A computational intelligence-based technique for the installation of multi-type FACTS devices

As power demand rises, the power system becomes more stressed, potentially leading to an increase in power losses. When compared to lower power losses, higher power losses result in higher power system operating cost. Flexible AC Transmission System (FACTS) devices help to reduce power losses. This paper describes the use of a computational intelligence-based technique, in this case the Artificial Immune System (AIS), to solve the installation of Thyristor Controlled Static Compensator (TCSC) and Static VAR Compensator (SVC) in a power system while ensuring optimal sizing of both devices. The goal of determining the best locations and sizes for the multi-type FACTS devices is to minimize system power loss. Three case studies are presented to investigate the effectiveness of the proposed AIS optimization technique in solving the multi-type FACTS device installation problem under various power system conditions. The optimization results generated by the proposed AIS are beneficial in improving the power system, particularly in terms of system power loss minimization, which also contributes to power system operating cost minimization. As a result, the likelihood of this being sustainable and able to be implemented for an extended period is greater.

Keywords:

FACTS devices , Computational intelligence , Artificial immune system , Loss minimization and multi-type