Modern Heuristic Optimization Techniques With Applications To Power Systems
Modern Heuristic Optimization Techniques With Applications To Power Systems
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Introduction to Genetic Algorithms
Introduction to Genetic Algorithms
A non-dominated sorting particle swarm optimizer for multiobjective optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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Optimal location, number, and settings of unified power flow controllers (UPFC) using various multi-objective optimization algorithms is presented in this paper. The UPFC parameters, locations and number are computed to maximize the voltage stability margin and minimize the real power losses at the same time. For this, developed hierarchical optimization versions of three recent multi-objective algorithms are proposed namely: non-dominated genetic algorithms (NSGA-II), non-dominated sorting particle swarm optimization (NSPSO) and Strength Pareto Evolutionary Algorithm 2 (SPEA2). The fuzzy logic is proposed to extract the best compromise solution from the Pareto set. The proposed algorithms are applied to IEEE 30-bus power system. The line flow and load bus voltage limits are taken into account. The obtained results show that the installation of the UPFC in the power system minimizes the power losses, enhances the static voltage stability, and improves the voltage profiles. Furthermore, the proposed methods are able to solve a hard discrete---continuous constrained multi-objective optimization problem. In addition, they do not show any limitation on the number of objective functions under consideration.