Fault Detection In Photovoltaic Systems Research Articles

Fault detection in photovoltaic systems using IoT

Fault detection in photovoltaic (PV) systems using the Internet of Things (IoT) allows monitoring variables that may be of interest to users who do not have technical knowledge and wish to measure: current, voltage, temperature, power generated, and money saved by the energy generated. This study aims to present a methodology for implementing a low-cost Internet of Things (IoT) to an FS in order to identify recurring faults using the Exponentially Weighted Moving Average (EWMA) statistical technique. The system was applied to a 3500 W PV located at the Universidad Veracruzana Campus Cotzacoalcos.

Model-based fault detection in photovoltaic systems: A comprehensive review and avenues for enhancement

Solar photovoltaic (PV) systems have become a vital renewable energy source, witnessing rapid global demand. Nevertheless, these systems are susceptible to faults and anomalies that can deteriorate performance and yield significant consequences. Hence, this paper is dedicated to reviewing recent advancements in monitoring, modeling, and fault detection methods for PV systems. It encompasses diverse PV system types, including grid-connected, stand-alone, and hybrid configurations, and delves into the latest data acquisition and monitoring techniques. The review also discusses various performance modeling approaches, including empirical, analytical, and numerical models, highlighting the significance of model validation and calibration. Furthermore, it provides a comprehensive analysis of model-based fault detection techniques. Overall, this paper underscores the pivotal role of fault detection in PV systems and offers a thorough comprehension of available techniques vital for enhancing system management and maintenance.

Enhancing Fault Detection and Diagnosis in Grid-Connected PV Systems under Irradiance Variations: A Novel Approach Using New Data Normalization Techniques on RBFNN Algorithm

With the global shift towards renewable energy, the reliability and efficiency of photovoltaic (PV) systems have become paramount. A crucial aspect of this is the timely detection and accurate classification of faults within the system. In fact, accurate fault detection and classification in the diagnostic systems can prevent severe damages, enhance operational efficiency, and prolong the lifespan of the installations. This paper introduces a novel data normalization technique tailored for enhancing the performance of meta-heuristic algorithms (namely RBFNN) applied to single-phase inverter current signals for fault detection in PV systems. While conventional normalization technics have shown their limitations in capturing the nuances of PV system faults, our proposed strategy demonstrates a more effective approach in preserving fault-specific features in the data. The proposed method is evaluated using multiple datasets encompassing a variety of fault scenarios. Empirical results show a significant improvement in both fault detection rates and classification accuracy. This study not only presents a breakthrough in PV system fault diagnosis but also sheds light on the broader potential of specialized normalization techniques in all signal-processing field, such as biomedical signal processing, industrial process control, or financial time series analysis, and other domains. The supremacy of the proposed diagnostic in reducing false alarms and providing more clear-cut classifications is highlighted by several simulations under Matlab-Simulink.

Artificial Intelligence in Photovoltaic Fault Identification and Diagnosis: A Systematic Review

Photovoltaic (PV) fault detection is crucial because undetected PV faults can lead to significant energy losses, with some cases experiencing losses of up to 10%. The efficiency of PV systems depends upon the reliable detection and diagnosis of faults. The integration of Artificial Intelligence (AI) techniques has been a growing trend in addressing these issues. The goal of this systematic review is to offer a comprehensive overview of the recent advancements in AI-based methodologies for PV fault detection, consolidating the key findings from 31 research papers. An initial pool of 142 papers were identified, from which 31 were selected for in-depth review following the PRISMA guidelines. The title, objective, methods, and findings of each paper were analyzed, with a focus on machine learning (ML) and deep learning (DL) approaches. ML and DL are particularly suitable for PV fault detection because of their capacity to process and analyze large amounts of data to identify complex patterns and anomalies. This study identified several AI techniques used for fault detection in PV systems, ranging from classical ML methods like k-nearest neighbor (KNN) and random forest to more advanced deep learning models such as Convolutional Neural Networks (CNNs). Quantum circuits and infrared imagery were also explored as potential solutions. The analysis found that DL models, in general, outperformed traditional ML models in accuracy and efficiency. This study shows that AI methodologies have evolved and been increasingly applied in PV fault detection. The integration of AI in PV fault detection offers high accuracy and effectiveness. After reviewing these studies, we proposed an Artificial Neural Network (ANN)-based method for PV fault detection and classification.

