Application of a revised Boltzmann machine to topological observability analysis

Author

Mori, Hiroyuki

Author_Institution

Dept. of Electr. Eng., Meiji Univ., Kawasaki, Japan

fYear

1991

fDate

23-26 Jul 1991

Firstpage

283

Lastpage

287

Abstract

The author presents a method for determining power system topological observability with a stochastic neural network. The proposed method is based on the Boltzmann machine that can cope with stochastic behavior of neurons. The Boltzmann machine is useful for solving combinatorial problems since it can avoid local minima. In this paper, a revised Boltzmann machine is proposed to improve the convergence characteristics. A squashing function is utilized to decrease the number of neurons in handling the inequality constraints of the topological observability problem

Keywords

combinatorial mathematics; convergence of numerical methods; neural nets; power system analysis computing; state estimation; stochastic processes; Boltzmann machine; combinatorial problems; convergence; inequality constraints; neurons; power system analysis computing; squashing function; state estimation; stochastic neural network; topological observability; Convergence; Fasteners; Neural networks; Neurons; Observability; Power engineering and energy; Power system analysis computing; State estimation; Stochastic processes; Stochastic systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks to Power Systems, 1991., Proceedings of the First International Forum on Applications of

Conference_Location

Seattle, WA

Print_ISBN

0-7803-0065-3

Type

conf

DOI

10.1109/ANN.1991.213462

Filename

213462