Research On Data Interruption Anomaly Detection Of Multi-Period Power System Based On Cloud Computing

In order to solve the common problems of high interference and poor detection accuracy in the current multi-period power system data anomaly detection process, a cloud computing-based multi-period power system data interruption anomaly detection method is proposed. Combined with feature acquisition, the abnormal data features are collected in and out, and the acquired feature parameters are de-noised and frequency modulated by genetic algorithm. The final time limit is used to detect the abnormal interruption of data in multi-period power system. Finally, the experiment proves that the anti-interference performance of the method is obviously improved compared with the traditional detection method, and the accuracy of the detection result can be effectively guaranteed.