Fault Diagnosis Of Photovoltaic Arrays Research Articles

Fault Diagnosis of PV Array Based on Time Series and Support Vector Machine

This paper proposes a diagnosis method based on time series and support vector machine (SVM) to improve the timeliness, accuracy, and feasibility of fault diagnosis for photovoltaic (PV) arrays. It obtains the nominal output power of the PV array based on real-time collected data such as voltage, current, radiation, and temperature and normalizes the power values at different time points throughout the day to form a time series. Using the time series values as input data for a “one-to-one” multiclass classifier, we can identify and classify typical operational faults such as random shading, fixed shading, and aging degradation of PV arrays. The developed algorithmic model is trained and tested for different fault conditions using the data sets generated by the PV array simulation device. The experimental results show that our model has fairly good reliability and accuracy, and to some extent, it solves the problem of classifying shading and aging faults, two of which exhibit rather similar degradation characteristics.

Feature extraction and fault diagnosis of photovoltaic array based on current–voltage conversion

Fault diagnosis plays a crucial role in the operation and maintenance (O&M) of photovoltaic (PV) arrays, and reasonable feature extraction is a prerequisite for effective fault diagnosis. In this paper, a feature extraction and fault diagnosis method based on current–voltage (I–V) conversion is proposed. First, the PV array modeling method based on the double diode model (DDM) and the reverse bias model (RBM) is proposed. This modeling method can simulate the I–V curves under different states and provide data foundation for feature extraction and fault diagnosis. Next, three procedures for correcting I–V curves and three feature enhancement methods are compared to select the optimal program for I–V conversion. The converted feature matrix is dimensionalized using T-distributed stochastic neighbor embedding (T-SNE) to achieve feature extraction. Finally, ten classification models for fault diagnosis are adopted to verify the effectiveness of the proposed feature extraction method. Experimental results demonstrate that the proposed methods perform well on simulation data and provide satisfactory fault diagnosis results for the measured I–V curves. Among the classification models tested, the variable prediction model (VPM) shows the optimal comprehensive performance, with the computational time of 0.17 s and the accuracy of 99.4%.

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

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

Photovoltaic arrays fault diagnosis based on an improved dilated convolutional neural network with feature-enhancement

Photovoltaic (PV) arrays are installed outdoors and prone to abnormalities and various faults under harsh natural conditions, reducing power conversion efficiency and the life of the PV modules, and even causing electric shock and fire. Current fault diagnosis methods are unable to accurately identify and locate faults in PV arrays in PV power systems, leading to increased operation and maintenance costs. Therefore, the feature-enhancement improved dilated convolutional neural network (CNN) is proposed for fault diagnosis of PV arrays in this paper. Firstly, aim at the problem of information loss due to data structure and spatial hierarchy within the traditional CNN, and the loss of data after down-sampling, which leads to the inability to reconstruct information, a dilated convolution is introduced to obtain a larger perceptual field while reducing the computational effort. Meanwhile, the adaptive dual domain soft threshold group convolution attention module is proposed to enhance the essential features of faults and reduce the information redundancy given the ambiguity and blindness of the feature data in PV array fault extraction. Finally, the model performance of the proposed model is validated and the operability and effectiveness of the proposed method are verified experimentally. The diagnostic results show that the average diagnostic accuracy of the proposed model is 98.95% compared with other diagnostic models, with better diagnostic accuracy and more stable diagnostic performance.

Implicit Mathematical Model of Photovoltaic Arrays with Improved Calculation Speed Based on Inflection Points of the Current–Voltage Curves

Dynamic reconfiguration, the monitoring of power production, and the fault diagnosis of photovoltaic arrays, among other applications, require fast and accurate models of photovoltaic arrays. In the literature, some models use the Lambert-W function to represent each module of the array, which increases the calculation time. Other models that use implicit equations to avoid the Lambert-W function do not use the inflection voltages to simplify the system of nonlinear equations that represent the array, increasing the computational burden. Therefore, this paper proposes mathematical models for series-parallel (SP) and total-cross-tied (TCT) photovoltaic arrays based on the implicit equations of the single-diode model and the inflection points of the current–voltage curves. These models decrease the calculation time by reducing the complexity of the nonlinear equation systems that represent each string of SP arrays and the whole TCT. Consequently, the calculation process that solves the model speeds up in comparison with processes that solve traditional explicit models based on the Lambert-W function. The results of several simulation scenarios using the proposed SP model with different array sizes show a reduction in the computation time by 82.97% in contrast with the traditional solution. Additionally, when the proposed TCT model for arrays larger than 2×2 is used, the reduction in the computation time is between 47.71% and 92.28%. In dynamic reconfiguration, the results demonstrate that the proposed SP model provides the same optimal configuration but 7 times faster than traditional solutions, and the TCT model is solved at least 4 times faster than classical solutions.

