Transformer Winding Condition Assessment Using Feedforward Artificial Neural Network and Frequency Response Measurements
Transformer Winding Condition Assessment Using Feedforward Artificial Neural Network and Frequency Response Measurements
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DOI：
10.3390/en14113227
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发表时间：
2021-05
期刊：
影响因子：
3.2
通讯作者：
M. Tahir;S. Tenbohlen
中科院分区：
文献类型：
--
作者：
M. Tahir;S. Tenbohlen
频率响应分析（FRA）是评估电力Transformer的有源部件的机械完整性的公知方法。FRA的测量程序如IEEE和IEC标准中所述被标准化。然而，FRA结果的解释远未达到公认和确定的方法，因为标准中没有可靠的代码。作为对这一必要性的贡献，本文提出了一种智能故障检测和分类算法，使用FRA的结果。该算法是基于一个多层，前馈，反向传播人工神经网络（ANN）。首先，开发了自适应分频算法，并使用各种数值指标来量化FRA迹线之间的差异并获得用于ANN的特征集。最后，开发了ANN的分类模型来检测和分类不同的Transformer状态，即，正常绕组、具有饱和铁芯的正常绕组、机械变形、电气故障以及由于不同测试条件而导致的再现性问题。在这项研究中使用的数据库由80个不同设计，额定值和不同制造商的电力变压器的FRA测量。实验结果证明了所提出的分类模型在基于FRA的电力Transformer故障诊断中的有效性。
Frequency response analysis (FRA) is a well-known method to assess the mechanical integrity of the active parts of the power transformer. The measurement procedures of FRA are standardized as described in the IEEE and IEC standards. However, the interpretation of FRA results is far from reaching an accepted and definitive methodology as there is no reliable code available in the standard. As a contribution to this necessity, this paper presents an intelligent fault detection and classification algorithm using FRA results. The algorithm is based on a multilayer, feedforward, backpropagation artificial neural network (ANN). First, the adaptive frequency division algorithm is developed and various numerical indicators are used to quantify the differences between FRA traces and obtain feature sets for ANN. Finally, the classification model of ANN is developed to detect and classify different transformer conditions, i.e., healthy windings, healthy windings with saturated core, mechanical deformations, electrical faults, and reproducibility issues due to different test conditions. The database used in this study consists of FRA measurements from 80 power transformers of different designs, ratings, and different manufacturers. The results obtained give evidence of the effectiveness of the proposed classification model for power transformer fault diagnosis using FRA.