Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
On self-adaptive features in real-parameter evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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Voltage stability has become an important issue in planning and operation of many power systems. Contingencies such as unexpected line outages in a stressed system may often result in voltage instability, which may lead to voltage collapse. This paper presents evolutionary algorithm techniques like Genetic Algorithm (GA) and Non-dominated Sorting Genetic Algorithm II (NSGA II) approach for solving the Voltage Stability Constrained Optimal Power Flow (VSC-OPF). Base-case generator power output, voltage magnitude of generator buses are taken as the control variables. Maximum L-index of load buses is used to specify the voltage stability level of the system. An improved GA which permits the control variables to be represented in their natural form is proposed to solve the Optimal Power Flow (OPF) problem and NSGA II is proposed to solve the VSC-OPF optimization problem. For effective genetic operation, crossover and mutation operators which can directly operate on floating point numbers and integers are used. The proposed approach has been evaluated on the IEEE 30-bus test system. Simulation results show the effectiveness of the proposed NSGA II approach than Multi-Objective Genetic Algorithm (MOGA) for improving the voltage security of the system.