Fault diagnosis of photovoltaic systems using artificial intelligence: A bibliometric approach

Conventional fault detection methods in photovoltaic systems face limitations when dealing with emerging monitoring systems that produce vast amounts of high-dimensional data across various domains. Accordingly, great interest appears within the international scientific community for the application of artificial intelligence methods, which are seen as a highly promising solution for effectively managing large datasets for detecting faults. In this review, more than 620 papers published since 2010 on artificial intelligence methods for detecting faults in photovoltaic systems are analyzed. To extract major research trends, in particular to detect most promising algorithms and approaches overcoming excessive time calculations, a conventional bibliographic survey would have been extremely difficult to complete. That is why this study proposes to carry out a review with an innovative approach based on a statistical method named Bibliometric and a Expert qualitative content analysis. This methodology consists of three stages. First, a collection of data from databases is carried out with all precautions to achieve a large, robust, high-quality database. Second, multiple bibliometric indicators are chosen based on the objectives to be achieved and analyzed to assess their real impact, such as the quantity and nature of publications, collaborative connections among organizations, researchers, and countries or most cited articles. Finally, the Expert qualitative content analysis carried out by experts identifies the current and emerging research topics that have the greatest impact on fault detection in photovoltaic systems using artificial intelligence.

Fault Detection in Photovoltaic Systems Using Optimized Neural Network

Fault Detection in Photovoltaic Systems Using Optimized Neural Network

Development of a machine-learning-based method for early fault detection in photovoltaic systems

In the process of the decarbonization of energy production, the use of photovoltaic systems (PVS) is an increasing trend. In order to optimize the power generation, the fault detection and identification in PVS is significant. The purpose of this work is the study and implementation of such an algorithm, for the detection as many as faults arising on the DC side of a photovoltaic system. A machine learning technique was chosen. The dataset used to train the algorithm was based on a year’s worth of irradiance and temperature data, as well as data from the PV cell used. The method uses logistic regression with cross validation as a new approach to detect and identify faults in PVS. It is applied to smart PV arrays, that can transmit voltage and current measurements from each PV cell of the array individually. The results are satisfactory since the algorithm can detect the majority of faults that occur on the DC side of a photovoltaic (open-circuit fault, short-circuit fault, mismatch faults). The accuracy of the algorithm (97.11%) is comparable to other methods presented by the literature. Moreover, the computational cost of the proposed method is significantly lower than the methods presented in the literature. In summary, the performance of the implemented algorithm is considered particularly satisfactory and can be easily applied to PVS.

Fault detection in photovoltaic systems using the inverse of the belonging individual Gaussian probability

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Health Monitoring and Fault Detection in Photovoltaic Systems in Central Greece Using Artificial Neural Networks

The operation and maintenance of a photovoltaic system is a challenging task that requires scientific soundness, and has significant economic impact. Faults in photovoltaic systems are a common phenomenon that demands fast diagnosis and repair. The effective and accurate diagnosis and categorization of faults is based on information received from the photovoltaic plant monitoring and energy management system. This paper presents the application of machine learning techniques in the processing of monitoring datasets of grid connected systems in order to diagnose faults. In particular, monitoring data from four photovoltaic parks located in Central Greece are analyzed. The existing data are divided for training and validation procedures. Different scenarios are examined first, in order to observe and quantify the behavior of artificial neural networks in already known faults. In this process, the faults are divided in three main categories. The system’s performance deviation against the prediction of the trained artificial neural network in each fault category is processed by health monitoring methodology in order to specify it quantitatively.

Application of Artificial Intelligence in PV Fault Detection

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

Ensemble Learning Techniques-Based Monitoring Charts for Fault Detection in Photovoltaic Systems

Over the past few years, there has been a significant increase in the interest in and adoption of solar energy all over the world. However, despite ongoing efforts to protect photovoltaic (PV) plants, they are continuously exposed to numerous anomalies. If not detected accurately and in a timely manner, anomalies in PV plants may degrade the desired performance and result in severe consequences. Hence, developing effective and flexible methods capable of early detection of anomalies in PV plants is essential for enhancing their management. This paper proposes flexible data-driven techniques to accurately detect anomalies in the DC side of the PV plants. Essentially, this approach amalgamates the desirable characteristics of ensemble learning approaches (i.e., the boosting (BS) and bagging (BG)) and the sensitivity of the Double Exponentially Weighted Moving Average (DEWMA) chart. Here, we employ ensemble learning techniques to exploit their capability to enhance the modeling accuracy and the sensitivity of the DEWMA monitoring chart to uncover potential anomalies. In the ensemble models, the values of parameters are selected with the assistance of the Bayesian optimization algorithm. Here, BS and BG are adopted to obtain residuals, which are then monitored by the DEWMA chart. Kernel density estimation is utilized to define the decision thresholds of the proposed ensemble learning-based charts. The proposed monitoring schemes are illustrated via actual measurements from a 9.54 kW PV plant. Results showed the superior detection performance of the BS and BG-based DEWMA charts with non-parametric threshold in uncovering different types of anomalies, including circuit breaker faults, inverter disconnections, and short-circuit faults. In addition, the performance of the proposed schemes is compared to that of BG and BS-based DEWMA and EWMA charts with parametric thresholds.

