Deep Learning Algorithm for Automatic Classification of Power Quality Disturbances
Power quality disturbances (PQDs) are major obstacles to maintaining the reliability and stability of electrical systems. This study introduces a new multi-scale deep learning method to classify PQDs, aiming to enhance the accuracy and efficiency of power quality (PQ) analysis and monitoring systems...
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/3/1442 |
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| author | Fatema A. Albalooshi M. R. Qader |
| author_facet | Fatema A. Albalooshi M. R. Qader |
| author_sort | Fatema A. Albalooshi |
| collection | DOAJ |
| description | Power quality disturbances (PQDs) are major obstacles to maintaining the reliability and stability of electrical systems. This study introduces a new multi-scale deep learning method to classify PQDs, aiming to enhance the accuracy and efficiency of power quality (PQ) analysis and monitoring systems. By combining 1-D convolutional neural networks (CNNs) with an attention mechanism, this approach overcomes the limitations of traditional techniques. Moreover, varying-size convolutional layers allow for the direct learning of complex patterns and features from PQ signals. To address the challenge of limited labeled PQ datasets, this research utilizes an open-source dataset generator to create large-scale datasets with annotated PQDs. Through a comparison with existing models in the field, the superiority of the proposed CNN-based approach is evident, achieving an accuracy level of up to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>99.49</mn><mo>%</mo></mrow></semantics></math></inline-formula>. The results demonstrate promising classification performance in terms of simplicity and accuracy, highlighting the potential of this approach to improve PQ analysis and disturbance identification. |
| format | Article |
| id | doaj-art-eb2d7b5eead44e4f84e99c89836c6deb |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-eb2d7b5eead44e4f84e99c89836c6deb2025-08-20T02:48:01ZengMDPI AGApplied Sciences2076-34172025-01-01153144210.3390/app15031442Deep Learning Algorithm for Automatic Classification of Power Quality DisturbancesFatema A. Albalooshi0M. R. Qader1College of Information Technology, University of Bahrain, Sakhir P.O. Box 32038, BahrainCollege of Engineering, University of Bahrain, Sakhir P.O. Box 32038, BahrainPower quality disturbances (PQDs) are major obstacles to maintaining the reliability and stability of electrical systems. This study introduces a new multi-scale deep learning method to classify PQDs, aiming to enhance the accuracy and efficiency of power quality (PQ) analysis and monitoring systems. By combining 1-D convolutional neural networks (CNNs) with an attention mechanism, this approach overcomes the limitations of traditional techniques. Moreover, varying-size convolutional layers allow for the direct learning of complex patterns and features from PQ signals. To address the challenge of limited labeled PQ datasets, this research utilizes an open-source dataset generator to create large-scale datasets with annotated PQDs. Through a comparison with existing models in the field, the superiority of the proposed CNN-based approach is evident, achieving an accuracy level of up to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>99.49</mn><mo>%</mo></mrow></semantics></math></inline-formula>. The results demonstrate promising classification performance in terms of simplicity and accuracy, highlighting the potential of this approach to improve PQ analysis and disturbance identification.https://www.mdpi.com/2076-3417/15/3/1442power quality disturbancemulti-scale CNNdeep learningattention mechanismfeature extraction |
| spellingShingle | Fatema A. Albalooshi M. R. Qader Deep Learning Algorithm for Automatic Classification of Power Quality Disturbances Applied Sciences power quality disturbance multi-scale CNN deep learning attention mechanism feature extraction |
| title | Deep Learning Algorithm for Automatic Classification of Power Quality Disturbances |
| title_full | Deep Learning Algorithm for Automatic Classification of Power Quality Disturbances |
| title_fullStr | Deep Learning Algorithm for Automatic Classification of Power Quality Disturbances |
| title_full_unstemmed | Deep Learning Algorithm for Automatic Classification of Power Quality Disturbances |
| title_short | Deep Learning Algorithm for Automatic Classification of Power Quality Disturbances |
| title_sort | deep learning algorithm for automatic classification of power quality disturbances |
| topic | power quality disturbance multi-scale CNN deep learning attention mechanism feature extraction |
| url | https://www.mdpi.com/2076-3417/15/3/1442 |
| work_keys_str_mv | AT fatemaaalbalooshi deeplearningalgorithmforautomaticclassificationofpowerqualitydisturbances AT mrqader deeplearningalgorithmforautomaticclassificationofpowerqualitydisturbances |