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Abstract
According to the distinct temperature difference characteristic between solar cells under different operation states, a new analysis and recognizing approach for the photovoltaic array operation states was proposed based on the infrared image analysis in the paper. At first, the infrared images of photovoltaic arrays are pre-processed and analyzed; the abnormal areas and their features are abstracted. To deal with influences of some factors on the temperature of photovoltaic, such as environment temperature, wind speed and solar illumination, a fuzzy reasoning technique based on the data fusion is employed and the fault areas are recognized automatically. The research results show that the normal, shadowed and aging destroyed operation states of photovoltaic array in power system can be recognized accurately with the proposed approach.
... This method is able to detect faults such as fragmentation, broken grids, black pieces and crack that are not visible with the naked eye [8]. A system was developed based on infrared image analyses that analyses and recognizes the working status of PV arrays [9]. The time domain reflectometry technique involves the use of a pulse signal which is injected into the PV module and then by comparing the input signal with the feedback output signal, faults are identified [4]. ...
... Feeding in the values of ambient temperature, surface temperature solar irradiation, and voltage and fault type, current was predicted. For the given values in Figure 3. 9, the open circuit current was 0.78A, short circuit current was 0.94A and no fault current was 0.97A. According to the data collected, for the same value of ambient temperature, surface temperature, solar irradiation and voltage, the open circuit current was 0.71A, short circuit current was 0.98A and no fault current was 1.01A. ...
Our world has never faced a far graver threat than it does today, the energy crisis. Low lying rural areas have been identified as mostly suffered areas due to non-availability of power. The energy production from solar photovoltaic system has gained attention worldwide as it a clean and renewable source of energy. The power output of solar panel mainly depends on solar irradiance and temperature. But just like many other power generation sources PV systems does come with its fair share of short comings too that reduces the performance and even causes serious safety concerns. This paper covers major faults like open circuit, short circuit and partial shading. These faults are induced in the PV strings and then output characteristics such as open circuit voltage, short circuit current, current and voltage at maximum power point (MPP) are measured using multi-meter and SP lite 2 sensor. The output characteristics are compared to the healthy strings and based on the difference in current (I) and voltage (v) output at maximum power point (MPP) of each fault induced system, faults are detected. The datasets collected for detection is then analyzed in Minitab software using regression analysis and predictive model is generated for each fault. Based on the amount of solar irradiation, surface temperature, ambient temperature, and voltage, current for each fault is predicted. Machine learning methodologies are then used to validate fault detection and prediction. Random Forest classifier is used for fault classification while linear regression is used for current prediction.
... Time Domain Reflectometry (TDR) is an effective technique for PV fault diagnosis in a seriesconnected PV farms. However, it requires precision instrument for analysis of signals to diagnose the fault accurately and can only be applied to series-connected PV modules as presented in [6]. VOLUME XX, 2017 1 As mostly PV arrays are connected in various seriesparallel configurations to extract the required amount of power, techniques presented in the above-cited works cannot diagnose faults in series-parallel configurations of PV arrays. ...
... If sigmoid function k 0 in given iteration, then the raise in k is determined by Eq. (6).The raise in k occurs and k s is estimated again as k s . ...
Categorization of PV faults is an essential task for improving the efficiency and reliability of a photovoltaic (PV) system. Output characteristics of a solar (PV) system can be severely affected under various fault conditions including short circuit, module mismatch, open circuit, and multiple faults under shading conditions. Such PV faults can potentially be analyzed through the PV characteristic curve analysis using a multilayer neural network with a scaled conjugate gradient algorithm (SCG). This paper presents an extensive investigation for categorization, i.e., classification of the above-mentioned PV faults using the SCG algorithm. The major contribution of the presented research work is the categorization of PV faults in sixteen different classes considering polycrystalline and thin-film PV technologies with two different configurations, including SP and TCT. The fault classification is achieved with high accuracy of 99.6% and a fast-computational time of 0.08 sec. The results are validated through the plot of the Confusion Matrix and Region of Convergence (ROC) with their performance evaluation in MATLAB. The achieved accuracy and fast computational time prove the effectiveness of the multilayer neural network-based approach for classification of the PV faults to increase power output, efficiency, and lifespan of PV systems.
