DocumentCode
1862538
Title
Research on Time-Sharing ZIP Load Modeling Based on Linear BP Network
Author
Kankan Wang ; Haixin Huang ; Chuanzhi Zang
Author_Institution
Shenyang Inst. of Autom., Shenyang, China
Volume
1
fYear
2013
fDate
26-27 Aug. 2013
Firstpage
37
Lastpage
41
Abstract
Classic load model is conservative and inaccurate by only considering constant power load model, but actual situation can be described more precisely by using ZIP load model. The different load rate Based on ZIP load model has a serious impact on static voltage stability. Due to the time-varying load rate, time-sharing modeling, which is identified for every 20 minutes in a day, can describe the variation of different load rate for the whole day more accurate than all-day modeling, which is identified once by using all data in a day. In this paper, all-day ZIP load model and time-sharing ZIP load model are established respectively in order to justify the effectiveness of time-sharing ZIP load modeling. By using Linear-BP network, the model parameters can be identified more easily and precisely. A case study is performed by using data from Dalian University of Technology. The results verify that this network is more suitable to ZIP model parameters identification.
Keywords
backpropagation; load flow; neural nets; parameter estimation; power engineering computing; Dalian University of Technology; ZIP model parameter identification; all-day ZIP load model; constant power load model; linear BP network; static voltage stability; time-sharing ZIP load modeling; time-varying load rate; Computational modeling; Load modeling; Mathematical model; Neurons; Power system dynamics; Reactive power; Linear-BP network; ZIP load model; all-day modeling; parameters identification; time-sharing modeling;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2013 5th International Conference on
Conference_Location
Hangzhou
Print_ISBN
978-0-7695-5011-4
Type
conf
DOI
10.1109/IHMSC.2013.16
Filename
6643828
Link To Document