Optimal Placement of FACTS Devices Based on Whale Optimization Algorithm for Power System Security Enhancement

Abstract

The present-day power system is vulnerable to instability and security threats due to the continuously changing load pattern. To enhance the security of the power system and to avoid the electrical power system from collapsing, the condition of the system security has to be inspected by security analysis tools and it can be enhanced by the proper integration of FACTS devices into the network. This paper presents a methodology in which the security of the system can be analyzed with the help of an index called Line Overload Severity Index (LOSI). Unified Power Flow Controller (UPFC) is preferred to improve the security of the power system. Owing to the cost involved in placing UPFCs it is obligatory to use minimum number of devices, by optimally placing them in the network. It is obligatory to recognize an ideal location to install UPFC. Considering the Line overload Sensitivity Index, the optimal location identification for UPFC is done. The paper also presents the formulation of a new severity function using transmission line loadings. The severity function combines the objectives of reducing transmission line loadings and improvement of voltage profile during multi line contingencies. In the event of multi-line contingencies, the objective function for reducing the fuel cost and the severity function are analyzed. Optimal power flow method is followed to analyze the security of the electrical power system during contingency situations. This optimal location identification procedure and the OPF are solved using a metaheuristic technique, Whale Optimization Algorithm (WOA). The whole methodology that is proposed is experimented on a standard IEEE-30 bus test system.

Optimal location of multiple FACTS devices in N-1 contingency conditions using traditional genetic algorithm

“…are compared among aforementioned FACTS devices [3]. On the IEEE 14 bus system, a new technique employing improved teaching learning-based optimization (ITLBO) and weight improvement of particle swarm optimization WIPSO has been proposed and implemented for Optimal power flow during the N-1 contingency condition [4]. Biogeography based optimization (BBO) [5] is used to alleviate power system problems, including overloading and voltage limit violations, by placing the UPFC and interline power flow controller (IPFC).…”