An embedded system for remote monitoring and fault diagnosis of photovoltaic arrays using machine learning and the internet of things

In this paper a novel embedded system for remote monitoring and fault diagnosis of photovoltaic systems is introduced. The idea is to embed machine leaning algorithms into a low-cost edge device for real-time deployment. First, an artificial neural network is developed to detect faults. Then an effective stacking ensemble learning algorithm is developed to classify the nature of the fault. The method performance is evaluated through common error metrics such as RMSE, MAE, MAPE, r and confusion matrix. Additional algorithms are also embedded into the edge device in order to remotely control the photovoltaic array parameters. Users can be notified by email and SMS about the state of their photovoltaic array. The Blynk IoT platform is used to monitor remotely the photovoltaic array parameters. The experimental results demonstrate the ability of the proposed embedded system to diagnose and monitor the photovoltaic array with a good accuracy.

An IoT and Semi-Supervised Learning-Based Sensorless Technique for Panel Level Solar Photovoltaic Array Fault Diagnosis

The performance of solar photovoltaic (SPV) systems is significantly affected by faults in the SPV arrays. Changes in environmental conditions and nonlinear characteristics of solar cells make it challenging to identify faults by conventional protection schemes. Existing learning-based fault diagnosis approaches require voluminous data and sensors, making them costly and challenging to integrate with existing SPV systems. Moreover, the lack of panel-level monitoring in existing approaches renders them incapable of pinpointing the precise location of the faulty panel. To address these issues, this paper proposes a methodology for detecting faults, localizing the exact faulty panel, and accurately classifying faults in SPV arrays. The methodology involves a sensorless electronic circuit using a bipolar junction transistor and Zener diode for fault detection, an IoT-based web application for monitoring individual solar panels for fault localization, and a deep autoencoder-based semi-supervised learning module followed by a hybrid support vector machine and logistic regression for fault classification. The proposed methodology is a low-cost and less data-intensive solution that results in precise fault detection, exact faulty panel localization, and fault classification with an overall accuracy of 99.67%. The methodology is validated in a laboratory-scale real-time setup of a grid-connected SPV system under varying electrical and environmental conditions.

Online fault diagnosis of PV array considering label errors based on distributionally robust logistic regression

This paper proposes a robust diagnosis method of photovoltaic (PV) array faults considering label errors in training data. First, the online data of PV systems, including the sequences of voltages, currents, and output power at maximum power points, are used to establish the input data of fault diagnosis. Second, a data processing method is used to extract fault features from electrical signals under fluctuating ambient conditions. Third, the parameter estimation of the regression-based fault diagnosis model is formulated as a stochastic optimization problem. To hedge against label errors, an ambiguity set of probability distributions is established from training data, and a distributionally robust logistic regression method is proposed to minimize the expected log-loss function under the worst-case probability distribution for obtaining model parameters of fault diagnosis. Finally, the proposed method is tested on real-world PV arrays under diverse conditions and scenarios. Data processing increases diagnosis accuracy by 18.4% when training data is error-free. The diagnosis accuracy is higher than 98% when the label error rate is smaller than 4%.

