Feature Selection of Power Quality Disturbance Signals with an Entropy-Importance-Based Random Forest
AbstractPower quality signal feature selection is an effective method to improve the accuracy and efficiency of power quality (PQ) disturbance classification. In this paper, an entropy-importance (EnI)-based random forest (RF) model for PQ feature selection and disturbance classification is proposed. Firstly, 35 kinds of signal features extracted from S-transform (ST) with random noise are used as the original input feature vector of RF classifier to recognize 15 kinds of PQ signals with six kinds of complex disturbance. During the RF training process, the classification ability of different features is quantified by EnI. Secondly, without considering the features with zero EnI, the optimal perturbation feature subset is obtained by applying the sequential forward search (SFS) method which considers the classification accuracy and feature dimension. Then, the reconstructed RF classifier is applied to identify disturbances. According to the simulation results, the classification accuracy is higher than that of other classifiers, and the feature selection effect of the new approach is better than SFS and sequential backward search (SBS) without EnI. With the same feature subset, the new method can maintain a classification accuracy above 99.7% under the condition of 30 dB or above, and the accuracy under 20 dB is 96.8%. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Huang, N.; Lu, G.; Cai, G.; Xu, D.; Xu, J.; Li, F.; Zhang, L. Feature Selection of Power Quality Disturbance Signals with an Entropy-Importance-Based Random Forest. Entropy 2016, 18, 44.
Huang N, Lu G, Cai G, Xu D, Xu J, Li F, Zhang L. Feature Selection of Power Quality Disturbance Signals with an Entropy-Importance-Based Random Forest. Entropy. 2016; 18(2):44.Chicago/Turabian Style
Huang, Nantian; Lu, Guobo; Cai, Guowei; Xu, Dianguo; Xu, Jiafeng; Li, Fuqing; Zhang, Liying. 2016. "Feature Selection of Power Quality Disturbance Signals with an Entropy-Importance-Based Random Forest." Entropy 18, no. 2: 44.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.