... Since off-line fault locating methods cause severe power loss of PV systems, online locating strategies have attracted the attention of many researchers. The infrared image analysis method can automatically identify the working status of PV arrays by combining a fuzzy reasoning technique [8]. Ref. [9] presents the infrared image detection method to identify and locate degradation and partial shading faults, but this approach is susceptible to irradiation change and requires many expensive infrared cameras. ...
... , respectively, as illustrated in Fig. 22. The readings 1,1 V to 1,8 V satisfy an arithmetic progression and 1,9 V is equal to oc ( 1) rV s rV m −− , which follows the case "01" in Table 1. Similarly, 90,1 V to 90,9 V match the case "10" in Table 1 , respectively, as presented in Fig. 23. ...
In practical grid-connected photovoltaic systems, faulty modules for small-scale photovoltaic arrays need to be accurately located, while the same strategy to locate each faulty module for large-scale arrays would imply high investment cost due to a large number of sensors needed. Therefore, to reduce the number of sensors and save fault monitoring investment, it suffices to identify where fault happens in a module block, which consists of several modules connected in series. For this purpose, this paper proposes a fault-locating strategy to identify faulty module blocks for large-scale arrays, where voltage sensors are deployed by differentiating the parity of string numbers in the array to acquire the terminal voltages of the module blocks between adjacent strings, and the fault locating rules for open circuit, short circuit, degradation and partial shading faults are formulated. With the help of MATLAB/Simulink platform, the proposed strategy is tested in one small-size array and two large-scale arrays, and the testing results demonstrate that the proposed strategy can locate the single fault case, multiple faults of single-type, as well as mixed faults of different sub-arrays. To summarize, the proposed fault-locating method has higher locating accuracy, lower implementation cost and wiring complexity, and is easily integrable with existing photovoltaic systems.
... On the other hand, the thermal infrared detection method uses an infrared scanner to identify and detects faults by measuring the body temperature of the PV panel for irregular heat. Peizhen and Shicheng [8] used infrared image analysis to recognize and analyze the working status of PV arrays. While their approach can recognize the shading and deterioration status of the PV panel, it focuses mainly on the identification of hot spot defects within the PV array. ...
... Under the normal operation mode (standard test condition), the maximum and minimum value of PR can be calculated using Equations (8) and (9) Figure 6 shows the developed fault detection algorithm. If the value of PR is not higher than the maximum PR and not within the scope of the normal operation mode, then the algorithm based on the fuzzy system will determine the type of fault and whether it is a minor or moderate fault. ...
The expanding use of photovoltaic (PV) systems as an alternative green source for electricity presents many challenges, one of which is the timely diagnosis of faults to maintain the quality and high productivity of such systems. In recent years, various studies have been conducted on the fault diagnosis of PV systems. However, very few instances of fault diagnostic techniques could be implemented on integrated circuits, and these techniques require costly and complex hardware. This work presents a novel and effective, yet small and implementable, fault diagnosis algorithm based on an artificial intelligent nonlinear autoregressive exogenous (NARX) neural network and Sugeno fuzzy inference. The algorithm uses Sugeno fuzzy inference to isolate and classify faults that may occur in a PV system. The fuzzy inference requires the actual sensed PV system output power, the predicted PV system output power, and the sensed surrounding conditions. An artificial intelligent NARX-based neural network is used to obtain the predicted PV system output power. The actual output power of the PV system and the surrounding conditions are obtained in real-time using sensors. The algorithm is proven to be implementable on a low-cost microcontroller. The obtained results indicate that the fault diagnosis algorithm can detect multiple faults such as open and short circuit degradation, faulty maximum power point tracking (MPPT), and conditions of partial shading (PS) that may affect the PV system. Moreover, radiation and temperature, among other non-linear associations of patterns between predictors, can be captured by the proposed algorithm to determine the accurate point of the maximum power for the PV system.
... The continuous, safe, reliable, and effective functioning of PV systems depends on the development of automatic diagnosis and classification techniques for PV monitoring systems. The huge number of solar modules used in large-scale PV facilities has made it difficult to detect and classify faults [6][7][8][9]. The automation of detection, diagnoses, and classification of PV system faults, using artificial intelligence (AI) approaches, has attracted a great deal of research interest. ...
