Classification of Power Quality Disturbance Based on Multiscale Singular Spectral Analysis and Multi Resolution Wavelet Transforms
Muhammad Abubakar1, Muhammad Shahzad2, Khalil Ur Rehman3, Benjamin Doh4, Benjamin Kwame Adobah5
1Muhammad Abubakar*, School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China.
2Muhammad Shahzad, School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China.
3Khalil Ur Rehman, School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China.
4Benjamin Doh, School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China.
5Benjamin Kwame Adobah, School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China.
Manuscript received on November 20, 2019. | Revised Manuscript received on November 28, 2019. | Manuscript published on 30 November, 2019. | PP: 6654-6659 | Volume-8 Issue-4, November 2019. | Retrieval Number: D8991118419/2019©BEIESP | DOI: 10.35940/ijrte.D8991.118419
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In real power system, Power quality disturbances (PQDs) have become major challenge due to the introduction of renewable energy resources and embedded power systems. In this research, two novel feature extraction methods multi resolution analysis wavelet transform (MRA-WT) and Multiscale singular spectral analysis (MSSA) have been analysed with convolution neural network classifier for the classification of PQDs. Statistical parameters are also applied for the optimal feature selection. MSSA is time-series tool and MRA-WT are applied for feature extraction and 1-dimensional CNN (1-DCNN) is used to classify the single and multiple PQDs. The architecture is built with forward propagation and back propagation is utilized to tune the data. Finally, the results of two selected feature extraction techniques are compared with classification accuracy. The simulation based results explained that MSSA with 1-DCNN has significantly higher classification accuracy under different noisy conditions.
Keywords: Power Quality Disturbance, Multiscale Singular Spectral Analysis, Multi Resolution Analysis Wavelet Transforms, Convolutional Neural Network.
Scope of the Article: Classification.