S-Transform Based FFNN Approach for Distribution Grids Fault Detection and Classification

• Published in: IEEE access Vol. 6; pp. 8080 - 8088
• Main Authors: Shafiullah, Md, Abido, M. A.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Additive white Gaussian noise; Artificial neural networks; Classification; distribution grid; Electrical resistance measurement; fault classification; Fault detection; Feature extraction; feedforward neural network; Feedforward neural networks; Loading; Noise measurement; Phase current; S-transform; Signal processing; Time-frequency analysis; Transformations (mathematics); Transforms
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2018.2809045

Detection and classification of any anomaly at its commencement are very crucial for optimal management of assets in power system grids. This paper presents a novel hybrid approach that combines S-transform (ST) and feedforward neural network (FFNN) for the detection and classification of distribution grid faults. In this proposed strategy, the measured three-phase current signals are processed through ST with a view to extracting useful statistical features. The extracted features are then fetched to FFNN in order to detect and classify different types of faults. The proposed approach is implemented in two different test distribution grids modeled and simulated in real-time digital simulator and MATLAB/SIMULINK. The obtained results justify the efficacy of the presented technique for both noise-free and noisy data. In addition, the developed technique is independent of fault resistance, inception angle, distance, and prefault loading condition. Besides, the comparative results confirm the superiority and competitiveness of the developed technique over the available techniques reported in the literature.