The use of artificial intelligence to automate PV module fault detection, diagnosis, and classification processes has gained interest for PV solar plants maintenance planning and reduction in expensive inspection and shutdown periods. The present article reports on the development of an adaptive neuro-fuzzy inference system (ANFIS) for PV fault classification based on statistical and mathematical features extracted from outdoor infrared thermography (IRT) and I-V measurements of thin-film PV modules. The selection of the membership function is shown to be essential to obtain a high classifier performance. Principal components analysis (PCA) is used to reduce the dimensions to speed up the classification process. For each type of fault, effective features that are highly correlated to the PV module’s operating power ratio are identified. Evaluation of the proposed methodology, based on datasets gathered from a typical PV plant, reveals that features extraction methods based on mathematical parameters and I-V measurements provide a 100% classification accuracy. On the other hand, features extraction based on statistical factors provides 83.33% accuracy. A novel technique is proposed for developing a correlation matrix between the PV operating power ratio and the effective features extracted online from infrared thermal images. This eliminates the need for offline I-V measurements to estimate the operating power ratio of PV modules.
... The thermal infrared detection method can distinguish and locate the modules with abnormal surface temperature caused by faults. In (Wang and Zheng, 2010), a method based on infrared image analysis is presented that can automatically identify the working status of PV arrays by combining a fuzzy reasoning technique. By utilizing the infrared images of the PV modules, faults such as cracks, broken grids and black spots of the modules can be differentiated and located (Nian et al., 2010). ...
Solar photovoltaic (PV) power generation has been widely used because of its environment-friendly advantages. However, operational faults in PV arrays have always been one of the critical factors affecting the PV system’s power-generation efficiency and life cycle. This paper proposes a fault locating strategy, which does not need the current sensor in every PV string and can also minimize the number of voltage sensors between strings, to accurately locate the faulty PV modules in a PV array. A variety of faults including open circuit, short circuit, degradation and partial shading faults are considered, and a universal method is proposed in this paper to locate the faulty modules under these faults. In this method, the voltage sensors are deployed through differentiating the parity of the number of strings for the PV array, while the fault locating rules are formulated under each of the afore-mentioned faults, respectively. Compared with existing fault locating methods, the proposed locating technique is shown to be effective in the application to PV arrays with any size and capacity, and it can accurately locate each faulty module for those faults, especially for degradation and partial shading faults. Thus, it is helpful in PV array dynamic reconfiguration and maintenance cost reduction.
... The existing fault diagnosis methods for PV modules fall into two categories: online diagnosis and offline diagnosis. Typical online diagnosis approaches include infrared imagery [4] and multi-sensor detection [5,6]. The infrared imagery makes use of the apparent temperature differences between normal and fault PV modules. ...
... The devices can detect the faults including black pieces, fragmentation, broken grid and crack for the PV modules. Peizhen and Shicheng [7] propose a method that can automatically analyze and recognize the working status of the PV arrays based on infrared image analysis. The method can accurately identify the normal, shading and degradation status of the PV modules. ...
Photovoltaic (PV) power generation systems work chronically in various climatic outdoor conditions, therefore, faults may occur within the PV arrays in PV systems. Online fault detection for the PV arrays are important to improve the system’s reliability, safety and efficiency. In view of this, a fault-detection method based on voltage and current observation and evaluation is presented in this paper to detect common PV array faults, such as open-circuit, short-circuit, degradation and shading faults. In order to develop this detection method, fault characteristic quantities (e.g., the open-circuit voltage, short-circuit current, voltage and current at the maximum power point (MPP) of the PV array) are identified first to define the voltage and current indicators; then, the fault-detection thresholds are defined by utilizing voltage and current indicators according to the characteristic information of various faults; finally, voltage and current indicators evaluated at real-time voltage and current data are compared with the corresponding thresholds to detect potential faults and fault types. The performances of the proposed method are simulated verifying by setting eight different fault patterns in the PV array. Simulation experimental results show the effectiveness of the proposed method, especially the capacities of distinguishing the degradation faults, partial shading faults and variable shading faults.
... Infrared imaging not only increases the cost, but also it is affected by environmental factors, which leads to inaccurate fault measurement. In [2], according to the thermodynamic characteristics of the photovoltaic module under different working conditions, the infrared image analysis fault detection method is proposed, but it is easily influenced by seasonal environment factors, and the cost of investment is relatively large. In [3], the multi-sensor detection method is proposed. ...
... Representative online diagnostic methods are infrared image detection, multi-sensor method and the PV array fault diagnosis model based on back propagation (BP) neural network. Infrared image detection method can determine the PV module fault type and fault location by analysing the infrared image of the PV module which has a significant temperature difference characteristics, in the normal and faulty states, captured by the infrared camera [2,3]. The principle of the multi-sensor method is to install voltage and current sensors for one or several PV modules, and to determine the fault type and fault location of the PV array by analysing the collected voltage and current data [4,5]. ...
