Fault Classification in Photovoltaic Arrays Using Graph Signal Processing

The random forest (RF) classifier, which is a combination of tree predictors, is one of the most powerful classification algorithms that has been recently applied for fault detection and diagnosis (FDD) of industrial processes. However, RF is still suffering from some limitations such as the noncorrelation between variables. These limitations are due to the direct use of variables measured at nodes and therefore the only use of static information from the process data. Thus, this article proposes two enhanced RF classifiers, namely the Euclidean distance based reduced kernel RF (RK-RF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ED</sub> ) and K-means clustering based reduced kernel RF (RK-RF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Kmeans</sub> ), for FDD. Based on the kernel principal component analysis, the proposed classifiers consist of two main stages: feature extraction and selection, and fault classification. In the first stage, the number of observations in the training data set is reduced using two methods: the first method consists of using the Euclidean distance as dissimilarity metric so that only one measurement is kept in case of redundancy between samples. The second method aims at reducing the amount of the training data based on the K-means clustering technique. Once the characteristics of the process are extracted, the most sensitive features are selected. During the second phase, the selected features are fed to an RF classifier. An emulated grid-connected PV system is used to validate the performance of the proposed RK-RF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ED</sub> and RK-RF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Kmeans</sub> classifiers. The presented results confirm the high classification accuracy of the developed techniques with low computation time.

Photovoltaic (PV) energy use has been increasing lately and, being highly dependent on environmental variables, its efficiency becomes a major factor for concern. Additionally, these systems can be affected by several kinds of faults, which can lead to a severe energy loss. In this sense, this work compares different machine-learning-based methods, such as K-Nearest Neighbors (k-NN), Decision Trees (DT), Support Vector Machines (SVM), and Artificial Neural Networks (ANN), for detecting the following faults that can occur in Photovoltaic (PV) systems: Module short circuit, MPPT fault, Open Circuit, Partial Shading, and Degradation. The accuracy and computational time taken for training each classifier were compared. ANN achieved the best accuracy, with 99.65%, while being the slowest to train. The SVM achieved a similar result, with significant less training time. There is lack of discussion on the analysis and comparison of PV fault classification methods in the literature, specially with the focus on further practical applications and computational complexity. This way, those points are the main contributions of this work, along with making all simulations and codes publicly available.

The availability of advanced sensors and computing enables early detection of faults and failures in large-scale solar photovoltaic (PV) systems. However, small variations in current and voltage caused by arcing or other faults/failures are often difficult to detect, potentially leading to a permanent reduction in power output and can potentially cause safety hazard. This work develops novel algorithm for detection and classification of DC arc and AC faults using Conditional Generative Adversarial Networks (CGANs). Three distinct CGAN architectures are developed: two for DC arc fault detection and classification, and another for AC faults. The CGAN networks generate output signals representing probability distributions based on voltage and current measurements at the inverter location of the PV system. The performance of the proposed CGAN architectures is evaluated using rolling precision scores on backcasted datasets and compared against a k-Nearest Neighbors (kNN) model, and also validated using real field data. Results show that the proposed architectures achieve high precision in detecting and classifying both types of faults in a 1 MW solar PV system modeled in a real-time simulator.

This study aims to develop a real-time model for detecting and classifying various fault types in power distribution networks to enhance reliability and operational efficiency. A hybrid DWT–SVM approach is essential for accurately detecting and classifying faults in modern power distribution systems, especially under non-stationary and dynamic conditions. The proposed method addresses a range of fault scenarios, including single-phase-to-ground, line-to-line, double-line-to-ground, and three-phase faults, within a 33 kV distribution network. A hybrid approach combining Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM) is introduced. Using the Debauchies-4 (Db4) wavelet, DWT effectively decomposes transient fault currents at the source terminal, capturing critical time-frequency domain features. Fault classification is performed using an SVM optimized with a Radial Basis Function (RBF) kernel and Particle Swarm Optimization (PSO), enabling precise mapping of data into higher-dimensional spaces for optimal separation. Validation conducted with MATLAB R2023b demonstrates a detection accuracy of 100% and a classification accuracy of 99%. Comparative analyses against models such as PSO-based Support Vector Machine (PSO-SVM), DWT-DNN and wavelet transform with artificial neural networks (WT-ANN) on the IEEE 13-bus system highlight the proposed method's superior performance. This innovative approach proves to be robust, adaptable, and highly effective across diverse fault scenarios, offering significant improvements in accuracy and reliability for fault detection and classification in power distribution networks. The method supports real-time operation by using high-speed simulation platforms MATLAB/Simulink to process signals and classify faults within milliseconds using a pre-trained SVM model.

