The online monitoring data in distribution networks contain rich information on the running states of the networks. By leveraging the data, this paper proposes a spatio-temporal correlation analysis approach for anomaly detection and location in distribution networks. First, spatio-temporal matrix for each feeder line in a distribution network is formulated and the spectrum of its covariance matrix is analyzed. The spectrum is complex and exhibits two aspects: 1) bulk, which arises from random noise or fluctuations and 2) spikes, which represents factors caused by anomaly signals or fault disturbances. Then, by connecting the estimation of the number of factors to the limiting empirical spectral density of covariance matrices of residuals, the spatio-temporal parameters are accurately estimated, during which free random variable techniques are used. Based on the estimators, anomaly indicators are designed to detect and locate the anomalies by exploring the variations of spatio-temporal correlations in the data. The proposed approach is sensitive to the anomalies and robust to random fluctuations, which makes it possible for detecting early anomalies and reducing false alarming rate. Case studies on both synthetic data and real-world online monitoring data verify the effectiveness and advantages of the proposed approach.

中文翻译：

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配电网异常检测与定位在线监测数据的时空相关分析

配电网络中的在线监视数据包含有关网络运行状态的丰富信息。通过利用数据，本文提出了一种时空相关分析方法，用于配电网中的异常检测和定位。首先，为配电网中的每条馈线制定时空矩阵，并分析其协方差矩阵的频谱。该频谱很复杂，表现出两个方面：1）体积大，这是由随机噪声或波动引起的； 2）尖峰，它表示由异常信号或故障干扰引起的因素。然后，通过将因子数量的估计与残差协方差矩阵的极限经验光谱密度联系起来，可以准确地估计时空参数，在此期间，使用自由随机变量技术。基于估计量，异常指标旨在通过探索数据中时空相关性的变化来检测和定位异常。所提出的方法对异常敏感并且对随机波动具有鲁棒性，这使得可以检测早期异常并降低虚警率。综合数据和现实世界在线监测数据的案例研究证明了该方法的有效性和优势。这样就可以检测早期异常并降低误报率。综合数据和现实世界在线监测数据的案例研究证明了该方法的有效性和优势。这样就可以检测早期异常并降低误报率。综合数据和现实世界在线监测数据的案例研究证明了该方法的有效性和优势。