... Even if they can find the fault existing in the PV systems, these methods could not locate the position in it. The methods of using infrared image for fault diagnosis are introduced in [3,10]. According to the distinct temperature difference characteristic between solar cells under different operation states, the normal, shadowed and aging destroyed operation states of PV arrays can be recognized accurately by pre-processing and analysing the infrared images of PV arrays. ...
Operating Statuses Identification (OSI) can help operators to find fault timely and minimize the loss. So it is of great significance for optimal operation of photovoltaic (PV) plants. The loss quantity of electricity (LQOE) is defined as that should have been generated but actually not, which is caused by inverter fault, PV modules fault, dust stratification or combinations of them. Firstly, mathematical models of PV cells are proposed to figure out the LQOE of each PV module. Secondly, after analysing the relation between LQOE and operating statuses, an index set of LQOE describing the distinction of different operating statuses are defined including four statistical feature parameters and a user-defined index. Thirdly, Support Vector Classification models for OSI (OSI-SVC) are built with input features extracted from the index set. Lastly, simulations are carried out to verify the effectiveness and evaluate the performance of the OSI-SVC models. The results indicated that the operating statuses can be effectively recognized by the proposed model.
... Currently, there are several common fault diagnoses based on infrared picture analysis, based on electrical measurement, based on intelligent detection and based on monitoring system，respectively. Some authors took advantage of the thermic effect of photovoltaic cells to detect PV module by means of infrared technique [2][3][4]. By analyzing the characteristics of infrared images, the status of the PV module can be obtained. ...
The power loss and the changes of internal I-V output characteristics of photovoltaic (PV) module in the typical fault condition were analyzed. We proposed an on-line real time fault diagnosis method for PV module, which takes into account the power loss and the internal I-V characteristics. Taking into account the changes of temperature and irradiation, the running status of the PV module were simulated in real time. Firstly, by comparing the simulated power with the measured power, it could determine whether the abnormal power loss has occurred. Then based on the change of output voltage, it could decide if short-circuit fault has occurred and estimate the number of short circuited cells roughly. Further, the value of fill factor (FF) can be utilized to determine whether aging fault has occurred and to acquire the remaining service life of the module. The results of simulation and experiment show that this method can effectively detect the partial shadow short-circuit fault and aging fault. It proves the feasibility and accuracy of the fault diagnosis method.
... Solar PV power generation has the advantages of clean, no pollution, sustainability and broad [2]. Therefore, the use of solar power has been widely valued by many countries [3]. However, the PV array works in the complex outdoor environment. ...
... Even if they can find the fault existing in the PV systems, these methods could not locate the position in it. The methods of using infrared image for fault diagnosis are introduced in [3,10]. According to the distinct temperature difference characteristic between solar cells under different operation states, the normal, shadowed and aging destroyed operation states of PV arrays can be recognized accurately by pre-processing and analysing the infrared images of PV arrays. ...
... First, the subtraction operation is carried out between the two infrared images of photovoltaic array which are to be analyzed and under normal condition respectively. Then, a method of combining the two dimensional threshold and the fuzzy means algorithm is implemented [5] [6] . ...
The overall efficiency of PV arrays is affected by hot spots which should be detected and diagnosed by applying responsible monitoring techniques. The method using the IR thermal image to detect hot spots has been studied as a direct, noncontact, nondestructive technique. However, IR thermal images suffer from relatively high stochastic noise and non-uniformity clutter, so the conventional methods of image processing are not effective. The paper proposes a method to detect hotspots based on curve fitting of gray histogram. The result of MATLAB simulation proves the method proposed in the paper is effective to detect the hot spots suppressing the noise generated during the process of image acquisition.
... Solar PV power generation has the advantages of clean, no pollution, sustainability and broad [2]. Therefore, the use of solar power has been widely valued by many countries [3]. However, the PV array work in the complex outdoor environment. ...
