Abstract—In this paper, the third Version of Non-dominated Sorting Genetic Algorithm (NSGA III) inspired by nature is presented and used for the problem of optimal power flow (OPF) in power systems with a unified power flow controller (UPFC). The total cost of production, emission and active loss in a power system with the UPFC that sets the load bus voltage and controls the power transits across the transmission line are minimized and validated optimally with the use of NSGA III.
The NSGA III algorithm is an extension of NSGA I and II that is based on natural selection, it is also recently proposed multi-objective optimization (MOO) algorithms. The performances of NSGA III have been tested and verified on the IEEE 30-bus power system by comparing them to several other methods multi objective particle swarm optimization (MOPSO) and Strength Pareto Evolutionary Algorithm (SPEA II). In addition, NSGA III are used not only to optimize contradictory objectives such as total production cost, emission and active power losses, but also to improve the voltage profile of the power system. Our results illustrate that NSGA III can be used successfully to solve non-linear power system problems in the presence of UPFC, the most powerful and dynamic device of the third generation of the FACTS family.
Index Terms—Front de Pareto, multi-objective optimisation, NSGA III, UPFC.
The authors are with the Electromechanical Systems Research Laboratory (LSELM), Faculty of Engineering Science, Badji Mokhtar University - Annaba, Algeria (e-mail: ahmed.merah@gmail.com, mohadjabi@yahoo.com).
[PDF]
Cite: Ahmed Merah and Mohamed Adjabi, "Multi-objective Optimization in Power Systems Including UPFC Controllerwith NSGA III," International Journal of Modeling and Optimization vol. 8, no. 6, pp. 318-325, 2018.
