Efficient Hybrid Algorithm Solution for Optimal Reactive Power Flow Using the Sensitive Bus Approach

Authors

  • Z. Sahli QUERE Laboratory, Electrical Engineering Department, University Ferhat Abbas Setif 1, Algeria
  • A. Hamouda QUERE Laboratory, Electrical Engineering Department, University Ferhat Abbas Setif 1, Algeria
  • S. Sayah QUERE Laboratory, Electrical Engineering Department, University Ferhat Abbas Setif 1, Algeria
  • D. Trentesaux LAMIH-UMR CNRS, University of Valenciennes, France
  • A. Bekrar LAMIH-UMR CNRS, University of Valenciennes, France
Volume: 12 | Issue: 1 | Pages: 8210-8216 | February 2022 | https://doi.org/10.48084/etasr.4680

Abstract

This paper presents the design and application of an efficient hybrid algorithm for solving the Optimal Reactive Power Flow (ORPF) problem. The ORPF is formulated as a nonlinear constrained optimization problem where the active power losses must be minimized. The proposed approach is based on the hybridization of Particle Swarm Optimization (PSO) and Tabu-Search (TS) technique. The proposed PSO-TS approach is used to find the settings of the control variables (i.e. generation bus voltages, transformer taps, and shunt capacitor sizes) which minimize transmission active power losses. The bus locations of the shunt capacitors are identified according to sensitive buses. To show the effectiveness of the proposed method, it is applied to the IEEE 30 bus benchmark test system and is compared with PSO and TS without hybridization, along with some other published approaches. The obtained results reveal the effectiveness of the proposed method in dealing with the highly nonlinear constrained nature of the ORPF problem.

Keywords:

optimal reactive power flow, active power loss minimization, hybrid methods, particle swarm optimization, tabu search, sensitive bus

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How to Cite

[1]
Z. Sahli, A. Hamouda, S. Sayah, D. Trentesaux, and A. Bekrar, “Efficient Hybrid Algorithm Solution for Optimal Reactive Power Flow Using the Sensitive Bus Approach”, Eng. Technol. Appl. Sci. Res., vol. 12, no. 1, pp. 8210–8216, Feb. 2022.

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