Storm Cloud-based Parallel Fault Diagnosis for Power Equipment
In order to achieve monitoring and diagnosis of multi-source data in power industry and meet the requirements of power system real-time processing,the Storm distributed real-time computing platform was introduced to data process-ing.A mixed clustering based stream data processing model on this platform was deployed to achieve fault diagnosis of da-ta streams.By combining subtractive clustering algorithm with K-means algorithm,fault detection of streaming data was achieved,in which subtractive clustering obtained optimal cluster center,and K-means algorithm calculated out better classification results according to this cluster center.By testing the data processing capacity of clusters and single ma-chines,experiments have shown that setting component parallelism reasonably in a cluster environment can improve the efficiency and throughput of stream computing.
万方数据
维普
