Open AccessOptimal Placement of Statcom for Iraqi National (400kV) Super High Voltage Grid System Genetic Algorithm Based Approach
Author(s) -
Hassan Ali Abdulmajeed Salbi,
Firas M. Tuaimah
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1076/1/012009
Subject(s) - voltage , genetic algorithm , ac power , electric power system , computer science , matlab , flexible ac transmission system , grid , power (physics) , high voltage , computation , control theory (sociology) , engineering , electrical engineering , power flow , algorithm , control (management) , mathematics , physics , geometry , quantum mechanics , machine learning , artificial intelligence , operating system
The Static Synchronous Compensator (STATCOM) is one of the most currently used Flexible Alternating Current Transmission System (FACTS) devices utilized to control the voltage and power flow. This work proposed an application of the STATCOM devices to maintain the voltages within the specified limits and enhancing voltage deviation, as well as reducing the apparent power losses. The STATCOM device has been chosen using the minimum possible size of the reactive power injected or absorbed by the STATCOM devices while satisfying the stability limits. A Genetic Algorithm (GA) is one of the Evolutionary Computation Techniques used to optimally determine the best locations and sizes of the STATCOM devices to be installed. The proposed approach has been examined and tested first on a 14-bus IEEE test system; the results obtained encouraged us to implement the same approach on the 27-bus Iraqi National (400kV) Super High Voltage Grid (INSHVG) System (part of the Iraqi power network), with different power system load conditions for all season in the year such as summer, winter, spring, and autumn to find the impact of the best location and size of each STATCOM device. A computer program, written in MatLab environment, was developed to represent the proposed method.