Black-start decision making based on collaborative filtering for power system restoration. (September 2018)

• Record Type: Journal Article
• Authors: Leng, Yajun; Lu, Qing; Liang, Changyong
• Is Part Of: International journal of electrical power & energy systems. Volume 100(2018)
• Page Start: 279
• Page End: 286
• Publication Date: 2018-09
• Subjects: Power system restoration -- Black-start -- Multiple attribute decision making -- Collaborative filtering; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2018.02.044
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: Collaborative filtering is used to determine the weights of indexes. A novel black-start decision making method based on collaborative filtering is proposed. The proposed black-start decision making method performs better than some popular methods. Abstract: Black-start decision making plays an important role in power system restoration after a large-area blackout. The entropy method is one of the most popular methods for determining the weights of indexes in black-start decision making. In spite of its success, the entropy method is affected significantly by normalization methods, which makes the final evaluation results of black-start schemes not reliable. To address this issue, in this article, we propose a novel black-start decision making method based on collaborative filtering. In the proposed method, the values in the decision matrix are withheld, and the collaborative filtering technique is adopted to predict the withheld values. Based on the prediction values and true values, the MAE weights of all the indexes are obtained. Finally, the MAE weights are used to compute the overall assessment value of each black-start scheme. Based on the data of Guangdong power system of China, experiments were carried out to evaluate the performance of the proposed method. Experimental results show that the proposed method is more stable and superior to the existing methods.