Evolutionary computation based optimal placement of phasor measurement unit for observability and islanding issues and stability studies for smart grid

Abstract

The optimal placement of Phasor Measurement Unit PMU for state estimation using evolutionary computation method is implemented This model presents an optimal placement of PMU model considering measurement redundancy using evolutionary algorithms such as Real Coded Genetic Algorithm RCGA Non Dominated Sorting Differential Evolutionary algorithms NSDE Non Dominated Sorting Genetic Algorithms NSGA Heterogeneous Comprehensive Learning Particle Swarm Optimization HCLPSO for state estimation This model ensures complete observability of power system for normal conditions as well as contingencies considering zero injection measurements The optimum solution is obtained by considering objectives such as minimum number of PMU and maximum redundancy The New England 39 IEEE 57 IEEE118 and practical networks of Indian grid such as western regional Indian power grid and northern regional Indian power grid are considered for the optimal placement of PMU Controlled islanding can be used as an effective emergency action to split a large scale power system into islands for avoiding blackouts An optimal PMU placement model was developed considering power system controlled islanding So that the power network remains observable under controlled islanding as well as norrmal operating condition A new method based on spectral clustering is proposed to handle multiple constraints in order to guarantee the stability of generated islands The objective function used in this controlled islanding algorithm is the minimal power flow disruption Constraints such as generator coherency load generation imbalance are considered for spectral clustering Optimal placement of PMU OPPMU is formed for the obtained islands using two conflicting objectives for minimizing the number of PMU and maximizing the measurement redundancy using conventional Mixed Integer Linear Programming MILP Real coded Genetic Algorithm RGA Non dominated Sorting Differential Evolutionary algorithm NSDE Real coded genetic algorithm with Simulated Binary crossover SBX and non domin