DocumentCode :
2428576
Title :
Study on high capability intelligent algotrithm in load flow of power system
Author :
Li-xin, Ma ; Xue-jia, Zhang
Author_Institution :
Sch. of Comput. & Electr. Eng., Univ. of Shanghai for Sci. & Technol., Shanghai
fYear :
2008
fDate :
7-11 June 2008
Firstpage :
78
Lastpage :
82
Abstract :
Power flow calculation is the most important and basic calculation in power system. This calculation is not only a very important calculation to seize power system planning and operation of the system state, but also a very important calculation for the analytical systems such as the stability index. The algorithm is usually Newton-Larfson , the P-Q algorithm, etc. However, the traditional methods of power system, with unknown parameters expended in Exponential increase becomes complex and make the Power system more difficult to create a mathematical model and solution. In this paper, a kind of traditional BP neural network algorithm with the parallel computing function was firstly recommended for the simulation of the flow calculation ;and then another kind of high-order BP neural network was recommended for the simulation, and the result shows that the algorithm can effectively resolve the difficulties of traditional modeling algorithm, and high calculating cost, and the poor in real-time.
Keywords :
backpropagation; load flow; neural nets; power system management; power system planning; power system simulation; power system stability; BP neural network algorithm; Newton-Larfson; P-Q algorithm; high capability intelligent algorithm; load flow; parallel computing function; power flow calculation; power system operation; power system planning; stability index; Computational modeling; Load flow; Neural networks; Power system analysis computing; Power system modeling; Power system planning; Power system simulation; Power system stability; Power systems; Stability analysis; Electric Power System; High order neural network; Load flow computation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks and Signal Processing, 2008 International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4244-2310-1
Electronic_ISBN :
978-1-4244-2311-8
Type :
conf
DOI :
10.1109/ICNNSP.2008.4590313
Filename :
4590313
Link To Document :
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