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首页> 外文期刊>American Journal of Electrical Power and Energy Systems >Early Anomaly Detection for Power Systems Based on Kullback-Leibler Divergence Using Factor Model Analysis
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Early Anomaly Detection for Power Systems Based on Kullback-Leibler Divergence Using Factor Model Analysis

机译：基于Kullback-Leibler利用因子模型分析的基于Kullback-Leibler发散的早期异常检测

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摘要

Real-time anomaly detection is a critical monitoring task for power systems. Most studies of power network detection fail to identify small fault signals or disturbances that might lead to damages or system-wide blackout. This work presents a methodology for analyzing high-dimensional PMU data and detecting early events for large-scale power systems in a non-Gaussian noise environment. Also, spatio-temporal correlations of PMU data are explored and determined by the factor model for anomaly detection. Based on random matrix theory, the factor model monitors the variation of spatio-temporal correlations in PMU data and estimates the number of dynamic factors. Kullback-Leibler Divergence is employed to measure the deviation between two spectral distributions: the empirical spectral distribution of the covariance matrix of residuals from online monitoring data and its theoretical spectral distribution determined by the factor model. Using IEEE 57-bus, IEEE 118-bus, and Polish 2383-bus systems, three different case studies demonstrate that the proposed method is more effective in identifying early-stage anomalies in high-dimensional PMU data collected from large-scale power networks. Performance evaluations validate that this method is sensitive and robust to small fault signals compared with other statistical approaches. The proposed method is a data-driven approach that doesn't require any prior knowledge of the topology of power networks.
机译：实时异常检测是电力系统的关键监控任务。大多数对电网检测的研究无法识别可能导致损坏或系统宽的损坏的小故障信号或干扰。该工作提出了一种用于分析高维PMU数据并在非高斯噪声环境中检测大型电力系统的早期事件的方法。此外，PMU数据的时空相关性由因子模型进行异常检测。基于随机矩阵理论，因子模型监测PMU数据中的时空相关性的变化，估计动态因子的数量。采用Kullback-Leibler发散来测量两个光谱分布之间的偏差：从在线监测数据和由因子模型确定的剩余协方差矩阵的经验谱分布及其理论谱分布。使用IEEE 57总线，IEEE 118公交车和波兰2383总线系统，三种不同的案例研究表明，所提出的方法在识别从大型电网收集的高维PMU数据中的早期异常更有效。性能评估验证该方法与其他统计方法相比，对小故障信号是敏感和强大的。所提出的方法是一种数据驱动方法，不需要任何先前了解电力网络拓扑的知识。

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