An Approach of Data Anomaly Detection in Power Dispatching Streaming Data Based on Isolation Forest Algorithm

Abstract

Dispatching is the guarantee of safe operation of power system. For scheduling monitoring flow data with "concept drift" feature, there are many issues in some aspects when using traditional offline data analysis or simple threshold judgement of anomaly detection methods, such as defective tightness between existing production systems and real-time operation state, excessivedependence on expert experience, etc. This paper puts forward an algorithm based on isolated forest data anomaly detection method of electric power dispatching flow, using historical data set to train and build many child forest anomaly detectors and compose base forest anomaly detector. Therefore, the detector update is triggered online according to abnormal data situation in sliding window and data size in cache area. An update strategy selecting sub-forest abnormal detector according to the size of abnormal deviation rate is proposed to solve dropping issue of the abnormal detector performance caused by random update. Using the flow of business data set from servers and provincial power grid dispatching centers as training and testing samples, the proposed method is verified on its advancement on comprehensive performance of anomaly detection such as recall and precision, feasible in practical system application.