Optimal Measurement Placement for Power System State Estimation Using Hybrid Genetic Algorithm and Simulated Annealing

Abstract

This paper proposes a hybrid genetic algorithm and simulated annealing (GA/SA) for solving optimal measurement placement for power system state estimation. Even though the global minimum measurement pair number is (N- 1) , their positions are required to make the system observable. GA/SA algorithm is based on genetic algorithm (GA) process. The acceptance criterion of simulated annealing (SA) is used for chromosome selection. Single measurement pair loss contingency is also considered. The Pthetas observable concept is used to check the network observability. Test results of 10-bus, IEEE 14, 30, 57 and 118-bus systems indicate that GA/SA is superior to tabu search (TS), GA and SA in terms of higher frequency of the best hit and faster computational time.