Regulated Two-Dimensional Deep Convolutional Neural Network-Based Power Quality Classifier for Microgrid
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 fundamenta...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/7/2532 |
| _version_ | 1797439514539458560 |
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| author | Cheng-I Chen Sunneng Sandino Berutu Yeong-Chin Chen Hao-Cheng Yang Chung-Hsien Chen |
| author_facet | Cheng-I Chen Sunneng Sandino Berutu Yeong-Chin Chen Hao-Cheng Yang Chung-Hsien Chen |
| author_sort | Cheng-I Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T11:54:05Z |
| format | Article |
| id | doaj.art-fc58a0a94de54bb6b9322eccda652aed |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T11:54:05Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-fc58a0a94de54bb6b9322eccda652aed2023-11-30T23:11:28ZengMDPI AGEnergies1996-10732022-03-01157253210.3390/en15072532Regulated Two-Dimensional Deep Convolutional Neural Network-Based Power Quality Classifier for MicrogridCheng-I Chen0Sunneng Sandino Berutu1Yeong-Chin Chen2Hao-Cheng Yang3Chung-Hsien Chen4Department of Electrical Engineering, National Central University, Taoyuan 320, TaiwanDepartment of Information and Technology, Immanuel Christian University, Yogyakarta 55571, IndonesiaDepartment of Computer Science and Information Engineering, Asia University, Taichung 413, TaiwanDepartment of Computer Science and Information Engineering, Asia University, Taichung 413, TaiwanMetal Industries Research and Development Centre, Taichung 407, TaiwanDue 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.https://www.mdpi.com/1996-1073/15/7/2532power quality disturbancessignal synchronizationregulated two-dimensional deep convolutional neural networkmicrogridpower quality classifierIEEE Std. 1159 |
| spellingShingle | Cheng-I Chen Sunneng Sandino Berutu Yeong-Chin Chen Hao-Cheng Yang Chung-Hsien Chen Regulated Two-Dimensional Deep Convolutional Neural Network-Based Power Quality Classifier for Microgrid Energies power quality disturbances signal synchronization regulated two-dimensional deep convolutional neural network microgrid power quality classifier IEEE Std. 1159 |
| title | Regulated Two-Dimensional Deep Convolutional Neural Network-Based Power Quality Classifier for Microgrid |
| title_full | Regulated Two-Dimensional Deep Convolutional Neural Network-Based Power Quality Classifier for Microgrid |
| title_fullStr | Regulated Two-Dimensional Deep Convolutional Neural Network-Based Power Quality Classifier for Microgrid |
| title_full_unstemmed | Regulated Two-Dimensional Deep Convolutional Neural Network-Based Power Quality Classifier for Microgrid |
| title_short | Regulated Two-Dimensional Deep Convolutional Neural Network-Based Power Quality Classifier for Microgrid |
| title_sort | regulated two dimensional deep convolutional neural network based power quality classifier for microgrid |
| topic | power quality disturbances signal synchronization regulated two-dimensional deep convolutional neural network microgrid power quality classifier IEEE Std. 1159 |
| url | https://www.mdpi.com/1996-1073/15/7/2532 |
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