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首页> 外文期刊>International Journal of Innovative Computing Information and Control >NEURAL NETWORK BASED REAL TIME DETECTION OF TEMPORARY SHORT CIRCUIT FAULT ON INDUCTION MOTOR WINDING THROUGH WAVELET TRANSFORMATION
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NEURAL NETWORK BASED REAL TIME DETECTION OF TEMPORARY SHORT CIRCUIT FAULT ON INDUCTION MOTOR WINDING THROUGH WAVELET TRANSFORMATION

机译：基于小波变换的感应电动机绕组临时短路故障的神经网络实时检测。

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摘要

In this paper, a new detection system for early stage short circuit fault in stator winding of induction motor is proposed. The early stage of stator winding short circuit is represented by a low magnitude current and a very short duration that is defined as temporary short circuit. The proposed method is based on transient current recognizing when short circuit fault starting occur and cleared. The transient current during fault is recognized by high frequency signal energy trending of wavelet transform. Three energy of high frequency signal from three consecutive current signal sampling are used as detection variables. Three wavelet types and five levels transformation are evaluated using linear discriminant analysis (LDA) to get the most suitable wavelet transform. The El-man neural network is designed as detection system. The proposed method is applied to laboratory experiment. As a result, the proposed method can clearly detect the temporary short circuit fault even though the fault has very fast occurrence and the current magnitude is lower than full load current, with the good accuracy and the ability to provide time information of fault, the proposed method is suitable for monitoring system.
机译：本文提出了一种新型的感应电动机定子绕组早期短路故障检测系统。定子绕组短路的早期阶段表现为低幅值电流和非常短的持续时间，这被定义为暂时短路。所提出的方法是基于短路电流启动发生和清除时的瞬态电流识别。小波变换的高频信号能量趋势可识别故障期间的瞬态电流。来自三个连续电流信号采样的三个能量的高频信号用作检测变量。使用线性判别分析（LDA）对三种小波类型和五级变换进行了评估，以获得最合适的小波变换。 El-man神经网络被设计为检测系统。该方法应用于实验室实验。结果，所提出的方法即使故障发生得非常快并且电流幅度低于满载电流也可以清楚地检测到临时短路故障，具有良好的准确性和提供故障时间信息的能力，该方法适用于监控系统。

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