A Novel CNN-Based Framework for Detection and Classification of Power Quality Disturbances: Exploring Multi-Class Versus Multi-Label Classification
The detection and classification of power quality (PQ) disturbances remains a significant challenge because of the rapid integration of renewable energy sources (RES), widespread use of power electronics, and increasing prevalence of sensitive microcontrollers. These evolving PQ issues necessitate t...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10906492/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850034007809458176 |
|---|---|
| author | Aleksandra Zlatkova Dimitar Taskovski |
| author_facet | Aleksandra Zlatkova Dimitar Taskovski |
| author_sort | Aleksandra Zlatkova |
| collection | DOAJ |
| description | The detection and classification of power quality (PQ) disturbances remains a significant challenge because of the rapid integration of renewable energy sources (RES), widespread use of power electronics, and increasing prevalence of sensitive microcontrollers. These evolving PQ issues necessitate the development of accurate and reliable methods for identifying and classifying PQ disturbances. In this paper, we propose a novel model based on a deep convolutional neural network (DCNN) for the feature extraction and classification of PQ disturbances. The architecture of the model was inspired by the visual geometry group (VGG), which is known for its effectiveness in image processing. The extracted features are highly suitable for both multi−class (MC) and multi−label (ML) classification tasks, effectively addressing the complexity of PQ disturbance signals. The ML approach proved its excellence in the classification of complex PQ disturbances. The performance of the model was rigorously evaluated using various metrics across different scenarios, which demonstrated exceptional accuracy and robustness. The model was trained, validated, and tested using synthetically generated data under different signal−to−noise ratio (SNR) scenarios ensuring its effectiveness in practical applications. |
| format | Article |
| id | doaj-art-148b45c48f1844a0a73c724d1bbfb2fd |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-148b45c48f1844a0a73c724d1bbfb2fd2025-08-20T02:57:59ZengIEEEIEEE Access2169-35362025-01-0113388783888810.1109/ACCESS.2025.354652010906492A Novel CNN-Based Framework for Detection and Classification of Power Quality Disturbances: Exploring Multi-Class Versus Multi-Label ClassificationAleksandra Zlatkova0https://orcid.org/0000-0003-2983-3148Dimitar Taskovski1https://orcid.org/0000-0001-8651-0973Faculty of Electrical Engineering and Information Technologies, Ss. Cyril and Methodius University in Skopje, Skopje, North MacedoniaFaculty of Electrical Engineering and Information Technologies, Ss. Cyril and Methodius University in Skopje, Skopje, North MacedoniaThe detection and classification of power quality (PQ) disturbances remains a significant challenge because of the rapid integration of renewable energy sources (RES), widespread use of power electronics, and increasing prevalence of sensitive microcontrollers. These evolving PQ issues necessitate the development of accurate and reliable methods for identifying and classifying PQ disturbances. In this paper, we propose a novel model based on a deep convolutional neural network (DCNN) for the feature extraction and classification of PQ disturbances. The architecture of the model was inspired by the visual geometry group (VGG), which is known for its effectiveness in image processing. The extracted features are highly suitable for both multi−class (MC) and multi−label (ML) classification tasks, effectively addressing the complexity of PQ disturbance signals. The ML approach proved its excellence in the classification of complex PQ disturbances. The performance of the model was rigorously evaluated using various metrics across different scenarios, which demonstrated exceptional accuracy and robustness. The model was trained, validated, and tested using synthetically generated data under different signal−to−noise ratio (SNR) scenarios ensuring its effectiveness in practical applications.https://ieeexplore.ieee.org/document/10906492/Convolutional neural networkdeep learningmulti-class classificationmulti-label classificationpower quality |
| spellingShingle | Aleksandra Zlatkova Dimitar Taskovski A Novel CNN-Based Framework for Detection and Classification of Power Quality Disturbances: Exploring Multi-Class Versus Multi-Label Classification IEEE Access Convolutional neural network deep learning multi-class classification multi-label classification power quality |
| title | A Novel CNN-Based Framework for Detection and Classification of Power Quality Disturbances: Exploring Multi-Class Versus Multi-Label Classification |
| title_full | A Novel CNN-Based Framework for Detection and Classification of Power Quality Disturbances: Exploring Multi-Class Versus Multi-Label Classification |
| title_fullStr | A Novel CNN-Based Framework for Detection and Classification of Power Quality Disturbances: Exploring Multi-Class Versus Multi-Label Classification |
| title_full_unstemmed | A Novel CNN-Based Framework for Detection and Classification of Power Quality Disturbances: Exploring Multi-Class Versus Multi-Label Classification |
| title_short | A Novel CNN-Based Framework for Detection and Classification of Power Quality Disturbances: Exploring Multi-Class Versus Multi-Label Classification |
| title_sort | novel cnn based framework for detection and classification of power quality disturbances exploring multi class versus multi label classification |
| topic | Convolutional neural network deep learning multi-class classification multi-label classification power quality |
| url | https://ieeexplore.ieee.org/document/10906492/ |
| work_keys_str_mv | AT aleksandrazlatkova anovelcnnbasedframeworkfordetectionandclassificationofpowerqualitydisturbancesexploringmulticlassversusmultilabelclassification AT dimitartaskovski anovelcnnbasedframeworkfordetectionandclassificationofpowerqualitydisturbancesexploringmulticlassversusmultilabelclassification AT aleksandrazlatkova novelcnnbasedframeworkfordetectionandclassificationofpowerqualitydisturbancesexploringmulticlassversusmultilabelclassification AT dimitartaskovski novelcnnbasedframeworkfordetectionandclassificationofpowerqualitydisturbancesexploringmulticlassversusmultilabelclassification |