Detection of abnormal load consumption in the power grid using clustering and statistical analysis

Abstract

Nowadays, the electricity load profiles of customers (consumers and prosumers) are changing as new technologies are being developed, and therefore it is necessary to correctly identify new trends, changes and anomalies in data. Anomalies in load consumption can be caused by abnormal behavior of customers or a failure of smart meters in the grid. Accurate identification of such anomalies is crucial for maintaining stability in the grid and reduce electricity loss of distribution companies. Smart meters produce huge amounts of load consumption measurements every day and analyzing all the measurements is computationally expensive and very inefficient. Therefore, the aim of this work is to propose an anomaly detection method, that addresses this issue. Our proposed method firstly narrows down potential anomalous customers in large datasets by clustering discretized time series, and then analyses selected profiles using statistical method S-H-ESD to calculate final anomaly score. We evaluated and compared our method to four state-of-the-art anomaly detection methods on created synthetic dataset of load consumption time series containing collective anomalies. Our method outperformed other evaluated methods in terms of accuracy.