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An Analytical Hierarchy Process Based Approach for Effective Maintenance Prioritization of Power Transformers

机译:基于层次分析法的电力变压器有效维护优先级排序方法

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Transformer diagnosis and condition monitoring, literally a big deal for the utility professionals and is still posing a challenge to the researchers and domain experts of this area. It is clear that the accuracy of the diagnosis or maintenance process will more or less depend on the accuracy of the competence professional. Considering this as a constraint, lot of work has been done by eminent researchers using various artificial intelligence techniques like artificial neural networks, fuzzy logic and various algorithms for uplifting the accuracy of the diagnosis. Thus, a step ahead in this work, maintenance prioritization of power transformer has been carried out by using a Multiple Attribute Decision Making (MADM) technique, i.e., Analytical Hierarchy Process (AHP). Seven power transformers for which maintenance scheduling is to be done were considered as alternatives, and the results of various diagnostic tests such as furan analysis (2-FAL), total dissolved combustible gases (TDCG), Breakdown voltage (BDV), Dissipation Factor (DF), Acidity and Moisture content were considered as attributes for the corresponding transformer or alternative. Step-by-step calculations were done, and an optimal scheduling for these seven transformers was recommended considering the vulnerability state of insulation oil and criticality of the transformer.
机译:变压器诊断和状态监控对公用事业专业人士而言确实是一笔不小的数目,但仍然对这一领域的研究人员和领域专家构成挑战。显然,诊断或维护过程的准确性或多或少取决于专业人员的准确性。考虑到这是一个制约因素,杰出的研究人员已使用各种人工智能技术(如人工神经网络,模糊逻辑和各种算法来提高诊断的准确性)进行了大量工作。因此,通过使用多属性决策(MADM)技术(即,层次分析法(AHP)),对电力变压器的维护进行了优先排序,这是这项工作的第一步。七台需要进行维护计划的电力变压器被视为替代品,各种诊断测试的结果,例如呋喃分析(2-FAL),总可燃气体(TDCG),击穿电压(BDV),耗散因数( DF),酸度和水分含量被认为是相应变压器或替代品的属性。进行了逐步计算,并考虑到绝缘油的易损性状态和变压器的关键性,建议对这七个变压器进行最佳调度。

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