Stacking ensemble based fault diagnosis approach for improved operation of photovoltaic arrays

ABSTRACT The operation of photovoltaic (PV) systems, like any other system, in fault free environment ensures maximum performance. Hence, accurate and timely fault identification of PV system demands greater importance. Present investigation proposes a stacking ensemble-based PV array fault diagnosis method, which is integrated with automatic feature engineering and selection technique, handling of imbalanced dataset for unbiased classification. The proposed ensemble utilizes decision tree (DT), random forest (RF), extra trees (EXT), extreme gradient boosting machine (XGBoost) as base learners, and light gradient boosting machine (LightGBM) as both base and meta-learner. To validate the proposed technique, a test system of 4.8kWp capacity has been built using the MATLAB/Simulink environment incorporating one-year real-time irradiance and module temperature data. Irradiance, module temperature, voltage, current, and power are collected as primary raw data which are then concocted using the autofeat python library for automatic feature engineering and selection. Subsequently, the proposed fault diagnosis strategy is built in python 3.8.5 using the engineered and class label balanced dataset, prepared using the synthetic minority over-sampling technique (SMOTE). Results demonstrate promising performance of the proposed ensemble technique with 97.04% accuracy in classifying the different faults in the PV array, which accounts for approximately 1–3% improvement over other individual machine learning (ML) models.

Intelligent fault diagnosis of photovoltaic array based on variable predictive models and I–V curves

Photovoltaic (PV) arrays are installed in outdoors and continuously exposed to harsh environmental conditions, which are susceptible to suffer from abnormal and various kinds of faults, reduce the efficiency of power conversion and the lifetime of PV modules, and even cause electric shock and fire. In recent years, many methods of machine learning and deep learning have been successfully applied in the field of fault diagnosis for PV array. However, these methods still exist some issues: neural network training requires a large number of data samples and is prone to overfitting and underfitting; network structure and hyperparameters are difficult to determine, mainly rely on extensive experiments; The kernel extreme learning machine (KELM) and support vector machine (SVM) require kernel functions and hyper parameter adjustment, and the selection of kernel functions and hyperparameter will directly affect the accuracy of fault diagnosis; To solve the above issues, an intelligent fault diagnosis method for PV array based on the variable predictive models and I–V curves is proposed. Firstly, the impact of different operating states on the I–V curve of PV array under standard test condition (STC) is analyzed. Then, the five-dimensional effective feature variables are extracted from the key points of the measured I-V curve as input of the fault diagnosis model. Since the output of the electrical characteristics of the PV array is affected by faults and environmental factors, the extracted feature variables are normalized to eliminate the influence of environmental factors. Finally, the normalized feature variables are used as the input of variable predictive model to identify the fault types of the PV array, which reduces the computational cost and avoids the problem that the kernel function and hyperparameter are difficult to determine. Moreover, the proposed method is verified by simulation and measured data and compared with other machine learning algorithms. The results indicate that the proposed fault diagnosis method has high accuracy and reliability.

Performance assessment of selective machine learning techniques for improved PV array fault diagnosis

Solar photovoltaics (SPV) are susceptible to various kinds of faults which can diminish overall performance of the system. Proper fault diagnosis strategy needs to be developed to accurately identify the faults for smooth operation of the photovoltaic (PV) systems. Machine learning (ML) can be used to diagnose the faults in PV arrays. In this paper, three powerful machine learning algorithms i.e., categorical boosting (CatBoost), light gradient boosting method (LGBM), and extreme gradient boosting (XGBoost) have been selected for investigating their efficacy to diagnose different PV array faults. A PV system has been designed in MATLAB/Simulink environment using real time irradiance and temperature data acquired from grid connected PV System of National Institute of Technology Agartala. The constructed dataset is used to extract features including one new index to train these algorithms in Python 3.7. Promising results have been achieved using these algorithms as average detection and classification accuracy of 99.996% and 99.745% has been noted by implementing LGBM, followed by CatBoost, and XGBoost respectively. Moreover, these algorithms reduce the computational time significantly with LGBM leading the chart with training time of 0.053 and 0.375 s for fault detection and classification. These algorithms have been compared with random forest (RF) technique to exhibit their proficiency in fault diagnosis of PV arrays. • Assessment of CatBoost, LGBM and XGBoost algorithms for PV array fault diagnosis. • A new performance index based on array capture loss of PV array. • Selected methods detect various types of faults in PV array with high accuracy. • Applied techniques can detect and classify faults with very less training time. • Methods are scrutinized with various performance indicators to confirm efficacy.