Development of a multi-output feed-forward neural network for fault detection in Photovoltaic Systems

The main role of a fault detection and identification technique is to interpret what is​ causing a PVS’s real-time energy production to drop. To keep photovoltaic (PV) systems performing at their best, fault detection is crucial. Various fault detection systems have been proposed in the literature over the last decade. Some of the fault detection methods presented solely used data from I–V curves, while others relied on ANN techniques to find faults. This paper presents the development of a multi-output Artificial Neural Network (ANN), for fault detection and identification on the DC side of a Photovoltaic System (PVS). Measurements and benchmarking were carried out in order to validate the algorithm’s capacity to detect both normal operation at maximum power and the ability to determine and identify faults introduced during the experiment’s procedure. It is observed that the classification output, compared with the regression output, provides a better assessment of the operational status of the photovoltaic cell. The developed method has a 93.4 percent accuracy in detecting open-circuit, short-circuit, and mismatch faults, using the classification output, while the regression output provides an operational status estimation with mean absolute error (MAE) equal to 0.305. When compared to other implementations, the findings are encouraging, indicating that the approach could be used in PVS.

Voting-Based Ensemble Learning Algorithm for Fault Detection in Photovoltaic Systems under Different Weather Conditions

A photovoltaic (PV) system is one of the renewable energy resources that can help in meeting the ever-increasing energy demand. However, installation of PV systems is prone to faults that can occur unpredictably and remain challenging to detect. Major PV faults that can occur are line-line and open circuits faults, and if they are not addressed appropriately and timely, they may lead to serious problems in the PV system. To solve this problem, this study proposes a voting-based ensemble learning algorithm with linear regression, decision tree, and support vector machine (EL-VLR-DT-SVM) for PV fault detection and diagnosis. The data acquisition is performed for different weather conditions to trigger the nonlinear nature of the PV system characteristics. The voltage-current characteristics are used as input data. The dataset is studied for a deeper understanding, and pre-processing before feeding it to the EL-VLR-DT-SVM. In the pre-processing step, data are normalized to obtain more feature space, making it easy for the proposed algorithm to discriminate between healthy and faulty conditions. To verify the proposed method, it is compared with other algorithms in terms of accuracy, precision, recall, and F-1 score. The results show that the proposed EL-VLR-DT-SVM algorithm outperforms the other algorithms.

Investigation of diode dynamic effect on fault detection of photovoltaic systems

Abstract This study proposes a new fault diagnosis algorithm for a photovoltaic (PV) solar system implemented on the bases of diode distribution and two classifiers based on ensemble bagged trees and neural pattern recognition techniques. The proposed approach is designed by adding two diodes connected to the upper and lower terminals of each PV string. The two diodes are connected to prevent the reverse direction of faulted string currents, thereby preventing high injecting fault currents. Given that the string fault current is prevented owing to diode interaction, this dynamic interaction provides new states during the fault periods considered to detect string faults. Owing to the distributed diodes, system voltage is not reduced during fault disturbances and voltage is not considered an input to the proposed detection techniques. Monitoring currents at the upper and lower ends of each string is required as input for the proposed approaches to detect string fault types. The proposed approaches are used to detect string fault types, such as cell-to-string negative terminal, cell-to-cell in the same string, and string-to-string faults. The proposed model is applied for 400 kW, and the PV system consists of 4 arrays interconnected with 1200 V AC grid built-in MATLAB/Simulink. Experimental results validate the newly suggested approaches.

