지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국정보처리학회 JIPS(Journal of Information Processing Systems) JIPS(Journal of Information Processing Systems) 제15권 제1호
- 발행연도
- 2019.1
- 수록면
- 116 - 126 (11page)
이용수
초록· 키워드
오류제보하기
In this paper, an improved one-against-one support vector machine algorithm is used to classify multiple powerquality disturbances. To solve the problem of parameter selection, an improved particle swarm optimizationalgorithm is proposed to optimize the parameters of the support vector machine. By proposing a new inertiaweight expression, the particle swarm optimization algorithm can effectively conduct a global search at theoutset and effectively search locally later in a study, which improves the overall classification accuracy. Theexperimental results show that the improved particle swarm optimization method is more accurate than a gridsearch algorithm optimization and other improved particle swarm optimizations with regard to its classificationof multiple power quality disturbances. Furthermore, the number of support vectors is reduced.
목차
등록된 정보가 없습니다.
참고문헌 (21)
참고문헌 신청
W. G. Morsi / 2011 / Power quality evaluation in smart grids considering modern distortion in electric power systems / Electric Power Systems Research 81(5):1117~1123
Z. G. Liu / 2013 / Review of power quality mixed disturbances identification / Power System Protection and Control 41(13):146~153
M. Valtierra-Rodriguez / 2013 / Detection and classification of single and combined power quality disturbances using neural networks / IEEE Transactions on Industrial Electronics 61(5):2473~2482
M. Biswal / 2013 / Detection and characterization of multiple power quality disturbances with a fast S-transform and decision tree based classifier / Digital Signal Processing 23(4):1071~1083
D. De Yong / 2015 / An effective power quality classifier using wavelet transform and support vector machines / Expert Systems with Applications 42(15-16):6075~6081
Z. G. Liu / 2013 / Review of power quality mixed disturbances identification / Power System Protection and Control 41(13):146~153
M. Valtierra-Rodriguez / 2013 / Detection and classification of single and combined power quality disturbances using neural networks / IEEE Transactions on Industrial Electronics 61(5):2473~2482
M. Biswal / 2013 / Detection and characterization of multiple power quality disturbances with a fast S-transform and decision tree based classifier / Digital Signal Processing 23(4):1071~1083
D. De Yong / 2015 / An effective power quality classifier using wavelet transform and support vector machines / Expert Systems with Applications 42(15-16):6075~6081
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