An Abnormal State Detection Method for Power Distribution Network Based on Big Data Technology

This paper focuses on using big data technology to solve the abnormal state detection problem in power distribution system. With the increasingly more widespread use of digitalization technology, various related systems have been embedded extensively in power system, resulting in a large number of interconnected observations. In order to discover more complex deep-seated rules and provide more effective decision support for power system decision-making, it is necessary to study data mining and analysis methods that are suitable for massive data under current situation. This paper studies the method to identify abnormal data from multi-temporal and multi-spatial data in distribution networks and propose a method to detective abnormal operation state using likelihood-ratio test for three-dimensional spatiotemporal data. In order to speed up the data processing rate, an anomaly detection method based on multi-threading and Hadoop parallelization methods and techniques is proposed.