Assessment of machine learning and ensemble methods for fault diagnosis of photovoltaic systems

The photovoltaic (PV) array is the most sensible element in PV plants, which is subject to different type of faults and defects. Thus, to keep these plants working efficiently they should be monitored and protected carefully. Some faults if they are not detected and isolated promptly they may lead to hazardous risks. The diagnosis of PV systems is widely addressed and recently machine learning (ML) and deep leaning (DL) methods drawn the attention of many researchers. Most applications of ML methods are based on the use of the I–V curves measurement, as enough information and features can be extracted from the curves, to detect and classify faults. These methods showed their capability to classify some faults, like line to line, degradation, disconnected PV modules, partial shading effect, and bypass diode faults. Another approach is based on the use of thermal or electroluminescence images of PV modules/arrays to detect and identify defects, such as hot spot, snails crack, and others. In this paper, different ML and ensemble learning (EL) methods are evaluated for fault diagnosis of PV arrays. The focus is mainly on the detection and classification of some complex faults that may affect the PV arrays, i.e., multiple faults, and faults with similar I–V curves, that are not evaluated before. The results showed the ability of the methods developed to detect faults with very good accuracy (classification rate = number of classified instances/total instances), within 99%, while the classification faults is done with an acceptable accuracy, within 81.73%. Through this study it is shown when really ML and EL methods should be used, and some recommendations, challenges and future directions in this topic are presented.

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.

Fault Detection and Diagnosis Method of Distributed Photovoltaic Array Based on Fine-Tuning Naive Bayesian Model

With the widespread attention and research of distributed photovoltaic (PV) systems, the fault detection and diagnosis problems of distributed PV systems has become increasingly prominent. To this end, a distributed PV array fault diagnosis method based on fine-tuning Naive Bayes model for the fault conditions of PV array such as open-circuit, short-circuit, shading, abnormal degradation, and abnormal bypass diode is proposed. First, in view of the problem of less distributed PV fault data, a fine-tuning Naive Bayes model (FTNB) is proposed to improve the diagnosis accuracy. Second, the failure sample set is used to train the model. Then, the maximum power point data of the PV inverter and the meteorological data are collected for fault diagnosis. Finally, the effectiveness and accuracy of the proposed method are verified by the analysis of simulation. In addition, this method requires only a small number of fault sample sets and no additional measurement equipment is required, which is suitable for real-time monitoring of distributed PV systems.

A robust parametrization method of photovoltaic modules for enhancing one-diode model accuracy under varying operating conditions

Modeling the current-voltage (I–V) characteristics of photovoltaic (PV) modules is of importance for condition monitoring and fault diagnosis of PV arrays. The practicality of the one-diode model (ODM) is relying on the behaviors of parameter extraction and translation techniques. In this paper, a simple and robust dual-iteration algorithm is presented to extract the ODM parameters using only three points under limited conditions. These points can be easily obtained from both the datasheet and measurements, making the method universal and practical. Parameters under other unknown conditions are translated from the nearest acquired condition to make the proposed method applicable for various conditions. The modeling I–V curve obtained by the proposed method is in a close agreement with the field measurement, demonstrating its effectiveness to characterize the PV modules with different technologies. Moreover, three existing models are used to benchmark the accuracy of the proposed method. The comparison further confirms that the accuracy of the ODM has been enhanced especially at low irradiance using the proposed parameter translation method. Furthermore, a distance-weighted method is presented to reliably translate the parameters, whose superiority has been validated against measurements and the other four methods, thus being an effective output assessment for monitoring and diagnosis.

Fault diagnosis of photovoltaic array based on SE-ResNet

In recent decades, fault diagnosis of photovoltaic (PV) arrays has become more and more important for the power generation of PV systems. Many traditional artificial intelligence methods have been successfully applied to use fault data samples to build fault diagnosis models, but most rely on manual feature extraction or expert knowledge to build diagnosis models, which is inefficient and may ignore some potentially useful features. Therefore, this paper proposes an SE-ResNet PV array fault diagnosis algorithm based on Residual Network (ResNet) and Squeeze-and-Excitation Network (SENet), and uses Bayesian Optimization (BO) optimize the parameters. In order to verify the effectiveness of the proposed fault diagnosis algorithm, a comprehensive fault experiment was carried out on the PV platform. Experimental results show that the model has achieved high overall performance in terms of accuracy, generalization performance, reliability and training efficiency, and has high practicability.