A fault classification method for photovoltaic arrays based on divisible tissue-like P systems

This study explores the efficacy of supervised machine learning regression models in precisely forecasting the Global Maximum Power Point (GMPP) in Photovoltaic (PV) systems under Partial Shading Conditions (PSC). Various supervised machine learning models are examined for the suitability of GMPP tracking during PSC. The study evaluated the effectiveness of Neural Network architectures, including Long Short Term Memory (LSTM), Bidirectional Long Short Term Memory (Bi LSTM), Recurrent Neural Networks (RNN), Support Vector Machines (SVM) and Artificial Neural Networks (ANN) in predicting GMPP. The data set for training was obtained from the PV system, modelled in MATLAB Simulink. All regression models underwent rigorous training in the MATLAB Simulink environment, followed by an analytical review of their performance, during partial shading scenarios. Comparative analysis conducted across different regression models to evaluate their effectiveness in predicting the GMPP in PV systems under PSC suggests that Neural Networks exhibit the capability to forecast the GMPP during PSC scenario. This finding underscores the potential of Feedforward Neural Network (FNN), particularly its deep variant, demonstrated superior performer in terms of predicting the GMPP.

Fast and reliable fault detection is critical in photovoltaic (PV) systems to improve reliability and energy yield and reduce maintenance costs, ensuring safe and efficient operation under varying operating conditions. Although recent data-driven PV fault detection techniques (FDTs) in literature have demonstrated high diagnostic accuracies, they often suffer from practical limitations, offline operation, lack of fault localization and/or inability to concurrently identify faults. To address these challenges, a unified framework is proposed that simultaneously integrates real-time operation, fault classification and localization, and concurrent-fault identification in a single compact diagnostic approach. This is realized by developing a parallel feedforward neural network (FFNN) architecture whose performance is enhanced by a residual model-based structure, resulting in a more interpretable, scalable, reliable and accurate scheme. In addition, Grey Wolf Optimizer–Support Vector Machine (GWO–SVM) feature selection is incorporated to select the most influential diagnostic features, thus reducing data redundancy, enhancing diagnostic accuracy, and shortening training time. The proposed approach was tested to diagnose five types of PV faults (open circuit, short circuit, partial shading, degradation, and simultaneous faults), as well as classify their intensity and location. Simulation results show that the proposed FFNNs consistently outperform conventional Support Vector Machines (SVMs) in classification accuracy, with accuracies exceeding 98% and 99% for fault classification and localization, respectively, and above 95% for noisy data. Moreover, GWO-SVM proved to offer more stable feature subsets compared to Particle Swarm Optimization–SVM (PSO–SVM) in the considered feature selection structure. Simulation results validated the effectiveness of the proposed unified multiclassification fault diagnosis approach, even under system uncertainties, making it suited for real-world PV systems.

This paper addresses electric motor fault diagnosis using supervised machine learning classification. A total of 15 distinct fault types are classified and multilabel strategies are used to classify concurrent faults. we explored, developed, and compared the performance of different types of binary (fault/non-fault), multi-class (fault type) and multi-label (single fault versus combination fault) classifiers. To evaluate the effectiveness of fault identification and classification, we used different supervised machine learning methods, including Random forest classification, support vector machine and neural network classification. Through experiment, we compared these methods over 4 classification regimes and finally summarize the most suitable machine learning algorithms for different aspects of health diagnosis in traction motors area.

This study aims to improve the performance of transmission line directional relaying, fault classification and fault location schemes using fuzzy system. Three separate fuzzy inference system are designed for complete protection scheme for transmission line. The proposed technique is able to accurately detect the fault (both forward and reverse), locate and also identify the faulty phase(s) involved in all ten types of shunt faults that may occur in a transmission line under different fault inception angle, fault resistances and fault location. The proposed method needs current and voltage measurements available at the relay location and can perform the fault detection and classification in about a half‐cycle time. The proposed fuzzy logic based relay has less computation complexity and is better than other AI based methods such as artificial neural network, support vector machine, and decision tree (DT) etc. which require training. The percentage error in fault location is within 1 km for most of the cases. Fault location scheme has been validated using χ2 test with 5% level of significance. Proposed scheme is a setting free method and is suitable for wide range of parameters, fault detection time is less than half cycle and relay does not show any reaching mal‐operation so it is reliable, accurate and secure.

&lt;p&gt;Breast cancer is ranked as a significant cause of mortality among females globally. Its complex nature poses principal challenges for physicians and researchers for rapid diagnosis and prognosis. Hence, machine learning algorithms are employed to forecast and identify diseases. This study discusses the comparative analysis of seven machine learning models, e.g., logistic regression (LR), support vector machine (SVM), k-nearest neighbor classifier (KNN), decision tree classifier (DT), random forest classifier (RF), Naïve Bayes (NB), and artificial neural network (ANN) to predict breast cancer using Wisconsin breast cancer and breast cancer datasets. In the Wisconsin breast cancer dataset, KNN depicted 99% accuracy, followed by RF (98%), SVM (96%), NB (96%), LR (96%), ANN (93%), and DT (92%). On the contrary, in the breast cancer (BC) dataset, the highest accuracy was achieved by LR at 83%, and the lowest was achieved by DT (65%), which depicted that the numeric dataset WBC has better accuracy than the breast cancer dataset.&lt;/p&gt;

I335 the training of Support Vector Machine (SVM) classification model, some problems such as over-fitting, under-fitting, slow training speed and prediction affected by penalty and kernel function parameters are exposed. A fault classification method of rolling bearing based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Grey Wolf Optimization (GWO) -SVM is proposed. Firstly, the acquire signals are adaptively decomposed into a plurality of Intrinsic Mode Function(IMF) component by CEEMDAN, and the energy entropy of each IMF component is extracted to form a set of high dimensional feature vector. Then, the processed feature vectors are introduced into the diagnosis network of GWO-SVM algorithm to build a fault classification model of motor rolling bearing. The classification results show that the CEEMDAN and GWO-SVM fault classification network of motor rolling bearing has higher accuracy and shorter diagnosis time than Particle Swarm Optimization (PSO) -SVM and Cross Validation (CV) -SVM.

