Multi-objective GA with fuzzy decision making for security enhancement in power system

• Published in: Applied soft computing 2012-09, Vol.12 (9), p.2756-2764
• Main Authors: Banu, R. Narmatha, Devaraj, D.
• Format: Article
• Language: English
• Subjects: Flexible AC transmission system (FACTS) devices; Genetic algorithm; Pareto optimal frontier; Power system security; Thyristor Controlled Series Capacitors (TCSC)
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2012.03.057

[Display omitted] ► Power system security enhancement is a major concern in the operation of power system. ► In this paper, the task of security enhancement is formulated as a multi-objective optimization problem. ► Multi-objective genetic algorithm is applied to solve this security optimization problem. ► A fuzzy set theory-based approach is employed to obtain the best compromise solution over the trade-off curve. ► The excellent robustness and efficiency of the proposed method suggests to apply it in real life conditions on very large scale problems. Power system security enhancement is a major concern in the operation of power system. In this paper, the task of security enhancement is formulated as a multi-objective optimization problem with minimization of fuel cost and minimization of FACTS device investment cost as objectives. Generator active power, generator bus voltage magnitude and the reactance of Thyristor Controlled Series Capacitors (TCSC) are taken as the decision variables. The probable locations of TCSC are pre-selected based on the values of Line Overload Sensitivity Index (LOSI) calculated for each branch in the system. Multi-objective genetic algorithm (MOGA) is applied to solve this security optimization problem. In the proposed GA, the decision variables are represented as floating point numbers in the GA population. The MOGA emphasize non-dominated solutions and simultaneously maintains diversity in the non-dominated solutions. A fuzzy set theory-based approach is employed to obtain the best compromise solution over the trade-off curve. The proposed approach has been evaluated on the IEEE 30-bus and IEEE 118-bus test systems. Simulation results show the effectiveness of the proposed approach for solving the multi-objective security enhancement problem.