Abstract
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.
Highlights
Renewable energy has drawn attention owing to its advantages, such as green techniques and low carbon dioxide emission, and studies have been conducted on integrating them into existing power networks [1]–[4]
A series arc fault is generated by the disconnection of a conductor in transmission power lines, whereas a parallel arc fault results from the insulation breakdown between two or more parallel lines because of n external force or heat
In DC networks, most of the components are connected through electronic circuits or converters, and the electromagnetic distortion noise produced by electronic converters renders arc fault detection more challenging
Summary
Renewable energy has drawn attention owing to its advantages, such as green techniques and low carbon dioxide emission, and studies have been conducted on integrating them into existing power networks [1]–[4]. 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].
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