Power Transformer Fault Diagnosis Using Fuzzy Reasoning Spiking Neural P Systems

Y. Yahya, Ai Qian, Adel Yahya
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引用次数: 10

Abstract

This paper presents an intelligent technique to fault diagnosis of power transformers dissolved and free gas analysis (DGA). Fuzzy Reasoning Spiking neural P systems (FRSN P systems) as a membrane computing with distributed parallel computing model is powerful and suitable graphical approach model in fuzzy diagnosis knowledge. In a sense this feature is required for establishing the power transformers faults identifications and capturing knowledge implicitly during the learning stage, using linguistic variables, membership functions with “low”, “medium”, and “high” descriptions for each gas signature, and inference rule base. Membership functions are used to translate judgments into numerical expression by fuzzy numbers. The performance method is analyzed in terms for four gas ratio (IEC 60599) signature as input data of FRSN P systems. Test case results evaluate that the proposals method for power transformer fault diagnosis can significantly improve the diagnosis accuracy power transformer.
基于模糊推理脉冲神经P系统的电力变压器故障诊断
提出了一种电力变压器溶解与游离气体分析(DGA)智能故障诊断技术。模糊推理脉冲神经P系统(FRSN P系统)作为一种膜计算分布式并行计算模型,是一种功能强大、适用于模糊诊断知识的图形化方法模型。从某种意义上说,这一特征是在学习阶段建立电力变压器故障识别和隐式捕获知识所必需的,使用语言变量、每个气体特征的“低”、“中”和“高”描述的隶属函数以及推理规则库。利用隶属函数将判断转化为模糊数的数值表达。分析了四气比(IEC 60599)信号作为FRSN - P系统输入数据的性能方法。用例结果表明,本文提出的电力变压器故障诊断方法能显著提高电力变压器故障诊断的准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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