OPTIMAL PLACEMENT OF METERS FOR POWER SYSTEM STATE ESTIMATION BY USING ARTIFICIAL INTELLEGENCE TECHNIQUES, A COMPARATIVE STUDY

  • Kassim Abdulrezak Al-Anbarri Electrical Eng. Dept., Mustansiriyah University, Baghdad, Iraq.
  • Mohammed Falih Hasan Electrical Eng. Dept., Mustansiriyah University, Baghdad, Iraq.

Abstract

Meters placements play an important role in attaining the system observability for estimating the state of the power system. This paper presents algorithms to select the best locations for installing the meters by using artificial intelligence techniques. Two algorithms have been proposed and implemented in order to avoid the circumstances arisen by random distribution of the meters. The first algorithm include optimal placement of meters by using Particle Swarm Optimization (PSO). The second algorithm utilizes the Artificial Bee Colony (ABC) to select the best allocation of meters. The proposed algorithms randomly searches the best location of meter placement based on the minimum error of state estimation. In comparison to traditional methods, PSO and ABC able to search the optimal measurement placement without having to test possible location one after another since PSO and ABC are an optimization method. The performance of the proposed algorithms are verified by applying the proposed algorithms on IEEE-14 and 30 bus standard test system. The obtained results reveal the importance of optimal selection of meter placement in accelerating the convergence the state estimation process. The capability of the proposed algorithm in determining the best estimate of the state variables accurately with a less number of iterations and less execution time than conventional method (WLS) is clarified.

Published
2019-03-19
How to Cite
ABDULREZAK AL-ANBARRI, Kassim; FALIH HASAN, Mohammed. OPTIMAL PLACEMENT OF METERS FOR POWER SYSTEM STATE ESTIMATION BY USING ARTIFICIAL INTELLEGENCE TECHNIQUES, A COMPARATIVE STUDY. JOURNAL OF ENGINEERING AND SUSTAINABLE DEVELOPMENT, [S.l.], v. 22, n. 02 Part -2, p. 15-32, mar. 2019. ISSN 2520-0917. Available at: <http://jeasd.uomustansiriyah.edu.iq/index.php/jeasd/article/view/495>. Date accessed: 02 july 2020.