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A neural network-based scheme for fault diagnosis of power transformers

机译：基于神经网络的电力变压器故障诊断方案

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摘要

This research presents an artificial neural network (ANN)-based scheme for fault diagnosis of power transformers. The scheme is designed to detect the fault, estimate the faulted side, classify the fault type and identify the faulted phase. The proposed fault diagnosis scheme (FDS) consists of three hierarchical levels. In the first level, a pre-processing of input data is performed. In the second level, there is an ANN which is designed to detect the fault and determine the faulted side if any. In the third level, there are two sides diagnosis systems. Each system is dedicated to one side and consists of one ANN in series with four paralleled ANNs (for fault type classification). The proposed FDS is trained and tested using local measurements of three-phase primary voltage and primary and secondary currents. These samples are generated using EMTP simulation of the High Dam 15.75/500 kV transformer substation in Upper Egypt. All the possible fault types were simulated. The fault locations and fault incipience time were varied within each fault type. Testing results proved that the performance of the proposed ANN-based FDS is satisfactory.
机译：这项研究提出了一种基于人工神经网络（ANN）的电力变压器故障诊断方案。该方案旨在检测故障，估计故障侧，对故障类型进行分类并确定故障相位。提出的故障诊断方案（FDS）包含三个层次级别。在第一级中，执行输入数据的预处理。在第二级中，有一个ANN，其设计用于检测故障并确定有故障的一侧（如果有）。在第三级，有两个侧面诊断系统。每个系统专用于一侧，由一个串联的ANN和四个并行的ANN（用于故障类型分类）组成。建议的FDS使用三相初级电压以及初级和次级电流的本地测量值进行了培训和测试。这些样本是使用上埃及高水坝15.75 / 500 kV变电站的EMTP模拟生成的。模拟了所有可能的故障类型。在每种故障类型中，故障位置和故障开始时间都不同。测试结果证明，所提出的基于人工神经网络的FDS的性能令人满意。

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