Optimization of static inter-phase controller using joint particle swarm and neural network algorithm for damping power system fluctuations
Arash Khashayar, Pouya Derakhshan-Barjoei
With the advent of new developments in the field of power electronic circuits technology, the use of power stabilizer has been commonplace in controlling the power system fluctuations to eliminate fluctuations and stabilize system stability. The primary use of these devices is to increase the capability of systems to transmit power. In-phase controller (IPC) is one of the Flexible Alternating Current Transmission System (FACTS) devices used to transfer power between individual lines. IPC, although potentially, capable of independent control of active and reactive power or transmission lines, has not been able to emerge as a result of phase-shifting transmissions (PSTs). The major problem with IPC is the ability to control active and reactive power, while its control range is also limited. This limitation comes from the fact that the IPC uses PST as phase shifters. Accordingly, a rational solution to address the major disadvantages of the IPC could be to find better alternatives for PST. In this paper, the Voltage Switch Convertor (VSC) option is proposed as an appropriate candidate, and a new structure based on Static IPC (SIPC), considering Pourhossein et al., and Mondal et al., has been proposed. In the simulation, a controller based on the neural network trained by the PSO algorithm for the nonlinear system. Simulation results showed the proper functioning of the PSO-based Neural Network (PSONN) controller in our proposed tuning model, reducing the velocity fluctuations against disturbances.