Contingency Screening of Power System Based on Rough Sets and Fuzzy ARTMAP

Author(s):  
Youping Fan ◽  
Yunping Chen ◽  
Wansheng Sun ◽  
Dong Liu ◽  
Yi Chai
2018 ◽  
Vol 33 (1) ◽  
pp. 1080-1081 ◽  
Author(s):  
Teodora Dimitrovska ◽  
Urban Rudez ◽  
Rafael Mihalic

2017 ◽  
Vol 45 (8) ◽  
pp. 852-863 ◽  
Author(s):  
Tilman Weckesser ◽  
Hjörtur Jóhannsson ◽  
Mevludin Glavic ◽  
Jacob Østergaard

2011 ◽  
Vol 60 (2) ◽  
pp. 394-403 ◽  
Author(s):  
Claudio M. Rocco ◽  
Jose Emmanuel Ramirez-Marquez ◽  
Daniel E. Salazar ◽  
Cesar Yajure

2014 ◽  
Vol 672-674 ◽  
pp. 1405-1408
Author(s):  
Hong Zhang ◽  
Zhi Guo Lei ◽  
Yue Cheng ◽  
Yu Ming Wang

Power load forecasting is one of the important parts in power system planning. This paper focused on the research of load forecasting and proposed an improved Rough set for mid-long term load forecasting. Based on the theory, the most sensitive factor can be found, which is applied to studying the uncertain problems of mid-long term load forecasting in this paper and one method fitting for mid-long term load forecasting are proposed. The validity and effectiveness of the method is tested in a real power system example.


2011 ◽  
Vol 383-390 ◽  
pp. 5023-5027
Author(s):  
Zhi Xian Pi ◽  
Ru Zhi Xu ◽  
Jian Guo

Short-term load forecasting in power system is an important daily work in Dispatch Operation Department of Power System. The level of forecasting accuracy directly affects the operating economy and supply quality of power system.This paper adopts the rough sets to forecast short-term load. It designs an overall structure of forecasting the short-term load based on the rough sets, applies the rough sets to analyze the importance of attribute of each condition on decision-making attribute and then gets a reduced forecasting system, lists examples to forecast short-term load on the basis of the real historical data, and compare the results with the traditional decision-making tree algorithm. The results of this study prove that the rough sets is much practical in short-term load forecasting.


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