Fault diagnosis of photovoltaic array based on deep belief network optimized by genetic algorithm

When using deep belief networks (DBN) to establish a fault diagnosis model, the objective function easily falls into a local optimum during the learning and training process due to random initialization of the DBN network bias and weights, thereby affecting the computational efficiency. To address the problem, a fault diagnosis method based on a deep belief network optimized by genetic algorithm (GA-DBN) is proposed. The method uses the restricted Boltzmann machine reconstruction error to structure the fitness function, and uses the genetic algorithm to optimize the network bias and weight, thus improving the network accuracy and convergence speed. In the experiment, the performance of the model is analyzed from the aspects of reconstruction error, classification accuracy, and time-consuming size. The results are compared with those of back propagation optimized by the genetic algorithm, support vector machines, and DBN. It shows that the proposed method improves the generalization ability of traditional DBN, and has higher recognition accuracy of photovoltaic array faults.

Probability modeling for PV array output interval and its application in fault diagnosis

It is crucial to detect fault arrays timely to ensure the safe and economic operation of large-scale photovoltaic (PV) power plants. The inconsistencies among different arrays and the random fluctuations of PV output result in uncertainties of the PV array output, and the deterministic fault diagnosis methods decrease the accuracy of PV fault diagnosis methods. In this paper, a probability modeling approach for a PV array electrical parameter distribution, which can effectively solve the problems of the nonlinearity and uncertainty of the PV array output interval, is proposed. Actual PV plant data are utilized to calculate fault indicators and analyze the uncertainty of fault indicator distributions. The t-location scale distribution function is used to fit the fault indicator distributions and to establish a probability model of PV fault indicators in different irradiance ranges. Finally, fault indicator thresholds with different degrees of confidence are obtained to complete the PV array fault diagnosis. The setting of the indicator threshold for PV arrays is based on measured data rather than experience, and it can effectively detect different types of faults. The effectiveness of the proposed method is verified in a real PV plant.

Fault diagnosis for photovoltaic array based on convolutional neural network and electrical time series graph

Fault diagnosis of photovoltaic array plays an important role in operation and maintenance of PV power plant. The nonlinear characteristics of photovoltaic array and the Maximum Power Point Tracking technology in the inverter prevent conventional protection devices to trip under certain faults which reduces the system’s efficiency and increases the risks of fire hazards. In order to better diagnose photovoltaic array faults under Maximum Power Point Tracking conditions, the sequential data of transient in time domain under faults are analyzed and then applied as the input fault features in this work. Firstly, the sequential current and voltage of the photovoltaic array are transformed into a 2-Dimension electrical time series graph to visually represent the characteristics of sequential data. Secondly, a Convolutional Neural Network structure comprising nine convolutional layers, nine max-pooling layers, and a fully connected layer is proposed for the photovoltaic array fault diagnosis. The proposed model for photovoltaic array fault diagnosis integrates two main parts, namely the feature extraction and the classification. Thirdly, this model automatically extracts suitable features representation from raw electrical time series graph, which eliminates the need of using artificially established features of data and then employs for photovoltaic fault diagnosis. Moreover, the proposed Convolutional Neural Network based photovoltaic array fault diagnosis method only takes the array of voltage and current of the photovoltaic array as the input features and the reference panels used for normalization. The proposed approach of photovoltaic array fault diagnosis achieved over 99% average accuracy when applied to the case studies. The comparisons of the experimental results demonstrate that the proposed method is both effective and reliable.

A Dilation and Erosion-Based Clustering Approach for Fault Diagnosis of Photovoltaic Arrays

Due to the incremental economic and environmental crisis, solar energy is becoming one of the most promising supplements for electric power generation. In order to eliminate the risks of safety hazards in the Photovoltaic system, fault detection technology is a priority research topic. Since the data distributions of different operation conditions present nonspherical clustering features, various supervised and semi-supervised intelligent learning methods have been widely carried out to detect and classify various fault conditions. However, the number of fault types needs to be determined in advance in these methods. This step takes a lot of time in the process of fault identification. In this paper, an original clustering method based on dilation and erosion theory is proposed. The main advantage of this approach is that the clustering number does not need to be predetermined. The simulation and experimental results verify that the proposed method has the high adaptability and validity under a wide range of meteorological conditions.