Adaptive-Fuzzy-Neural-Network Data-Fusion-Based Fault-Location Technique Using Wide-Area Synchronized Measurements for Transmission Grids

Improvement of fault location accuracy plays a great role in decreasing repair time and expediting service restoration. However, intricate measurement conditions are produced by some factors including data variation and noise. These factors would lead to nonconformity in reporting samples from multiple detectors and cause errors in the final results calculating. To improve fault location accuracy in transmission grids, an innovative data fusion algorithm, based on the Adaptive Fuzzy Neural Network (AFNN) mechanism, is proposed in this paper. In the model, Fuzzy Data Fusion (FDF) mechanism is formed and serves as the initial fusion to estimate the correction coefficient of Faulted Traveling Wave Propagation Speed (FTWPS). In the final fusion, Adaptive Fuzzy-Neural-Network-based Data Fusion Systems (AFNN-DFSs) are trained to yield the final fault location results with high accuracy. The overall procedure of the proposed fault location technique is constructed using PSCAD/EMTDC and MATLAB. Besides, the feature of the faulted traveling wave is extracted by using Continuous Wavelet Transform (CWT). Finally, case studies and discussions on the new method, based on AFNN-DFSs, are given to prove the advantages of the novel method in computational accuracy.