期刊
IEEE TRANSACTIONS ON POWER DELIVERY
卷 26, 期 4, 页码 2342-2351出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRD.2011.2149547
关键词
Feature selection; power-quality disturbance (PQD); probabilistic neural network (PNN); S-transform; TT-transform
资金
- National Science Council of the R.O.C. [NSC 99-2632-E-033-001-MY3]
This paper proposes an optimal feature selection approach, namely, probabilistic neural network-based feature selection (PFS), for power-quality disturbances classification. The PFS combines a global optimization algorithm with an adaptive probabilistic neural network (APNN) to gradually remove redundant and irrelevant features in noisy environments. To validate the practicability of the features selected by the proposed PFS approach, we employed three common classifiers: multilayer perceptron, k-nearest neighbor and APNN. The results indicate that this PFS approach is capable of efficiently eliminating nonessential features to improve the performance of classifiers, even in environments with noise interference.
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