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The Comprehensive Recognition Method of Critical Lines Based on the Principal Component Analysis
Hao Guangtao,Lin Zhenheng,Wu Xuanyi,Chen Xuejun 대한전기학회 2023 Journal of Electrical Engineering & Technology Vol.18 No.5
Accurate identification of a few key lines in power system is of great significance to prevent blackout. Traditional key lines are mainly identified by single index such as power grid structure, line load rate and active power of line, which is one-sided. Therefore, in the wide area measurement environment, a multi-dimensional comprehensive identification method of key lines based on principal component analysis is proposed. Firstly, from the aspect of transmission breadth and depth, the utilization index of line is proposed. Secondly, the grid structure, line load rate and line active power index are proposed. Thirdly, based on principal component analysis, a comprehensive identification method of the above four indicators is proposed. Finally, taking the actual power grid as an example, the accuracy of this method is verified by comparing with the traditional method.
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Optimal SVM learning method based on adaptive sparse sampling and granularity shift factor
Hui Wen,Dongshun Jia,Zhiqiang Liu,Hang Xu,Guangtao Hao 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.4
To improve the training efficiency and generalization performance of a support vector machine (SVM) in a large-scale set, an optimal SVM learning method based on adaptive sparse sampling and the granularity shift factor is presented. The proposed method combines sampling optimization with learner optimization. First, an adaptive sparse sampling method based on the potential function density clustering is designed to adaptively obtain sparse sampling samples, which can achieve a reduction in the training sample set and effectively approximate the spatial structure distribution of the original sample set. A granularity shift factor method is then constructed to optimize the SVM decision hyperplane, which fully considers the neighborhood information of each granularity region in the sparse sampling set. Experiments on an artificial dataset and three benchmark datasets show that the proposed method can achieve a relatively higher training efficiency, as well as ensure a good generalization performance of the learner. Finally, the effectiveness of the proposed method is verified.
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