| Title | Detection and Classification of Power Quality Disturbances Using Variational Mode Decomposition and Convolutional Neural Networks |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Deng, Weimin, Xu, Da, Xu, Yuhan, Li, Mengshi |
| Conference Name | 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC) |
| Keywords | composability, compositionality, Conferences, convolutional neural networks, decomposition, Deep Learning, feature extraction, Hybrid power systems, Metrics, power quality, pubcrawl, Signal processing, variational mode decomposition |
| Abstract | Power quality gains more and more attentions because disturbances in power quality may damage equipment security, power availability and system reliability in power system. Detection and classification of the power quality disturbances is the first step before taking measures to lessen their harmful effects. Common methods to classify power quality disturbances includes signal processing methods, machine learning methods and deep learning methods. Signal processing methods are good at feature extraction, while machine learning methods and deep learning methods are expert in multi-classification tasks. Via combing their respective advantages, this paper proposes a combined method based on variational mode decomposition and convolutional neural networks, which needs a small quantity of samples but achieves high classification precision. The proposed method is proved to be a qualified and competitive scheme for the detection and classification of power quality disturbances. |
| DOI | 10.1109/CCWC51732.2021.9376031 |
| Citation Key | deng_detection_2021 |
Detection and Classification of Power Quality Disturbances Using Variational Mode Decomposition and Convolutional Neural Networks