Anomaly Detection Algorithm Based on Electric Equipment

Abstract

Traditional methods for detecting data anomalies of power equipment fail to fully mine the data characteristics. And it has shortcomings such as complex calculation, poor flexibility and low accuracy. To solve the problem of abnormal fault detection of electric equipment, the abnormal detection algorithm based on statistical analysis and machine learning is used in the paper. The reconstruction method based on Long Short Term Memory (LSTM) time sequence is proposed for time series data abnormal detection. Experiments show that the new method is effective in detecting and correcting abnormal data, which reduces the detection time and improves the accuracy of state estimation results. There are huge differences within the positive samples in anomaly detection, so several methods are studied in the paper. One-class SVM algorithm is often used in novelty detection and isolation forest algorithm is used in outlier detection. Machine learning methods which is based on statistical analysis will play an increasingly important role in the fields of equipment monitoring and predictive maintenance, safety of electric equipment.