Multi-end partial discharge location algorithm based on trimmed mean data filtering technique for MV underground cable

A remarkable challenge in online partial discharge monitoring is to extract desired partial discharge signal from excessive noise caused by the operating cable. Wavelet based de-noising technique can be utilized for noise suppression. However, the wavelet based de-noising technique's ability to fully suppress the non-constant level of noise is limited. Therefore, trimmed mean data filtering technique is applied in partial discharge location algorithm for statistical analysis. In the process of creating trimmed mean partial discharge location algorithm, multi-end partial discharge location algorithm is restructured by adding trimmed mean data filtering technique. Both multi-end partial discharge location algorithm and trimmed mean partial discharge location algorithm had been tested for their performance. In the performance test, noise is modeled and added into the simulated partial discharge signals to enhance the similarity of simulated partial discharge signals with real detected partial discharge signals. Discrete wavelet transform de-noising technique is used for suppressing various level of simulated noise in partial discharge signals. The test result showed that trimmed mean partial discharge location algorithm enhances the accuracy in estimating partial discharge location under noisy environment.