Title :
Signal processing of power system load data based on stochastic hybrid-state jump-dynamic system theory
Author_Institution :
Dept. of Electr. Eng., California Univ., Los Angeles, CA, USA
Abstract :
The author considers the problems of filtering and smoothing for LG (linear Gaussian) hybrid-state regime-shift signal processing systems where a totally observed state process is generated by a linear stochastic differential equation whose parameters are functions of a random jump process called the regime. The objective is to compute the conditional probability distribution of the regime variable at each instant of time, given the data set relevant to filtering or smoothing. The pertinent continuous-time equations are given as well as results of a computer simulation for a power system application
Keywords :
filtering and prediction theory; power system control; stochastic systems; computer simulation; conditional probability distribution; continuous-time equations; filtering; linear stochastic differential equation; power system load data; random jump process; regime-shift; smoothing; stochastic hybrid-state jump-dynamic system theory; totally observed state process; Differential equations; Distributed computing; Filtering; Hybrid power systems; Nonlinear filters; Power systems; Signal generators; Signal processing; Smoothing methods; Stochastic systems;
Conference_Titel :
Circuits and Systems, 1991., IEEE International Sympoisum on
Print_ISBN :
0-7803-0050-5
DOI :
10.1109/ISCAS.1991.176529
