Incorporation of unified power flow controller model for optimal placement using particle swam optimization technique

• Main Authors: Nireekshana, T., Rao, G. K., Raju, S. S. N.
• Format: Conference Proceeding
• Language: English
• Subjects: Genetic Algorithm; Genetic algorithms; Load flow; Newton-Raphson load Flow; Particle swam Optimization (PSO); Power system stability; Reactive power; Stability criteria; Unified Power Flow Controller (UPFC); Voltage control
• DOI: 10.1109/ICECTECH.2011.5941591

Flexible Alternating Current Transmission Systems (FACTS) devices have been proposed to be effective for controlling power flow and regulating bus voltage in electrical power systems, resulting in an increased transfer capability, low system losses and improved stability. Unified Power Flow Controller (UPFC) is one of the most promising FACTS devices for power flow control. In principle, the UPFC is capable of providing active and reactive power control, as well as adaptive voltage magnitude control. Provided no operating limits are violated, the UPFC regulates all three variables simultaneously or any combination of them. Moreover, since the UPFC parameters are computed after the load flow has converged, there is no way of knowing during the iterative process whether or not the UPFC parameters are within limits. This has provided the motivation for developing a new UPFC model suitable for incorporating into an existing Newton-Raphson load flow algorithm. It is also necessary to determine the optimal setting of the device so that the net saving is maximized. In this work a new mathematical model of UPFC is developed which can be easily incorporated in Newton-Raphson load flow algorithm. Optimal location of UPFC is determined based on Voltage Stability Index. Particle Swarm Optimization (PSO) technique is used to set the parameters UPFC. The objective function formulated consists of two terms: cost for energy loss and cost related to UPFC, which has to be maximized for net saving. The results obtained using PSO is compared with that of results obtained using genetic algorithm. The validity of the proposed work is tested on IEEE 5-Bus and IEEE 14-Bus systems using MATLAB.