Hybrid methodology based on extension theory for partial discharge fault diagnosis of power capacitors

Abstract

Power capacitors are widely used in power systems, and any internal capacitor failures will affect the safe operations of the systems. The most common failures include humidity, partial discharge, aging, or insulating material degradation and structural damage. The purpose of this study is to detect the types of power capacitor failures by using a humanmachine interface diagnostic system in order to determine the real-time status of the power capacitors. Partial discharge data measurement and diagnostic analysis were mainly conducted on power capacitors operating at a low voltage for a long time. Defects were pre-processed before capacitance measurement, and then, an electric testing machine was used to conduct a partial discharge test for capacitor enclosures and to continuously apply voltage until the occurrence of a partial discharge. A high frequency oscillograph was used to capture the voltage and partial discharge signals. Subsequently, the empirical mode decomposition (EMD) was applied to identify the characteristics of the discharge signals and to build the chaos and error scatter map by combining the chaotic synchronization detection and analysis method. Moreover, eyes of chaos were taken as the characteristics of fault diagnosis, and an extension algorithm was used to identify capacitance failures. The advantages of this method are to reduce the characteristics' captured data and to effectively detect the minimum movement of the voltage signal discharged from power capacitors, so that the operating states of the power capacitors can be detected and determined, in order to carry out emergency measures in advance and prevent major disasters. After verification through actual measurement, the proposed method has a detection rate of 84% based on the extension theory, which proves that the method used in this study is applicable to partial discharge detection of power capacitors.

A novel fault diagnosis method for PV arrays using convolutional extension neural network with symmetrized dot pattern analysis

“…Step 2: Calculate the weight medians Z k and Z k j of each feature, expressed as Equations ( 10) and (11). 11) where k = 1, 2, … , N c…”

“…These are commonly adopted in PV array fault diagnosis. Some artificial intelligence (AI) methods have been proposed to diagnose PV array faults or power‐related fields in the literature, including clockwork recurrent neural network (CW‐RNN) [6], improved adaptive genetic algorithm (GA) [7], K‐nearest neighbours (KNN) [8], reduced kernel random forest method [9], extension neural network (ENN) [10, 11], and CNN‐related research [12–14].…”

“…The weight connecting the jth input neuron to kth output neuron includes maximum weight W k j and minimum weight U k j . The central value of weight Z k j can be obtained by Equation(11). O ik is the fault type k of the ith data.…”

Evaluación de la fuga de corriente en capacitores electrolíticos mediante dos procesos de envejecido

“…La mayoría de las investigaciones, se han enfocado en evaluar el rendimiento de los capacitores en condiciones extremas de temperatura y humedad, que incluían configuraciones de voltajes y corrientes de trabajo exigentes. Los resultados obtenidos han permitido mejorar los procesos productivos con un impacto positivo en las propiedades (físicas, térmicas, eléctricas y mecánicas) de los CET (Wang, 2020;Zou, 2012;Trainer, 2001;Chaneliere, 1998).…”

Fault diagnosis of power capacitors using a convolutional neural network combined with the chaotic synchronisation method and the empirical mode decomposition method

“…Thus, using the model could help to predict the state of a power capacitor that is reaching the end of its service life. Wang et al [8] proposed the extension theory for fault diagnosis of normal and aging 220 V low-voltage capacitors and to detect feature signals with chaotic eye coordinates, which can enabled 84% accuracy of the recognition results. However, when the extension theory is used to build the fault detection model of capacitors, the ranges of the classical domain and node domain of the matter-element model of the capacitors must be set in advance based on experts' experience, which increases the complexity of the algorithm during pre-work.…”