Title :
Reactive power optimization based on genetic algorithm
Author :
Zhang, Haibo ; Zhang, Lizi ; Meng, Fanling
Author_Institution :
Dept. of Electr. Eng., Texas A&M Univ., College Station, TX, USA
Abstract :
This paper presents an improved genetic algorithm (GA) for optimizing power system reactive power. Based on a basic GA, an integer/float mixed coding GA is proposed in this paper. The objective function of the proposed method is to minimize the system active power loss. The control variables are generator bus voltages, transformer tap positions and switchable shunt capacitor banks. The refined GA overcomes the drawbacks of conventional reactive power optimization methods. The proposed method has been applied to the practical ward-Hale 6 bus system and IEEE 30-bus system. The test results show that this method is a very feasible and practical method
Keywords :
genetic algorithms; power systems; reactive power; IEEE 30-bus system; active power loss minimisation; artificial intelligence; control variables; generator bus voltages; genetic algorithm; integer/float mixed coding genetic algorithm; power system; reactive power optimization; switchable shunt capacitor banks; transformer tap positions; ward-Hale 6 bus system; Artificial intelligence; Capacitors; Genetic algorithms; Integer linear programming; Optimization methods; Power system stability; Power systems; Reactive power; System testing; Voltage control;
Conference_Titel :
Power System Technology, 1998. Proceedings. POWERCON '98. 1998 International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-4754-4
DOI :
10.1109/ICPST.1998.729327