The vast majority of photo voltaic (PV) arrays often work in harsh outdoor environment, and undergo various fault, such as local material aging, shading, open circuit, short circuit and so on. The generation of these fault will reduce the power generation efficiency, and even lead to fire disaster which threaten the safety of social property. In this paper, an on-line distributed monitoring system based on ZigBee wireless sensors network is designed to monitor the output current, voltage and irradiate of each PV module, and the temperature and the irradiate of the environment. A simulation PV module model is established, based on which some common faults are simulated and fault training samples are obtained. Finally, a genetic algorithm optimized Back Propagation (BP) neural network fault diagnosis model is built and trained by the fault samples data. Experiment result shows that the system can detect the common faults of PV array with high accuracy.
Abnormal conditions of field photovoltaic (PV) array such as open-circuit, short-circuit, and partial shading are embedded in DC side voltage/current curves. Besides, meteorological factors as solar radiation, wind speed, and ambient temperature can also influence fault behaviors. To realize abnormal identify of PV array under environmental interference, this paper presents a graph attention network (GAT) based fault detection algorithm for PV array. Fault simulation experiments are conducted on grid-connected PV array, and the voltage/current curves of the PV DC side under different states (normal, open-circuit, short-circuit, and partial shading) were collected to build dataset. One-dimensional convolution and two parallel graph attention layers are adopted to extract temporal and dimensional features of the voltage/current series. A gated recurrent unit (GRU) is employed to capture the long-term dependencies of the time-series data. Fully connected (FC) layers and variational auto-encoder (VAE) are combined optimized for detecting and locating the PV abnormal events. Model performance are compared with Robust Anomaly Detection (OmniAnomaly), Transformer Networks for Anomaly Detection (TranAD), and Long Short-Term Memory (LSTM), result show that the proposed grid-connected PV array fault detection model achieves an accuracy of 96.8% on the test dataset, providing an effective method for fault diagnosis of grid-connected PV systems under different meteorological conditions.
The photovoltaic (PV) panel’s output energy depends on many factors. As they are becoming the leading alternative energy source, it is essential to get the best out of them. Although the main factor for maximizing energy production is proportional to the amount of solar radiation reaching the photovoltaic panel surface, other factors, such as temperature and shading, influence them negatively. Moreover, being installed in a dynamic and frequently harsh environment causes a set of reasons for faults, defects, and irregular operations. Any irregular operation should be recognized and classified into faults that need attention and, therefore, maintenance or as being a regular operation due to changes in some surrounding factors, such as temperature or solar radiation. Besides, in case of faults, it would be helpful to identify the source and the cause of the problem. Hence, this study presented a novel methodology that modeled a PV system in a tree-like hierarchy, which allowed the use of a fuzzy nonlinear autoregressive network with exogenous inputs (NARX) to detect and classify faults in a PV system with customizable granularity. Moreover, the used methodology enabled the identification of the exact source of fault(s) in a fully automated way. The study was done on a string of eight PV panels; however, the paper discussed using the algorithm on a more extensive PV system. The used fuzzy NARX algorithm in this study was able to classify the faults that appeared in up to five out of the eight PV panels and to identify the faulty PV panels with high accuracy. The used hardware could be controlled and monitored through a Wi-Fi connection, which added support for Internet of Things applications.
A variety of faults often occur during the operation of PV arrays, which may seriously affect the normal operation of the system, the machine diagnosis of the types of fault has become a hotspot in the field of photovoltaic power generation. This paper proposes a novel fault diagnostic technique for photovoltaic systems based on Cascaded Forest. Through the in-depth analysis of the output of PV arrays from a data platform of Shijiazhuang Kelin Electric Co, the input variables of the diagnosis model are obtained. Compared with other fault diagnosis methods for the PV array, the proposed method can work under a small number of tagged data and the system can be run online and real-time. Finally, the experimental results show that the fault diagnosis method for the PV array based on the cascading forests can effectively detect four types of fault for PV array such as short-circuit, open-circuit, abnormal degradation and partial shading. This method has a good value for the intelligent fault diagnosis of PV.
Due to weathering and external forces, solar panels are subject to fouling and defects after a certain amount of time in service. These fouling and defects have direct adverse consequences such as low-power efficiency. Because solar power plants usually have large-scale photovoltaic (PV) panels, fast detection and location of fouling and defects across large PV areas are imperative. A drone-mounted infrared thermography system was designed and developed, and its ability to detect rapid fouling on large-scale PV panel systems was investigated. The infrared images were preprocessed using the K neighbor mean filter, and the single PV module on each image was recognized and extracted. Combining the local and global detection method, suspicious sites were located precisely. The results showed the flexible drone-mounted infrared thermography system to have a strong ability to detect the presence and determine the position of PV fouling. Drone-mounted infrared thermography also has good technical feasibility and practical value in the detection of PV fouling detection. © 2017 Society of Photo-Optical Instrumentation Engineers (SPIE).
