Power Quality Disturbances Feature Selection and Recognition Using Optimal Multi-Resolution Fast S-Transform and CART Algorithm

Nantian Huang, Hua Peng, Guowei Cai and Jikai Chen
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Nantian Huang: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
Hua Peng: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
Guowei Cai: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
Jikai Chen: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China

Energies, 2016, vol. 9, issue 11, 1-21

Abstract: In order to improve the recognition accuracy and efficiency of power quality disturbances (PQD) in microgrids, a novel PQD feature selection and recognition method based on optimal multi-resolution fast S-transform (OMFST) and classification and regression tree (CART) algorithm is proposed. Firstly, OMFST is carried out according to the frequency domain characteristic of disturbance signal, and 67 features are extracted by time-frequency analysis to construct the original feature set. Subsequently, the optimal feature subset is determined by Gini importance and sorted according to an embedded feature selection method based on the Gini index. Finally, one standard error rule subtree evaluation methods were applied for cost complexity pruning. After pruning, the optimal decision tree (ODT) is obtained for PQD classification. The experiments show that the new method can effectively improve the classification efficiency and accuracy with feature selection step. Simultaneously, the ODT can be constructed automatically according to the ability of feature classification. In different noise environments, the classification accuracy of the new method is higher than the method based on probabilistic neural network, extreme learning machine, and support vector machine.

Keywords: power quality disturbances; optimal multi-resolution fast S-transform; classification and regression tree algorithm; feature selection; decision tree (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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