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A Machine-Learning Based Method to Analyze the Correlation between Meteorological Data and Component Outages of Power System

机译:基于机器学习的气象数据与电力系统组件损耗相关性分析方法

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Apart from the operating conditions of power systems, weather conditions have great impact on the reliability performance of the components in power systems. Analyzing the underlying correlation between weather conditions and component outages will be essential to the operating reliability of the systems. This paper proposes a machine-learning based method to analyze the underlying correlation based on real time data. A data enhancement method is also proposed in case the realistic data are insufficient or unavailable. Case studies based on realistic meteorological data of 5 areas in Fujian, China, combined with the IEEE RTS-96 system, demonstrate the effectiveness of the proposed method.
机译:除了电力系统的运行状况外,天气状况还对电力系统中组件的可靠性能产生很大影响。分析天气状况和组件故障之间的潜在相关性对于系统的运行可靠性至关重要。本文提出了一种基于机器学习的方法,用于基于实时数据分析潜在的相关性。在实际数据不足或不可用的情况下,还提出了一种数据增强方法。基于中国福建省5个地区的实际气象数据的案例研究,结合IEEE RTS-96系统,证明了该方法的有效性。

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