As scale of solar photovoltaic application continues to expand, real-time state monitoring, fault diagnosis and fault positioning for photovoltaic(PV) array are becoming more and more important. A kind of PV array sensor embedding method is put forward to reduce usage of sensors on the basis of assure positioning precision. With the structure the consistency and strategic decision problems among the measurement data in diagnosis is solved using fuzzy group decision theory. Experimental result verifies the proposed fault diagnosis and positioning method for PV array has strong ability of anti-interference and increases accuracy and reliability of diagnosis.
The fault diagnosis modes and fault reasons of grid-connected photovoltaic power system were described, and as to the possible faults of solar grid-connected power system, a method of fault diagnosis based on BP neural network was proposed. The fault information was used as samples to train the BP neural network, and the relations between the network fault information and fault patterns output were established utilizing its strong self-adaptation and nonlinear mapping ability. The test results demonstrate the effectiveness and feasibility of this method. It not only has the characteristics of high diagnostic accuracy, but also is easy to implement, so it can be applied into fault iagnosis of grid-connected photovoltaic power system.
Because PV arrays are always installed in poor outdoor environment, a variety of faults often occur during the operation. In order to obtain the types of fault, a fault diagnosis method of the BP neural network based on L-M algorithm is proposed. Through the in-depth analysis of the output of the PV array under normal state and fault states, the input variables of the diagnosis model are obtained. Compared with other fault diagnosis methods for the PV array, the proposed method does not need additional equipments, so the cost is reduced and the system can be run online and real-time. Finally, the simulation and experimental results show that the fault diagnosis method for the PV array based on the BP neural network can effectively detect four types of fault for PV array such as short-circuit, open-circuit, abnormal degradation and partial shading.
To improve generating efficiency of photovoltaic (PV) generation system and decrease the cost for its artificial maintenance, based on back propagation (BP) neural network (NN) an online fault diagnosis strategy for PV modules is proposed. The contributing factors causing short-circuit fault and abnormal aging of PV modules are analyzed and using Matlab the output characteristics of PV module under fault condition is simulated. According to simulation results and combining with mathematical model of PV module, the fault patterns of PV module are summarized, and a BPNN based fault diagnosis model for PV modules is built and a model to simulate various faults occurred in PV modules is established. The samples suitable for the training of BPNN are collected and the BPNN based fault diagnosis model is trained. Combining with PV power optimizer the simulation and experimental research on online fault diagnosis of PV modules are performed, and results from simulation and research show that the proposed fault diagnosis strategy is correct, effective and possesses environmental suitability.
Photovoltaic (PV) array output characteristics under fault conditions were simulated, the changing regularity of them was summarized. Fault diagnosis method based on branch current and PV module voltage detection was studied, three different voltage sensor placement methods were compared and the optimal sensors placement method was designed. PV array was equivalent to matrix, and weight matrix was established. This paper proposed a method of optimal voltage sensor placement based on covering all weight nodes. By the proposed method, a 3 × 3 PV array was researched, and voltage sensor characteristic values under different fault conditions were also measured. On the base of PV array fault diagnosis method, this paper also proposed maximum power point tracking (MPPT) strategy under shadow. Experimental results were presented to validate the proposed method.
Partial shading instances are very common in photovoltaic (PV) systems. Partial shading will lead to the decrease of the PV system output power, even cause a hot spot phenomenon which may destroy the structure of the PV cell and in severe cases it may cause fire. In this paper, the output characteristics of PV module under partial shading condition was deeply studied, and the changing regularity of the output characteristics of PV module with hot spot was summarized. Then a method to detect whether there is a hot spot using the slope and the short-circuit current was proposed. Based on the method, this paper also proposed a fault diagnosis and optimal control strategy based on fuzzy control. Through the scan of output characteristic of PV module and the fuzzy diagnosis, the hot spot detection and the optimal control has been realized, and the service life of PV module has been extended. Finally, simulation and experimental results were presented to validate the proposed method.
