Power Quality Disturbance Recognition Using VMD-Based Feature Extraction and Heuristic Feature Selection

• Main Authors: Lei Fu, Tiantian Zhu, Guobing Pan, Sihan Chen, Qi Zhong, Yanding Wei
• Format: Article
• Language: English
• Published: MDPI AG 2019-11-01
• Series: Applied Sciences
• Subjects: power quality disturbances; variational mode decomposition; permutation entropy; heuristic feature selection; multi-class support vector machine
• Online Access: https://www.mdpi.com/2076-3417/9/22/4901

Power quality disturbances (PQDs) have a large negative impact on electric power systems with the increasing use of sensitive electrical loads. This paper presents a novel hybrid algorithm for PQD detection and classification. The proposed method is constructed while using the following main steps: computer simulation of PQD signals, signal decomposition, feature extraction, heuristic selection of feature selection, and classification. First, different types of PQD signals are generated by computer simulation. Second, variational mode decomposition (VMD) is used to decompose the signals into several instinct mode functions (IMFs). Third, the statistical features are calculated in the time series for each IMF. Next, a two-stage feature selection method is imported to eliminate the redundant features by utilizing permutation entropy and the Fisher score algorithm. Finally, the selected feature vectors are fed into a multiclass support vector machine (SVM) model to classify the PQDs. Several experimental investigations are performed to verify the performance and effectiveness of the proposed method in a noisy environment. Moreover, the results demonstrate that the start and end points of the PQD can be efficiently detected.