A practical approach for power system state estimation based on the hybrid Particle Swarm Optimization algorithm

2016 ◽  
pp. 421-425
Author(s):  
Ujjal Sur ◽  
Gautam Sarkar
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
State Estimation ◽  
Optimization Algorithm ◽  
Practical Approach ◽  
System State ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Power System State Estimation ◽  
Hybrid Particle Swarm Optimization

Particle swarm optimization for power system state estimation

2015 ◽  
Vol 148 ◽  
pp. 175-180 ◽  
Author(s):  
D.H. Tungadio ◽  
B.P. Numbi ◽  
M.W. Siti ◽  
A.A. Jimoh
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
State Estimation ◽  
Particle Swarm ◽  
System State ◽  
Swarm Optimization ◽  
Power System State Estimation

scholarly journals Artificial fish swarm optimization algorithm for power system state estimation

2020 ◽  
Vol 18 (3) ◽  
pp. 1130
Author(s):  
Surender Reddy Salkuti
Keyword(s):  
Power System ◽  
State Estimation ◽  
Optimization Algorithm ◽  
Search Algorithm ◽  
Test System ◽  
System State ◽  
State Variables ◽  
Swarm Optimization ◽  
Power System State Estimation ◽  
Artificial Fish Swarm

In this paper, the power system state estimation (SE) problem is formulated as a general non-linear programming problem with equality constraints and boundary limits on the state variables. The proposed SE problem is solved using an evolutionary based Artificial Fish Swarm Optimization Algorithm (AFSOA). The AFSOA is a global search algorithm based on the characteristics of fish swarm and its autonomous model. The detailed algorithm with its flow chart is presented in this paper. To show the effectiveness of the proposed SE approach, six bus test system is considered. The obtained results are compared with other algorithms reported in the literature.

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