变压器早期故障诊断与状态评估专家系统

H. Malik, T. Tarkeshwar, R. Jarial
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引用次数: 30

摘要

充油变压器产生的气体可用于早期故障的判定。变压器油溶解气体分析(DGA)已成为最常用的故障检测方法之一。各种方法,如液相色谱法、声学分析和变压器函数技术都需要一些经验来解释观察结果。研究人员使用人工智能(AI)方法对这些诊断技术进行编码。本文介绍了模糊逻辑在变压器多故障诊断中的应用,并对人工神经网络技术进行了综述。本文给出了理论和实践中的模糊逻辑信息模型以及各种研究者基于人工神经网络的实验结论。本文介绍了FL技术在变压器早期故障诊断中的应用。
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An Expert System for Incipient Fault Diagnosis and Condition Assessment in Transformers
The gases generated in oil filled transformer can be used for determination of incipient faults. Dissolved gas analysis (DGA) of transformer oil has been one of the most power full methods to detect the faults. The various methods such as liquid chromatography, acoustic analysis, and transformer function techniques are require some experience to interpret observations. The researchers have used artificial intelligence (AI) approach to encode these diagnostic techniques. This paper presents fuzzy-logic application and an overview of ANN techniques which can diagnose multiple faults in a transformer. Theoretical and practical fuzzy-logic (FL) information model and various researchers' ANN based experimental conclusion have been presented. This paper includes a demonstration of the application of the FL technique for transformer incipient fault diagnosis.
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