A String-Current Behavior and Current Sensing-Based Technique for Line–Line Fault Detection in Photovoltaic Systems

The normal operation of a photovoltaic (PV) system is undermined by line-line (LL) faults. An LL fault, which establishes an unintentional current path between two points of different potentials, can cause malfunctions and even fire hazards to the PV systems. Although the overcurrent protection devices such as fuses are utilized to terminate the LL faults, the maximum power point tracking (MPPT) controller, partial shading, and blocking diodes are affecting the fault currents and they may be lower than the rated current of the fuse. Therefore, it is necessary to develop an effective LL fault detection technique for the PV systems. In this article, an LL fault detection technique based on the string-current behavior and current sensing is proposed. The characteristics of the LL faults and the string-current behavior under various fault conditions were analyzed to develop the algorithm. The proposed technique could determine the occurrence, type, location, and percentage of the LL faults. It was verified that the fault detection algorithm and current sensors in specific locations could effectively detect the LL faults despite the effects of MPPT, partial shading, and blocking diodes.

Collaborative Fault Detection for Large-Scale Photovoltaic Systems

Data-driven approaches have gained increasing interests in fault detection of photovoltaic systems due to the availability of sensor data. However, the noise introduced by environmental variations and measurement variabilities pose significant challenges on effective fault detection. Furthermore, the change in electrical signal magnitude of a faulty photovoltaic component is usually small, making it difficult to distinguish an anomaly from normal ones. As such, incipient faults are nearly undetectable when they cause less loss of electricity generation. This article proposes a collaborative fault detection solution based on collaborative filtering techniques. Specifically, the proposed solution first predicts photovoltaic strings’ current values according to similar strings using historical data. Faults are then detected by long-term differences between the predicted and actual values. A key advantage of the proposed solution is its ability to capture similarities among different photovoltaic strings under noisy and spatial-temporally variant conditions, which significantly enhances fault detection performance. The proposed solution has been deployed in two large-scale solar farms (39.36 MWp and 51.04 MWp). The results show that the proposed solution is superior to existing data-driven solutions in terms of efficiency, effectiveness, and robustness.

Series arc fault detection in photovoltaic system by small‐signal impedance and noise monitoring

Series arc fault detection in photovoltaic system by small‐signal impedance and noise monitoring

Machine learning-based statistical testing hypothesis for fault detection in photovoltaic systems

In this paper, we consider a machine learning approach merged with statistical testing hypothesis for enhanced fault detection performance in photovoltaic (PV) systems. The developed method makes use of a machine learning based Gaussian process regression (GPR) technique as a modeling framework, while a generalized likelihood ratio test (GLRT) chart is applied to detect PV system faults. The developed GPR-based GLRT approach is assessed using simulated and real PV data through monitoring the key PV system variables (current, voltage, and power). The computation time, missed detection rate (MDR), and false alarm rate (FAR) are computed to evaluate the fault detection performance of the proposed approach.

Series Arc Fault Detection in Photovoltaic Systems Based on Signal-to-Noise Ratio Characteristics Using Cross-Correlation Function

Series arc fault (SAF) is one of the most important causes for failure of photovoltaic (PV) systems. Conventional protections could not detect SAF at initial moments. In this paper, a new method is presented for detection of SAF. The method is based on extracting SAF signatures from high frequency contents of the normalized dc terminal voltage. Inverter switching signatures in the signal have a periodic nature, which are rejected by subtracting the resized data-windows based on the lag relevant to maximum cross-correlation value. The utilized criterion is defined as ratio of power of the low frequency components to power of the arc signal (or signal-to-noise ratio). A set of possible scenarios, whether in practice or simulation, is considered to evaluate the performance of the method. They include different arc lengths, different fault locations in the PV system, various partial shadings, different switching frequencies for the inverter, and different types of environmental noise.

Logistic Regression Based Arc Fault Detection in Photovoltaic Systems Under Different Conditions

This paper investigates direct current (DC) arc fault detection in photovoltaic system. In order to avoid the risk of fire ignition caused by the arc fault in the photovoltaic power supply, it is urgent to detect the DC arc fault in the photovoltaic system. Once an arc fault is detected, the power supply should be cut off immediately. A lot of field experiments are carried out to obtain the data of arc fault current of the photovoltaic system under different current conditions. Cable length, arc gap, and the effects of different sensors are tested. These three conditions are the most significant features of this paper. Four characteristic variables from both the time domain and the frequency domain are extracted to identify the arc fault. Then the logistic regression method in the field of artificial intelligence and machine learning is originally used to analyze the experimental results of arc fault in the photovoltaic system. The function between the probability of the arc fault and the change of the characteristic variables is obtained. After validating 80 groups of experimental data under different conditions, the accuracy rate of the arc fault detection by this algorithm is proved to reach 100%.