A multi-scale dual-stage model for PV array fault detection, classification, and monitoring technique

The output generated by photovoltaic arrays is influenced mainly by the irradiance, which has non-uniform distribution in a day. This has resulted in the current-limiting nature and nonlinear output characteristics, and conventional protection devices cannot detect and clean faults appropriately. This paper proposes a low-cost model for a multi-scale dual-stage photovoltaic fault detection, classification, and monitoring technique developed through MATLAB/Simulink. The main contribution of this paper is that it can detect multiple common faults, be applied on multi-scale photovoltaic arrays regardless of environmental conditions, and be beneficial for photovoltaic system maintenance work. The experimental results show that the developed algorithm using supervised learning algorithms mutual with k-fold cross-validation has produced good performances in identifying six common faults of photovoltaic arrays, achieved 100% accuracy in fault detection, and achieved good accuracy in fault classification. Challenges and suggestions for future research direction are also suggested in this paper. Overall, this study shall provide researchers and policymakers with a valuable reference for developing photovoltaic system fault detection and monitoring techniques for better feasibility, safety, and energy sustainability

Novel Open-Circuit Photovoltaic Bypass Diode Fault Detection Algorithm

In this article, a novel photovoltaic (PV) bypass diode fault detection algorithm is presented. The algorithm consists of three main steps. First, the threshold voltage of the current–voltage (I–V) curve is obtained using different failure bypass diode scenarios. Second, the theoretical prediction for the faulty regions of bypass diodes is calculated using the analysis of voltage drop in the I–V curve as well as the voltage at maximum power point. Finally, the actual I–V curve under any environmental condition is measured and compared with theoretical predictions. The proposed algorithm has been experimentally evaluated using a PV string that comprises three series-connected PV modules, and subtotal of nine bypass diodes. Various experiments have been conducted under diverse bypass diodes failure conditions. The achieved detection accuracy is always greater than 99.39% and 99.74% under slow and fast solar irradiance transition, respectively

A comprehensive review and performance evaluation in solar (PV) systems fault classification and fault detection techniques

The renewable energy industry is growing faster than ever before and in particular solar systems have significantly expanded. Abnormal conditions lead to a reduction in the maximum available power from solar (photovoltaic) systems. Thus, it is necessary to identification, detection, and monitoring of various faults in the PV system that they are the key factors to increase the efficiency, reliability, and lifetime of these systems. Up to now, faults on PV components and systems have been identified; some of them have physical damage on PV systems and some of them are electrical faults that occur on the DC side or AC side of the PV system. Here, the faults will be divided into groups based on their location of occurrence. This paper provides a comprehensive review of almost all PV system faults and fault detection techniques of PV system proposed in recent literature

Fault Location in Grid Connected Ungrounded PV Systems Using Wavelets

Solar photovoltaic (PV) power has become one of the major sources of renewable energy worldwide. This thesis develops a wavelet-based fault location method for ungrounded PV farms based on pattern recognition of the high frequency transients due to switching frequencies in the system and which does not need any separate devices for fault location. The solar PV farm used for the simulation studies consists of a large number of PV modules connected to grid-connected inverters through ungrounded DC cables. Manufacturers report that about 1% of installed PV panels fail annually. Detecting phase to ground faults in ungrounded underground DC cables is also difficult and time consuming. Therefore, identifying ground faults is a significant problem in ungrounded PV systems because such earth faults do not provide sufficient fault currents for their detection and location during system operation. If such ground faults are not cleared quickly, a subsequent ground fault on the healthy phase will create a complete short-circuit in the system, which will cause a fire hazard and arc-flashing. Locating such faults with commonly used fault locators requires costly external high frequency signal generators, transducers, relays, and communication devices as well as generally longer lead times to find the fault. This thesis work proposes a novel fault location scheme that overcomes the shortcomings of the currently available methods. In this research, high frequency noise patterns are used to identify the fault location in an ungrounded PV farm. This high frequency noise is generated due to the switching transients of converters combined with parasitic capacitance of PV panels and cables. The pattern recognition approach, using discrete wavelet transform (DWT) multi-resolution analysis (MRA) and artificial neural networks (ANN), is utilized to investigate the proposed method for ungrounded grid integrated PV systems. Detailed time domain electromagnetic simulations of PV systems are done in a real-time environment and the results are analyzed to verify the performance of the fault locator. The fault locator uses a wavelet transform-based digital signal processing technique, which uses the high frequency patterns of the mid-point voltage signal of the converters to analyze the ground fault location. The Daubechies 10 (db10) wavelet and scale 11 are chosen as the appropriate mother wavelet function and decomposition level according to the characteristics of the noise waveform to give the proposed method better performance. In this study, norm values of the measured waveform at different frequency bands give unique features at different fault locations and are used as the feature vectors for pattern recognition. Then, the three-layer feed-forward ANN classifier, which can automatically classify the fault locations according to the extracted features, is investigated. The neural network is trained with the Levenberg-Marquardt back-propagation learning algorithm. The proposed fault locating scheme is tested and verified for different types of faults, such as ground and line-line faults at PV modules and cables of the ungrounded PV system. These faults are simulated in a real-time environment with a digital simulator and the data is then analyzed with wavelets in MATLAB. The test results show that the proposed method achieves 99.177% and 97.851% of fault location accuracy for different faults in DC cables and PV modules, respectively. Finally, the effectiveness and feasibility of the designed fault locator in real field applications is tested under varying fault impedance, power outputs, temperature, PV parasitic elements, and switching frequencies of the converters. The results demonstrate the proposed approach has very accurate and robust performance even with noisy measurements and changes in operating conditions

Artificial intelligence techniques for ground fault line selection in power systems: State-of-the-art and research challenges

In modern power systems, efficient ground fault line selection is crucial for maintaining stability and reliability within distribution networks, especially given the increasing demand for energy and integration of renewable energy sources. This systematic review aims to examine various artificial intelligence (AI) techniques employed in ground fault line selection, encompassing artificial neural networks, support vector machines, decision trees, fuzzy logic, genetic algorithms, and other emerging methods. This review separately discusses the application, strengths, limitations, and successful case studies of each technique, providing valuable insights for researchers and professionals in the field. Furthermore, this review investigates challenges faced by current AI approaches, such as data collection, algorithm performance, and real-time requirements. Lastly, the review highlights future trends and potential avenues for further research in the field, focusing on the promising potential of deep learning, big data analytics, and edge computing to further improve ground fault line selection in distribution networks, ultimately enhancing their overall efficiency, resilience, and adaptability to evolving demands

Industrial Applications: New Solutions for the New Era

This book reprints articles from the Special Issue "Industrial Applications: New Solutions for the New Age" published online in the open-access journal Machines (ISSN 2075-1702). This book consists of twelve published articles. This special edition belongs to the "Mechatronic and Intelligent Machines" section