期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 15, 期 5, 页码 2915-2926出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2018.2871253
关键词
Classification; discriminative projection; extreme learning machine (ELM); multilabel; multiple power quality disturbance (MPQD); variational mode decomposition (VMD)
类别
资金
- National Natural Science Foundation of China [51277080, 51707069]
- State Key Laboratory of Synthetical Automation for Process Industries [PAL-N201806]
- Major Science and Technology Foundation of Guangdong Province [2015B010104002]
- Youth Scholars Educational Commission of Fujian Province of China [JT180147]
Power quality events are usually associated with more than one disturbance and their recognition is typically based on multilabel learning. In this study, we propose a new method for recognizing multiple power quality disturbances (MPQDs) based on variational mode decomposition (VMD) and a random discriminative projection extreme learning machine for multilabel learning (RDPEML). First, VMD is employed to decompose the MPQDs into several intrinsic mode functions and the standard energy differences of each mode are extracted as features that form the input vectors of the classifier. Second, a novel multilabel classifier called RDPEML is constructed by combining a random discriminative projection multiclass extreme learning machine (ELM) and a thresholding learning method-based kernel ELM. In order to obtain better classification performance, a tenfold cross-validation embedded particle swarm optimization approach is utilized to search for the optimal values of the structural parameters. Finally, a test study was conducted using MATLAB synthetic signals and real signals sampled from a three-phase standard source under different noise conditions. Compared with the several recent state-of-the-art multilabel learning algorithms, RDPEML achieved better classification performance with superior computational speed.
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