Detection of PQ Short Duration Variations using Stockwell Transform with LSTM
Ali, M., Oda, E., Abdelsalam, A., Abdelaziz, A. (2023). Detection of PQ Short Duration Variations using Stockwell Transform with LSTM. EKB Journal Management System, 1(2), 33-48. doi: 10.21608/sceee.2023.240048.1006
Mohamed Ali Ali; Eyad S Oda; Abdelazeem Abdelsalam; Almoataz Y Abdelaziz. "Detection of PQ Short Duration Variations using Stockwell Transform with LSTM". EKB Journal Management System, 1, 2, 2023, 33-48. doi: 10.21608/sceee.2023.240048.1006
Ali, M., Oda, E., Abdelsalam, A., Abdelaziz, A. (2023). 'Detection of PQ Short Duration Variations using Stockwell Transform with LSTM', EKB Journal Management System, 1(2), pp. 33-48. doi: 10.21608/sceee.2023.240048.1006
Ali, M., Oda, E., Abdelsalam, A., Abdelaziz, A. Detection of PQ Short Duration Variations using Stockwell Transform with LSTM. EKB Journal Management System, 2023; 1(2): 33-48. doi: 10.21608/sceee.2023.240048.1006

Detection of PQ Short Duration Variations using Stockwell Transform with LSTM

Suez Canal Engineering, Energy and Environmental Science
Volume 1, Issue 2, July 2023, Page 33-48  XML PDF (506.03 K)
Document Type: Original Article
DOI: 10.21608/sceee.2023.240048.1006
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Authors
Mohamed Ali Ali email orcid 1; Eyad S Oda2; Abdelazeem Abdelsalam3; Almoataz Y Abdelaziz4
1Operation Engineer
2Electrical Engineering Department, Faculty of Engineering, Suez Canal University, Ismailia, Egypt
3Ismailia
4Electrical Power & Machines Dept., Faculty of Engineering, Ain Shams University, Cairo, Egypt
Abstract
Classification and detection of power quality disturbances (PQDs) are high priorities within the electrical power system. We are using feature extraction with artificial intelligence (AI) and deep learning to solve PQD problems using a two-step methodology: Feature extraction and classification steps, with the Feature extraction step utilizing Stockwell Transform and the classification step employing Long Short-Term Memory techniques. This work aims to use Stockwell Transform as a feature extraction using a Deep Learning (DL) approach known as LSTM for the classification and detection of PQ disturbance occurrences.
Signal characteristics are collected from the time-frequency analysis data based on Stockwell transform utilizing the Deep Learning technique in the long short-term memory (LSTM) network, which finds and classifies PQ disturbance events. By integrating the S-transform with the long short-term memory (LSTM) network, it is possible to achieve a high level of classification efficiency. Many PQ disturbances are treated with single and combination disruptions. The findings demonstrate that the proposed approach is precise and robust in detecting and identifying single and combination PQ disruptions. In comparison with many concise studies, the proposed strategy performs well.
Keywords
Power quality; Detection; Short duration variations; LSTM; S-transform
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