The output characteristics of photovoltaic (PV) modules under short-circuit or abnormal degradation conditions were analyzed. The online fault diagnosis method for PV modules based on four parameters, namely open circuit voltage, short-circuit current, the voltage of maximum power point and the current of maximum power point were proposed. Then the fault type factor K was introduced. Through comparing the difference between the value of K and the standard value, the type of faults could be determined. Once the faults are certain, the extent of faults and the early warnings are analyzed automatically online. When the PV modules are short-circuited, the artificial neural network can be utilized for acquiring the number of short-circuited cells. When the PV modules are in abnormal degradation, the value of fill factor (FF) can be utilized to acquire the extent of degradation. The results of simulations and experiments show that the method has a high accuracy rate, and its feasibility and effectiveness are proved.
Photovoltaic (PV) array is often composed with a lot of photovoltaic modules in series or in parallel, which make it difficult to obtain the accurate fault locations when some PV modules are failure. To well dispose this problem, a novel strategy of fault location by use of Gaussian process(GP) regression is presented in this paper. First, binary strings are designed to represent the states of the PV array, and then a regression model of the fault location by converting the binary strings into corresponding integers is presented. Based on the voltage and current as well as the converted integers, the Gaussian process is trained. With newly measured voltage and current, the current state of the PV array is diagnosed by using the trained Gaussian Process. The proposed algorithm is applied to a 2×4 PV array and verified via four different fault states, i.e., one module, two modules, three modules and four modules are failure. By comparing with the common used BP network in fault diagnosis and location, the effectiveness of our algorithm is empirically demonstrated. The results show that the Gaussian Process based method outperforms BP network one in more accurately locating the faults. Furthermore, the proposed algorithm is more benefit for the first three fault states than the one that four modules are failure.
To exactly judge the operating conditions of photovoltaic (PV) modules, a parameter identification based fault diagnosis model for PV modules is proposed. The output characteristic curves of PV modules under arbitrary operating modes are analyzed, and by means of improved artificial fish swarm algorithm, parameters in the mathematical model are identified. Through analyzing the variation of the model parameters with the change of irradiance and temperature the model parameter values under multiple operating modes are obtained, and according to various fault data of PV modules a radical basis function (RBF) neural network based fault diagnosis model, in which the photo-generated current, the reverse saturation current of the diode, the ideal quality factor of the diode and equivalent series-parallel resistance are taken as the input layer vectors and the normality of the module, the short-circuit of the module, the abnormal ageing of equivalent series resistance and the abnormal ageing of equivalent series-parallel resistance are taken as output layer vectors, is established, and the correctness of above-mentioned fault rule of PV modules is validated by simulation results. A programmable electronic load based outdoor experimental platform for PV module is constructed to carry out experimental research on fault diagnosis of PV modules. The effectiveness and accuracy of the proposed method are validated by experimental results.
This paper provides proof of concept for a technique that uses high resolution thermal infrared imager to detect faults for outdoor operation photovoltaic array. An experimental study focused on two situations was tested: abnormal emitting heat of monocrystalline silicon solar cells (hot spot); open circuit fault of photovoltaic array. Experimental results show that the infrared image could clearly give prominence to the faulty solar cell or PV array. The back surface temperature of solar cell with hot spot is higher than normal ones with 13.2°C in mean value，and the back surface temperature of open circuit PV array is higher than normal ones with 2.8°C in mean value.
Traditionally, faulted snapshot device is diagnosed artificially, which is inefficient and has low accuracy. This paper proposes a method of fault diagnosis for snapshot device through identifying abnormal images caught by the device. First, nine types of image abnormal features are classified and described, abnormal features are also mapped to different faults of snapshot device. Second, the distribution of three channels, R, G and B of original color image caught by the snapshot device are calculated and analyzed , the features of mean gray value,distribution of gray value and region features in gray image are analyzed, white light beam in binary image,as well as aspect ratio are both researched deeply. Third, different algorithms are studied to identify different image abnormal features and then the whole diagnosis method is developed. Finally, the proposed method is proved to have a high efficiency and accuracy in a case.
An experimental study has been carried out on the dependence of the hot-carrier highly nonlinear photovoltaic effect induced in silicon n+/p junctions by 10.6 μm laser pulses on the lattice temperature. A four orders of magnitude increase in the photovoltage was observed for a lattice temperature increase of 150 K. A model based on the hot-carrier plasma generated by the laser radiation fit the experimental results. Sensitivity can possibly be further improved by increasing the junction temperature. © 2002 American Institute of Physics.