Multi-source Traveling Wave Signal Fusion and Fault Identification Supported by Cloud Computing

Abstract

The accuracy and real-time performance of fault identification of multi-source traveling wave signals in current power systems are difficult to guarantee due to problems such as asynchronous acquisition, data heterogeneity, and processing delay. In view of the above problems, this paper constructs a multi-source traveling wave signal fusion and fault identification method based on cloud computing platform, relying on the advantages of cloud computing in resource elastic scheduling and distributed computing to achieve efficient concurrent processing of large-scale heterogeneous signals and global information collaborative analysis. The signal time alignment is completed through the global clock synchronization mechanism, and the unified data format is used for standardized processing. The transient characteristics of each source signal are extracted based on wavelet transform, and the multi-source characteristics are fused in the cloud before inputting into the deep neural network model to complete fault identification and location. The adopted fault identification model is constructed based on long short-term memory network and spatio-temporal attention mechanism, which can realize dynamic weighted fusion of multi-source signal characteristics in time dimension and space dimension, and output fault type discrimination results and location estimation values in an end-to-end manner. Experimental results show that the proposed method achieves a fault identification accuracy of 98.6% on the IEEE 39-node power system simulation platform, and the average system response time is 1.3 seconds, which meets the real-time identification requirements. The research results verify the effectiveness of this method in improving the efficiency and reliability of power system fault handling, and provide technical support for smart grid fault monitoring in the Internet of Things environment.