimation which generates the system static error. Simulation is carried out based on the simplified power system with large-scale photovoltaic plant modelling, and the power factor, solar radiation strength, and bus fault are considered for the further research. It’s demonstrated that the parameter adjustment of PI controller is simple and convenient, dynamic response of system is transient, and the stability of the inverter control is verified.

Forecast of Power Generation for Grid-Connected Photo-voltaic System Based on Grey Theory and Verification Model

Being photovoltaic power generation affected by radiation strength, wind speed, clouds cover and environment temperature, the generating in each moment is fluctuating. The operational characteristics of grid-connected PV systems are coincided with gray theory application conditions. A gray theory model has been applied in short-term forecast of grid-connected photovoltaic system. The verification model of the probability of small error will help to check the accuracy of the gray forecast results. The calculated result shows that the ?model accuracy has been greatly enhanced.

A composite controller for grid connected photovoltaic systems

This paper addresses a new composite controller for a boost-buck inverter to interface the photovoltaic array with the power grid. Based on dynamically tracking the maximum power point, two novel control methodologies are proposed to control the DC-bus and the output current for the inverter. First, a new linear cycle discrete control algorithm is introduced to realize linear control of the DC-bus voltage and synchronously get the reference current for the inner current loop with a little calculation. Then, to assure a good adaptability to noise and model uncertainty, an auto-disturbance rejection controller is chosen as the output current controller, which does not depend on precise mathematics model. Thus, output current of PV system to the grid is with low harmonic distortion and unity power factor. Simulations and experiments are performed, and the results show that the system is of excellent robustness and effective.

Quasi-Z Source Inverter Control of PV Grid-Connected Based on Fuzzy PCI

The photovoltaic grid-connected inverter is an important interface between the photovoltaic power generation system and power grid.Its high-quality operation is directly related to the output power quality of the power grid.In order to further optimize the control effect of the quasi-Z source grid-connected photovoltaic inverter,a fuzzy proportional complex integral control(PCI)method is proposed for the current internal loop control.This method can eliminate the steady-state error,and has the characteristic of zero steady-state error adjustment for the AC disturbance signal of a specific frequency.The inductance-capacitance-inductance(LCL)filter is adopted in the grid-connected circuit,and the feedback capacitive current is taken as the control variable of the inner loop to form the active damping control method,which can not only effectively suppress the resonance of the LCL circuit,but also significantly inhibit the high-order harmonics in the grid-connected current.Finally,a system simulation model is built in MATLAB/Simulink to verify the superiority and effectiveness of the proposed method.

FCS-MPC Strategy for PV Grid-Connected Inverter Based on MLD Model

In the process of grid-connected photovoltaic power generation,there are high requirements for the quality of the power that the inverter breaks into the grid.In this work,to improve the power quality of the grid-connected inverter into the grid,and the output of the system can meet the grid-connected requirements more quickly and accurately,we exhibit an approach toward establishing a mixed logical dynamical(MLD)model where logic variables were introduced to switch dynamics of the single-phase photovoltaic inverters.Besides,based on the model,our recent efforts in studying the finite control set model predictive control(FCS-MPC)and devising the output current full state observer are exciting for several advantages,including effectively avoiding the problem of the mixed-integer quadratic programming(MIQP),lowering the THD value of the output current of the inverter circuit,improving the quality of the power that the inverter breaks into the grid,and realizing the current output and the grid voltage same frequency and phase to meet grid connection requirements.Finally,the effectiveness of the mentioned methods is verified by MATLAB/Simulink simulation.

Outdoor Performance Evaluation of Grid-Connected PV Technologies in Cyprus

This paper presents the outdoor performance evaluation of different grid-connected PV technologies installed in Cyprus over a two year period. The PV research and testing facility at the University of Cyprus was commissioned in 2006 to perform continuous measurements of meteorological and PV operational parameters. The test site is appropriately equipped to undertake such evaluations at a very high resolution （1 measurement per second）. The perfromance results obtained for the two year evaluation period clearly show how each PV technology has performed under the climatological conditions in Cyprus. Finally the high average energy yield of the fixed plate systems under test, 1580 kWh/kWp and 1609 kWh/kWp during the first and second year of evaluation respectively, also verifies that solar energy is a very promising renewable source of energy for countries with a high solar resource.