Regulated Two-Dimensional Deep Convolutional Neural Network-Based Power Quality Classifier for Microgrid

Cheng-I Chen, Sunneng Sandino Berutu, Yeong-Chin Chen, Hao-Cheng Yang and Chung-Hsien Chen
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Cheng-I Chen: Department of Electrical Engineering, National Central University, Taoyuan 320, Taiwan
Sunneng Sandino Berutu: Department of Information and Technology, Immanuel Christian University, Yogyakarta 55571, Indonesia
Yeong-Chin Chen: Department of Computer Science and Information Engineering, Asia University, Taichung 413, Taiwan
Hao-Cheng Yang: Department of Computer Science and Information Engineering, Asia University, Taichung 413, Taiwan
Chung-Hsien Chen: Metal Industries Research and Development Centre, Taichung 407, Taiwan

Energies, 2022, vol. 15, issue 7, 1-16

Abstract: Due to the penetration of renewable energy and load variation in the microgrid, the diagnosis of power quality disturbances (PQD) is important to the operation stability and safety of the microgrid system. Once the power imbalance is present between the generation and the load demand, the fundamental frequency would deviate from the nominal value. As a result, the performance of the power quality classifier based on the neural network would be deteriorated since the deviation of fundamental frequency is not taken into account. In this paper, the regulated two-dimensional (2D) deep convolutional neural network (CNN)-based approach for PQD classification is proposed. In the data preprocessing stage, the IEC-based synchronizer is introduced to detect the deviation of fundamental frequency. In this way, the 2D grayscale image serving as the input of the deep CNN classifier can be accurately regulated. The obtained 2D image can effectively preserve information and waveform characteristics of the PQD signal. The experiment is implemented with datasets containing 14 different categories of PQD. According to this result, it is revealed that the regulated 2D deep CNN can improve the effectiveness of PQD classification in a real-time manner. Furthermore, the proposed method outperforms the methods in previous studies according to the field verification.

Keywords: power quality disturbances; signal synchronization; regulated two-dimensional deep convolutional neural network; microgrid; power quality classifier; IEEE Std. 1159 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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