Cascaded fuzzy logic based arc fault detection in photovoltaic applications

Grichting, Benjamin; Goette, Josef; Jacomet, Marcel

doi:10.1109/iccep.2015.7177620

Cited by 21 publications

(9 citation statements)

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“…According to (16) and combined with the results of a large number of experiments, it is found that the training effect is best when the implied layer node points take 14. In addition, the studies [83] and [84] In [85] and [86], the authors used fuzzy logic system to detect the arc fault in the PV array. The accuracy of this method is increased up to 98.8%.…”

Section: ) Artificial Intelligence Methodsmentioning

confidence: 99%

“…At present, numerous methods could detect the arc fault of PV systems: physical analysis (clustering method) [55][56][57][58], Fast Fourier Transform (frequency domain analysis) [59][60][61][62][63], time domain analysis [64][65][66][67], wavelet detection (multi-resolution analysis) [68][69][70][71][72][73][74][75][76][77], and Artificial Intelligence method (neural networks, support vector machines, fuzzy logic systems, etc.) [78][79][80][81][82][83][84][85][86].…”

Section: B Fault Diagnosismentioning

confidence: 99%

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A Review for Solar Panel Fire Accident Prevention in Large-Scale PV Applications

Wen

et al. 2020

IEEE Access

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Due to the wide applications of solar photovoltaic (PV) technology, safe operation and maintenance of the installed solar panels become more critical as there are potential menaces such as hot spot effects and DC arcs, which may cause fire accidents to the solar panels. In order to minimize the risks of fire accidents in large scale applications of solar panels, this review focuses on the latest techniques for reducing hot spot effects and DC arcs. The risk mitigation solutions mainly focus on two aspects: structure reconfiguration and faulty diagnosis algorithm. The first is to reduce the hot spot effect by adjusting the space between two PV modules in a PV array or relocate some PV modules. The second is to detect the DC arc fault before it causes fire. There are three types of arc detection techniques, including physical analysis, neural network analysis, and wavelet detection analysis. Through these detection methods, the faulty PV cells can be found in a timely manner thereby reducing the risk of PV fire. Based on the review, some precautions to prevent solar panel related fire accidents in large-scale solar PV plants that are located adjacent to residential and commercial areas.

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Section: ) Artificial Intelligence Methodsmentioning

confidence: 99%

Section: B Fault Diagnosismentioning

confidence: 99%

A Review for Solar Panel Fire Accident Prevention in Large-Scale PV Applications

Wen

et al. 2020

IEEE Access

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“…With the advancement of information technology, artificial intelligence (AI) methods have become popular and offer potential techniques in fault diagnosis in various areas such as high impedance fault detection in medium voltage networks [22], failure detection in electrical machines [23], and track circuit fault detection in railway systems [24]. Several recent studies have achieved promising results for DC series arc fault detection with AI-based methods, such as the combined use of a support vector machine (SVM) and wavelet packet decomposition for series arc fault detection [25], the use of a hidden Markov model (HMM) for obtaining the maximum likelihood of series arc faults for correctly detecting faults [26], and the use of a cascaded fuzzy logic system in a photovoltaic system for series arc fault detection [27]. Numerous features such as current variations and high-frequency energy are extracted, trained for series arc detection based on weighted least squares SVM algorithms [28].…”

Section: Introductionmentioning

confidence: 99%

Series DC Arc Fault Detection Using Machine Learning Algorithms

Dang

Kim

Kwak³

et al. 2021

IEEE Access

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The wide variety of arc faults induced by different load types renders residential series arc fault detection complicated and challenging. Series dc arc faults could cause fire accidents and adversely affect power systems if not promptly detected. However, in practical power systems, they are difficult to detect because of a low arc current, absence of a zero-crossing period, and various abnormal behavior based on different types of power loads and controllers. In particular, conventional protection fuses may not be activated when they occur. Undetected arc faults could cause false operation of power systems and potentially lead to damage to property and human casualties. Therefore, it is imperative to develop a detection system for series arc faults in DC systems for the reliable and efficient operation of such systems. In this study, several typical loads, especially nonlinear and complex loads such as power electronic loads, were chosen and analyzed, and five time-domain parameters of the current-average value, median value, variance value, RMS value, and distance of the maximum and minimum values-were chosen for arc fault detection. Various machine learning algorithms were used for arc fault detection and their detection accuracies were compared.

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“…In order to reconcile simplicity and efficiency, the strategy adopted in this work relies on an adaptive thresholding logic. The main part of the decision making is constituted by a Fuzzy logic processor largely used in recent years in fault diagnosis in photovoltaic and robotic systems [30][31][32][33] .…”

Section: Introductionmentioning

confidence: 99%

Kalman filter and a fuzzy logic processor for series arcing fault detection in a home electrical network

Calderon-Mendoza

Schweitzer

Weber

2019

International Journal of Electrical Power & Energy Systems

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This paper presents a method for detecting series arcing faults in AC home electrical networks. The proposed algorithm is based on both a Kalman filter, used for identifying fault symptoms and a decision block, which confirms the presence of a series arc fault to activate a tripping signal. The current measured at one end of the power line is estimated using a model of two steady-state variables (X1 and X2). Firstly, residuals and the third order difference of state X2 are used as input parameters of a Fuzzy logic processor for detecting fault symptoms. Secondly, the fault symptoms are processed by a detection logic block, which confirms the presence of an electrical arcing fault. The algorithm is tested on a variety of loads in single or masking load configurations chosen accordingly to the requirements of the UL 1699 and IEC 62606 standards. The algorithm is also tested in the steady state or at load start (transient state). This method's performance is studied and discussed in the final part. Experimental results show that the method we propose can detect arcing faults efficiently, avoiding false tripping, whilst taking into account a high degree of diagnosis accuracy and average detection time.

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Cascaded fuzzy logic based arc fault detection in photovoltaic applications

Cited by 21 publications

References 1 publication

A Review for Solar Panel Fire Accident Prevention in Large-Scale PV Applications

A Review for Solar Panel Fire Accident Prevention in Large-Scale PV Applications

Series DC Arc Fault Detection Using Machine Learning Algorithms

Kalman filter and a fuzzy logic processor for series arcing fault detection in a home electrical network

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