Feature Extraction and Classification of Power Quality Disturbances Using Optimized Tunable-Q Wavelet Transform and Incremental Support Vector Machine
The widespread integration of renewable energy sources (RESs) into power systems using power electronics-based interface devices has led to a substantial rise in power quality (PQ) issues. There is an immediate requirement for effective monitoring, detection, and classification of power quality dist...
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| Format: | Article |
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Wiley
2024-01-01
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| Series: | International Transactions on Electrical Energy Systems |
| Online Access: | http://dx.doi.org/10.1155/2024/1335666 |
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| author | Indu Sekhar Samanta Pravat Kumar Rout Kunjabihari Swain Satyasis Mishra Murthy Cherukuri |
| author_facet | Indu Sekhar Samanta Pravat Kumar Rout Kunjabihari Swain Satyasis Mishra Murthy Cherukuri |
| author_sort | Indu Sekhar Samanta |
| collection | DOAJ |
| description | The widespread integration of renewable energy sources (RESs) into power systems using power electronics-based interface devices has led to a substantial rise in power quality (PQ) issues. There is an immediate requirement for effective monitoring, detection, and classification of power quality disturbances (PQDs) that is needed to take remedial measures and design planning of the system architecture. This study presents a hybrid approach with an objective for the feature extraction and classification of PQDs. The proposed hybrid approach is comprised of an optimized tunable-Q wavelet transform (OTQWT) for the feature extraction and incremental support vector machine (ISVM). A four-stage approach is suggested for the PQ detection and classification in this study. In the first stage, the various data are retrieved both in the form of synthetic data by mathematical formulations and real-time data with prototype design setup. In the second stage, regardless of the specified wavelet function, the PQD signals are decomposed into low-pass and high-pass sub-bands using the tunable-Q wavelet transform (TQWT). However, the utilization of default decomposition parameters to address nonstationary PQ signals may lead to information loss and reduced performance of the system. To avoid this limitation, an OTQWT as an enhanced technique to TQWT based on an Adaptive Particle Swarm Optimization (APSO) is suggested. A modified objective function based on the mean square error (MSE) is used to improve the decomposition process. In the third stage, an efficient classifier is suggested based on the ISVM. Lastly, to test and evaluate the performance of the proposed approach, twelve types of PQDs including noise and multiple occurrences are considered. The comparative analysis with other popular methods reflects the better performance of the proposed approach and justifies its use for PQ detection and classification purposes in real-time conditions. |
| format | Article |
| id | doaj-art-e68125b2bf3445cb8758c2ff4d53022d |
| institution | Kabale University |
| issn | 2050-7038 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Transactions on Electrical Energy Systems |
| spelling | doaj-art-e68125b2bf3445cb8758c2ff4d53022d2025-08-20T03:26:03ZengWileyInternational Transactions on Electrical Energy Systems2050-70382024-01-01202410.1155/2024/1335666Feature Extraction and Classification of Power Quality Disturbances Using Optimized Tunable-Q Wavelet Transform and Incremental Support Vector MachineIndu Sekhar Samanta0Pravat Kumar Rout1Kunjabihari Swain2Satyasis Mishra3Murthy Cherukuri4Department of Computer Science and EngineeringDepartment of Electrical and Electronics EngineeringDepartment of Electrical and Electronics EngineeringDepartment of Electronics and Communication EngineeringDepartment of Electrical and Electronics EngineeringThe widespread integration of renewable energy sources (RESs) into power systems using power electronics-based interface devices has led to a substantial rise in power quality (PQ) issues. There is an immediate requirement for effective monitoring, detection, and classification of power quality disturbances (PQDs) that is needed to take remedial measures and design planning of the system architecture. This study presents a hybrid approach with an objective for the feature extraction and classification of PQDs. The proposed hybrid approach is comprised of an optimized tunable-Q wavelet transform (OTQWT) for the feature extraction and incremental support vector machine (ISVM). A four-stage approach is suggested for the PQ detection and classification in this study. In the first stage, the various data are retrieved both in the form of synthetic data by mathematical formulations and real-time data with prototype design setup. In the second stage, regardless of the specified wavelet function, the PQD signals are decomposed into low-pass and high-pass sub-bands using the tunable-Q wavelet transform (TQWT). However, the utilization of default decomposition parameters to address nonstationary PQ signals may lead to information loss and reduced performance of the system. To avoid this limitation, an OTQWT as an enhanced technique to TQWT based on an Adaptive Particle Swarm Optimization (APSO) is suggested. A modified objective function based on the mean square error (MSE) is used to improve the decomposition process. In the third stage, an efficient classifier is suggested based on the ISVM. Lastly, to test and evaluate the performance of the proposed approach, twelve types of PQDs including noise and multiple occurrences are considered. The comparative analysis with other popular methods reflects the better performance of the proposed approach and justifies its use for PQ detection and classification purposes in real-time conditions.http://dx.doi.org/10.1155/2024/1335666 |
| spellingShingle | Indu Sekhar Samanta Pravat Kumar Rout Kunjabihari Swain Satyasis Mishra Murthy Cherukuri Feature Extraction and Classification of Power Quality Disturbances Using Optimized Tunable-Q Wavelet Transform and Incremental Support Vector Machine International Transactions on Electrical Energy Systems |
| title | Feature Extraction and Classification of Power Quality Disturbances Using Optimized Tunable-Q Wavelet Transform and Incremental Support Vector Machine |
| title_full | Feature Extraction and Classification of Power Quality Disturbances Using Optimized Tunable-Q Wavelet Transform and Incremental Support Vector Machine |
| title_fullStr | Feature Extraction and Classification of Power Quality Disturbances Using Optimized Tunable-Q Wavelet Transform and Incremental Support Vector Machine |
| title_full_unstemmed | Feature Extraction and Classification of Power Quality Disturbances Using Optimized Tunable-Q Wavelet Transform and Incremental Support Vector Machine |
| title_short | Feature Extraction and Classification of Power Quality Disturbances Using Optimized Tunable-Q Wavelet Transform and Incremental Support Vector Machine |
| title_sort | feature extraction and classification of power quality disturbances using optimized tunable q wavelet transform and incremental support vector machine |
| url | http://dx.doi.org/10.1155/2024/1335666 |
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