Group decision support system for backbone-network reconfiguration

被引:19
|
作者
Liu, Yutian [1 ]
Sun, Pengbo [1 ]
Wang, Chunyi [2 ]
机构
[1] Shandong Univ, Minist Educ, Key Lab Power Syst Intelligent Dispatch & Control, 17923 Jingshi Rd, Jinan 250061, Peoples R China
[2] State Grid Shandong Elect Power Co, Jinan 250001, Peoples R China
基金
国家高技术研究发展计划(863计划); 中国国家自然科学基金;
关键词
Power system restoration; Multiple attribute decision-making; Group decision support system; Intelligent system; Data warehouse; SERVICE RESTORATION; EXPERT-SYSTEM; POWER; TOPSIS; GDSS;
D O I
10.1016/j.ijepes.2015.03.006
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Modem power system is unprecedentedly vulnerable and the society is exposed to higher blackout risks. Therefore restoration is a key issue to power systems. Reasonable backbone-network reconfiguration is necessary for re-establishing the network and restoring loads quickly. In order to speed up network reconfiguration under security constraints, a group decision support system (GDSS) is developed. A unified data platform based on data warehouse is constructed to resolve inconsistencies of data, information and models. A three-stage restoration strategy and a two-layer restoration path searching method are proposed to establish restoration schemes. Multiple attribute decision-making is implemented to evaluate alternatives and consider various attributes about restoration speed and system security comprehensively. Group decision-making provides an aggregated cardinal ranking of candidate restoration schemes. The GDSS uses expert rules for qualitative analysis and numerical computation programs for quantitative analysis, and it can resolve the semi-structured decision problem of network reconfiguration efficiently. Restoration plan of Shandong power grid is developed with the proposed method and it has been adopted by State Grid Shandong Electric Power Company. Performance results of Shandong power grid of China demonstrate the feasibility and flexibility of the method. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:391 / 402
页数:12
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