Fast Support Vector Machine for Power Quality Disturbance Classification
The power quality disturbance (PQD) problem involves problems of voltage swell, voltage sag, power interruption, harmonics and complex events involving multiple PQD problems. The PQD problem attracted considerable attention from utilities, especially when renewable energy is getting a higher penetra...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/22/11649 |
| _version_ | 1827645063803437056 |
|---|---|
| author | Whei-Min Lin Chien-Hsien Wu |
| author_facet | Whei-Min Lin Chien-Hsien Wu |
| author_sort | Whei-Min Lin |
| collection | DOAJ |
| description | The power quality disturbance (PQD) problem involves problems of voltage swell, voltage sag, power interruption, harmonics and complex events involving multiple PQD problems. The PQD problem attracted considerable attention from utilities, especially when renewable energy is getting a higher penetration. The PQD problem could downgrade the service quality, causing problems of malfunctions and instabilities. This paper proposed a simplified SVM technique to identify the PQD problem including the multiple PQD classification. With the simple structure proposed, the methodology could reduce a great deal of training data; requires much less memory space and saves computing time. An IEEE 14-bus power system was used to show the performance. Many tests were conducted, and the method was compared with an artificial neural network (ANN). Simulation results showed the shortened processing time and the effectiveness of the proposed approach. |
| first_indexed | 2024-03-09T18:29:14Z |
| format | Article |
| id | doaj.art-50d0c3c3066d43bcae4705ef3ea706d2 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T18:29:14Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-50d0c3c3066d43bcae4705ef3ea706d22023-11-24T07:38:41ZengMDPI AGApplied Sciences2076-34172022-11-0112221164910.3390/app122211649Fast Support Vector Machine for Power Quality Disturbance ClassificationWhei-Min Lin0Chien-Hsien Wu1School of Mechanical and Electrical Engineering, Tan Kah Kee College, Xiamen University, Zhangzhou 361005, ChinaDepartment of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung 80424, TaiwanThe power quality disturbance (PQD) problem involves problems of voltage swell, voltage sag, power interruption, harmonics and complex events involving multiple PQD problems. The PQD problem attracted considerable attention from utilities, especially when renewable energy is getting a higher penetration. The PQD problem could downgrade the service quality, causing problems of malfunctions and instabilities. This paper proposed a simplified SVM technique to identify the PQD problem including the multiple PQD classification. With the simple structure proposed, the methodology could reduce a great deal of training data; requires much less memory space and saves computing time. An IEEE 14-bus power system was used to show the performance. Many tests were conducted, and the method was compared with an artificial neural network (ANN). Simulation results showed the shortened processing time and the effectiveness of the proposed approach.https://www.mdpi.com/2076-3417/12/22/11649Power Quality Disturbances (PQD)Support Vector Machine (SVM)binary classification |
| spellingShingle | Whei-Min Lin Chien-Hsien Wu Fast Support Vector Machine for Power Quality Disturbance Classification Applied Sciences Power Quality Disturbances (PQD) Support Vector Machine (SVM) binary classification |
| title | Fast Support Vector Machine for Power Quality Disturbance Classification |
| title_full | Fast Support Vector Machine for Power Quality Disturbance Classification |
| title_fullStr | Fast Support Vector Machine for Power Quality Disturbance Classification |
| title_full_unstemmed | Fast Support Vector Machine for Power Quality Disturbance Classification |
| title_short | Fast Support Vector Machine for Power Quality Disturbance Classification |
| title_sort | fast support vector machine for power quality disturbance classification |
| topic | Power Quality Disturbances (PQD) Support Vector Machine (SVM) binary classification |
| url | https://www.mdpi.com/2076-3417/12/22/11649 |
| work_keys_str_mv | AT wheiminlin fastsupportvectormachineforpowerqualitydisturbanceclassification AT chienhsienwu fastsupportvectormachineforpowerqualitydisturbanceclassification |