Optimized Complex Power Quality Classifier Using One vs. Rest Support Vector Machines
Optimized Complex Power Quality Classifier Using One vs. Rest Support Vector Machines作者机构：Universidad Nacional de Río Cuarto Río Cuarto Argentina Nexant INC Chandler AZ USA
出 版 物：《Energy and Power Engineering》 (能源与动力工程（英文）)
年 卷 期：2017年第9卷第10期
页 面：568-587页
学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学]
主 题：Complex Power Quality Optimal Feature Selection One vs. Rest Support Vector Machine Learning Algorithms Wavelet Transform Pattern Recognition
摘 要：Nowadays, power quality issues are becoming a significant research topic because of the increasing inclusion of very sensitive devices and considerable renewable energy sources. In general, most of the previous power quality classification techniques focused on single power quality events and did not include an optimal feature selection process. This paper presents a classification system that employs Wavelet Transform and the RMS profile to extract the main features of the measured waveforms containing either single or complex disturbances. A data mining process is designed to select the optimal set of features that better describes each disturbance present in the waveform. Support Vector Machine binary classifiers organized in a “One Vs Rest architecture are individually optimized to classify single and complex disturbances. The parameters that rule the performance of each binary classifier are also individually adjusted using a grid search algorithm that helps them achieve optimal performance. This specialized process significantly improves the total classification accuracy. Several single and complex disturbances were simulated in order to train and test the algorithm. The results show that the classifier is capable of identifying 99% of single disturbances and 97% of complex disturbances.
维普期刊数据库