Abstract
This paper presents an effective method of network overload management in
power systems. The three competing objectives 1) generation cost 2)
transmission line overload and 3) real power loss are optimized to provide
pareto-optimal solutions. A fuzzy ranking based non-dominated sorting
genetic algorithm-II (NSGA-II) is used to solve this complex nonlinear
optimization problem. The minimization of competing objectives is done by
generation rescheduling. Fuzzy ranking method is employed to extract the
best compromise solution out of the available non-dominated solutions
depending upon its highest rank. N-1 contingency analysis is carried out
to identify the most severe lines and those lines are selected for outage.
The effectiveness of the proposed approach is demonstrated for different
contingency cases in IEEE 30 and IEEE 118 bus systems with smooth cost
functions and their results are compared with other single objective
evolutionary algorithms like Particle swarm optimization (PSO) and
Differential evolution (DE). Simulation results show the effectiveness of
the proposed approach to generate well distributed pareto-optimal
non-dominated solutions of multi-objective problem
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