Transactions on Machine Intelligence

Transactions on Machine Intelligence

Optimized Reactive Power Distribution for Enhancing Power System Efficiency Using a Generalized Teaching-Learning-Based Optimization Algorithm

Document Type : Original Article

Authors
M. Elahi Nejad 1
A. Samimi 2
M. Nikzad 3
1 MSc Student, Power Engineering Department, Faculty of Engineering, Boroujerd Branch, Islamic Azad University, Iran
2 Assistant Professor, Department of Electrical Engineering, Arak University of Technology, Arak, Iran
3 Assistant Professor, Electrical Engineering Department, Boroujerd Branch, Islamic Azad University, Iran
10.47176/TMI.2021.153
Abstract
Electrical energy generation in power systems aims to minimize the total production cost of active units within the power network, making it one of the most critical aspects of modern power systems. Optimal reactive power distribution is a key approach to ensuring the reliable and economical operation of power systems. The primary objective of reactive power distribution in power networks is to determine the control variables that minimize the objective function while adhering to system constraints. In this paper, a hybrid optimization algorithm is introduced, combining the Multi-Objective Teaching-Learning-Based Optimization (MTLBO) method with a fuzzy decision-making algorithm to solve the Reactive Power Dispatch (RPD) problem. The proposed method addresses multiple objectives, including reducing active power losses, improving voltage profiles, and enhancing network security. To assess the effectiveness of the proposed approach, simulations were performed on IEEE 57-bus and 118-bus test systems. The simulation results confirm the efficiency and superiority of the proposed method compared to conventional optimization techniques.
Keywords
Optimal Reactive Power Distribution
MTLBO Algorithm
Fuzzy Decision-Making Algorithm
System Loss Reduction
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Transactions on Machine Intelligence
Volume 4, Issue 3
Summer 2021
Pages 153-168

  • Receive Date 04 June 2021
  • Revise Date 15 August 2021
  • Accept Date 22 September 2021