Improved power transformer condition monitoring under uncertainty through soft computing and probabilistic health index
Improved power transformer condition monitoring under uncertainty through soft computing and probabilistic health index
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DOI：
10.1016/j.asoc.2019.105530
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发表时间：
2019-06
期刊：
影响因子：
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通讯作者：
J. Aizpurua;B. Stewart;S. Mcarthur;B. Lambert;J. Cross;V. Catterson
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文献类型：
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作者：
J. Aizpurua;B. Stewart;S. Mcarthur;B. Lambert;J. Cross;V. Catterson
电力变压器的状态监测对于电网的可靠和经济运行至关重要。健康指数（HI）公式化是一种实用的方法，用于联合收割机组合多个信息源，并为资产管理规划生成一致的健康状态指标。通常，现有的Transformer HI方法基于特定Transformer子系统的专家知识或数据驱动模型。然而，不确定性的影响时，不考虑整合专家知识和数据驱动的模型，系统级HI估计。随着动态和非确定性工程问题的增加，电力和能源应用中的不确定性来源也在增加，例如具有新动态负载的电动汽车或具有断电周期的核电站，并且不确定性下的Transformer健康评估对于准确的状态监测变得至关重要。在此背景下，本文提出了一种新的软计算驱动的概率HI框架Transformer健康监测。该方法封装数据分析和专家知识沿着与不同来源的不确定性，并推断出一个Transformer HI值与置信区间的不确定性下的决策。使用核电厂的真实的数据，所提出的框架与传统的HI实施和结果证实了该方法的有效性Transformer健康评估。
Condition monitoring of power transformers is crucial for the reliable and cost-effective operation of the power grid. The health index (HI) formulation is a pragmatic approach to combine multiple information sources and generate a consistent health state indicator for asset management planning. Generally, existing transformer HI methods are based on expert knowledge or data-driven models of specific transformer subsystems. However, the effect of uncertainty is not considered when integrating expert knowledge and data-driven models for the system-level HI estimation. With the increased dynamic and non-deterministic engineering problems, the sources of uncertainty are increasing across power and energy applications, e.g. electric vehicles with new dynamic loads or nuclear power plants with de-energized periods, and transformer health assessment under uncertainty is becoming critical for accurate condition monitoring. In this context, this paper presents a novel soft computing driven probabilistic HI framework for transformer health monitoring. The approach encapsulates data analytics and expert knowledge along with different sources of uncertainty and infers a transformer HI value with confidence intervals for decision-making under uncertainty. Using real data from a nuclear power plant, the proposed framework is compared with traditional HI implementations and results confirm the validity of the approach for transformer health assessment.