Modeling of flexible AC transmission system devices and fuzzy controller for automatic generation control of electric vehicle-injected power system

Abstract

The automatic generation control (AGC) is a paradigm topic of interest that is continuously evolving for adequate power generation control in the power system. In this proposed study, a fuzzy adapted PID (FPID) controller is assigned for the AGC of the hybrid power system in various electrical risks and hazards. The hybrid power system includes traditional thermal and hydroelectric facilities along with gas and nuclear power plants. Two FACTS components, the superconducting magnetic energy storage (SMES) and the interline power flow controller (IPFC), have been included in to the system to improve stability of the system. The proposed FPID controller is developed to its full potential using the newly developed teaching learning-based optimization (TLBO) method. The TLBO technique's capability has been compared against a few common evolutionary strategies in order to demonstrate its superiority. It also shows how FPID controllers outperform traditional PID controllers. Investigations are also done on how IPFC and SMES might improve system stability performance. Owing to the outcome study, it is revealed that suggested TLBO-FPID, IPFC and SMES are most effective tool to advance the AGC mechanism. It is revealed from the outcomes that anticipated FPID controller reduces the settling time of area1 frequency (ΔF1) by 135.72 %, 428.58 % and 471.42 % over TLBO;PID, DE;PID and PSO;PI respectively. Finally, the proposed mechanisms are discussed with employing in electric vehicle integrated three-area distributed energy source-based power network to justify the efficacy of the proposed approaches.

Keywords

• Fuzzy controller
• Automatic generation control (AGC)
• Interline power flow controller
• Superconducting magnetic energy storage(SMES)
• Teaching Learning Based Optimization algorithm (TLBO)
• Objective function