A Method of Genetic Algorithm Optimized Extended Kalman Particle Filter for Nonlinear System State Estimation

2009 ◽  
Author(s):  
Shuying Yang ◽  
Wenjuan Huang ◽  
Qin Ma
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear System ◽  
Particle Filter ◽  
State Estimation ◽  
System State

scholarly journals Particle filter in power system state estimation – bad measurement data and branch disconnection

2015 ◽  
Vol 64 (2) ◽  
pp. 237-248
Author(s):  
Piotr Kozierski ◽  
Marcin Lis ◽  
Adam Owczarkowski ◽  
Dariusz Horla
Keyword(s):  
Power System ◽  
Particle Filter ◽  
State Estimation ◽  
Measurement Data ◽  
The State ◽  
Filter Method ◽  
System State ◽  
State Variables ◽  
Zero Bias ◽  
Power System State Estimation

Abstract An approach to power system state estimation using a particle filter has been proposed in the paper. Two problems have been taken into account during research, namely bad measurements data and a network structure modification with rapid changes of the state variables. For each case the modification of the algorithm has been proposed. It has also been observed that anti-zero bias modification has a very positive influence on the obtained results (few orders of magnitude, in comparison to the standard particle filter), and additional calculations are quite symbolic. In the second problem, used modification also improved estimation quality of the state variables. The obtained results have been compared to the extended Kalman filter method

scholarly journals Smart City Moving Target Tracking Algorithm Based on Quantum Genetic and Particle Filter

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Zhigang Liu ◽  
Jin Shang ◽  
Xufen Hua
Keyword(s):  
Genetic Algorithm ◽  
Particle Filter ◽  
State Estimation ◽  
Target Tracking ◽  
Time Constant ◽  
Smart City ◽  
Moving Target ◽  
Final Particle ◽  
Moving Target Tracking ◽  
Real State

In the application of moving target tracking in smart city, particle filter technology has the advantages of dealing with nonlinear and non-Gaussian problems, but when the standard particle filter uses resampling method to solve the degradation phenomenon, simply copying the particles will cause local optimization difficulties, resulting in unstable filtering accuracy. In this paper, a particle filter algorithm combined with quantum genetic algorithm (QGA) is proposed to solve the above problems. Aiming at the problem of particle exhaustion in particle filter, the algorithm adopts the method of combining evolutionary algorithm. Each particle in particle filter is regarded as a chromosome in genetic algorithm, and the fitness of each chromosome corresponds to the weight of particle. For each particle state with weight, the particle is first binary coded with qubit and quantum superposition state, and then quantum rotation gate is used for selection, crossing, mutation, and other operations, after a set number of iterations, the final particle set with accuracy and better diversity. In this paper, the filter state estimation and RMSF of N=50 and N=100 for nonlinear target tracking and the comparison of real state and state estimation trajectory in time-constant model under nonlinear target tracking are given. It can be seen that in nonlinear state, the quantum genetic and particle filter (QGPF) algorithm can achieve a higher accuracy of state estimation, and the filtering error of QGPF algorithm at each time is relatively uniform, which shows that the algorithm in this paper has better algorithm stability. Under the time-constant model, the algorithm fits the real state and realizes stable and accurate tracking.

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