Design of Artifi cial Inteligence-Based Load Frequency Controller for a Two Area Power System with Super Conducting Magnetic Energy Storage Device

Superconducting Magnetic Energy Storage (SMES) unit with a self-commutated converter is capable of controlling both active and reactive power simultaneously and quickly; increasing attention has been focused recently on power system stabilization by SMES control. In this study, a self-tuning control scheme for SMES is proposed and applied to Automatic Generation Control (AGC) in power system. The system is assumed to be consisting of two areas. The proposed self-tuning control scheme is used to implement the AGC for Load Frequency Control (LFC) application adding to conventional control confi guration. The effects of the self tuning confi guration with Artifi cial Neural Network (ANN) in AGC on SMES control for the improvement of LFC is compared with that of Conventional Integral controller, PI controller, Fuzzy Proportional Integral Controller (FPIC). The effectiveness of the SMES control technique is investigated when Area Control Error (ACE) is used as the control input to SMES. The computer simulation of the two-area interconnected power system shows that the self-tuning ANN control scheme of AGC is very effective in damping out of the oscillations caused by load disturbances in one or both of the areas and it is also seen that the ANN-controlled SMES performs primary frequency control more effectively compared to Integral controller, PI and FPIC controlled SMES in AGC control.