Title :
Hyper-Chaotic Neural Network Based on Newton Iterative Method and Its Application in Solving Load Flow Equations of Power System
Author :
Cai, Mingshan ; Chen, Rixin ; Kong, Lingshuang
Author_Institution :
Dept. of Electr. & Inf. Eng., Hunan Univ. of Arts & Sci., Changde, China
Abstract :
Most of load flow equations of power system are multi-variable nonlinear equations set which need to know the initial value for solving, and the initial value is very difficult to choose. Neural network is a kind of highly complex nonlinear dynamic system and chaotic phenomenon is found in it. By utilizing the simulated annealing mechanism to eliminate transiently chaotic neuron, this paper presents a kind of chaotic neuron which can permanently sustain chaotic search. The topology of chaotic neural network composed of four chaotic neurons in which hyper-chaos exists is studied. For the first time, a novel method to find all solutions of nonlinear equations is proposed in which initial points are generated by hyper-chaotic neural network. The numerical example shows that the new method proposed in this paper is correct and effective, and it lays a good engineering foundation for finding all the solutions of load flow equations of power system.
Keywords :
Newton method; load flow; neural nets; nonlinear equations; power engineering computing; set theory; simulated annealing; Newton iterative method; hyper-chaotic neural network; load flow equations; multivariable nonlinear equation set; nonlinear dynamic system; power system; simulated annealing mechanism; Chaos; Iterative methods; Load flow; Neural networks; Neurons; Nonlinear dynamical systems; Nonlinear equations; Power system dynamics; Power system simulation; Power systems; Hyper-Chaotic neural network; Load flow equations; Non-linear equations set; Power system;
Conference_Titel :
Measuring Technology and Mechatronics Automation, 2009. ICMTMA '09. International Conference on
Conference_Location :
Zhangjiajie, Hunan
Print_ISBN :
978-0-7695-3583-8
DOI :
10.1109/ICMTMA.2009.531