Addressing the challenges of complex signal characteristics and difficult fault type differentiation in ultrasonic detection, a fault classification method based on Particle Swarm Optimization Support Vector Machine (PSO-SVM) is proposed. This method utilizes the particle swarm optimization algorithm to globally optimize key parameters in the support vector machine model, overcoming the limitations of traditional support vector machines, such as relying on experience for parameter selection and unstable classification performance. Taking experimental data collected from ultrasonic detection as the research object, the original signals are first subjected to feature extraction and preprocessing. Subsequently, PSO-SVM and traditional SVM models are constructed for fault pattern recognition and classification experiments, respectively. By comparing and analyzing the performance of the two methods in terms of classification accuracy and other performance indicators, the experimental results demonstrate that the PSO-optimized SVM model outperforms the standalone SVM method in both fault classification accuracy and overall recognition effectiveness. This verifies the effectiveness and feasibility of the proposed method in ultrasonic detection fault classification.

Detection and classification are two significant tasks for industrial fault diagnosis. However, conventional methods typically treat these tasks as separate and independent problems, and necessitate a retraining process when new fault samples or classes are collected. Therefore, an incremental support vector data description scheme using Gaussian kernel function is pro-posed for industrial process fault diagnosis in a unified frame-work. In this framework, the decision boundary is updated incrementally only based on the specific original support vectors and newly collected samples. An adaptive threshold and a restructured radius are proposed to promote accuracy in the fault detection. In the classification procedure, the hyperspheres for all known classes are constructed by decision tree. The new sample that does not belong to any known class is identified as an unknown class. Without a time-consuming retraining process, the proposed diagnosis method with the incremental learning capability can synchronously achieve the fault detection and classification task. Experimental results demonstrate the effectiveness and superiority in terms of diagnosis performance. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In practical industrial processes, new fault samples are collected and new fault classes emerge continually. Under such scenarios, fault diagnosis with incremental learning capability is becoming increasingly important. This work proposes a unified incremental framework for fault diagnosis based on support vector data description, which is able to achieve fault detection and classification synchronously. For fault detection, an adaptive threshold and a restructured radius are developed. For fault classification, hypersphere-shaped boundaries of all fault classes are given via the decision tree-based strategy. The proposed method is updated to include new fault samples and fault classes without dimensionality reduction or any distributional assumptions.

Bypass diode faults are among the most hard-to-detect but impactful anomalies in photovoltaic (PV) systems, especially under partial shading conditions, where their electrical signatures often resemble those caused by non-critical irradiance variations. This study presents a systematic simulation-based investigation into how different bypass diode fault types—short-circuited, open-circuited, and healthy—affect the electrical behavior of PV strings under diverse irradiance profiles. A high-resolution MATLAB/Simulink model is developed to simulate 27 unique diode fault configurations across multiple shading scenarios, enabling the extraction of key features from resulting I–V curves. These features include global and local maximum power point parameters, open-circuit voltage, and short-circuit current. To address the challenge of feature redundancy and classification ambiguity, a preprocessing step is applied to remove near-duplicate instances and improve model generalization. An artificial neural network (ANN) model is then trained to classify the number of faulty bypass diodes based on these features. Comparative evaluations are conducted with support vector machines and random forests. The results indicate that the ANN achieves the highest test accuracy (93.57%) and average AUC (0.9925), outperforming other classifiers in both robustness and discriminative power. These findings highlight the importance of feature-informed, data-driven approaches for fault detection in PV systems and demonstrate the feasibility of diode fault classification without precise fault localization.

Photovoltaic (PV) systems have recieved a lot of attention in recent decades due to their accessibility and advancements in PV technology. The protection of PV systems from faults such as String to String (SS), String to Ground (SG), Open circuit (OC), and partial shading are the key challenges to the realization of cost-effective and environmentally friendly PV systems. Such unusual circumstances reduce the maximum available PV power. Partial shading and breakdowns in a PV array must therefore be noticed quickly for enhanced system efficiency and reliability. The significant fault current in PV systems can be detected using the existing safety devices in PV systems, such as fuses and residual current detectors. The flowing fault current being of low order is not significant enough for current protection devices to detect if the solar and/or fault mismatch is modest and the fault resistance is high. As a result, under cloudy and low irradiance conditions, the traditional protection devices fail to identify problems, resulting in reliability concerns and photovoltaic fire threats. In this context, a fault diagnosis scheme for PV systems is presented in this paper, which includes feature extraction using the Discrete wavelet transform, and classification of various defects on the PV system using Decision tree.