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A Subtraction-Average-Based Optimizer for Solving Engineering Problems with Applications on TCSC Allocation in Power

Ghareeb Moustafa1,2, Mohamed A Tolba3, Ali M El-Rifaie4

  • 1Electrical Engineerng Department, Jazan University, Jazan 45142, Saudi Arabia.

Biomimetics (Basel, Switzerland)
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PubMed
Summary
This summary is machine-generated.

This study introduces an improved subtraction-average-based optimization algorithm (ISAOA) that significantly enhances engineering optimization. The ISAOA demonstrates superior performance in reducing power system losses through optimal Thyristor Controlled Series Capacitor (TCSC) placement.

Keywords:
allocation problembenchmark models testingcooperative learning techniquepower losses minimizationsubtraction-average-based optimizer

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Area of Science:

  • Engineering Optimization
  • Electrical Power Systems

Background:

  • Evolutionary algorithms are crucial for complex engineering problems.
  • The standard Subtraction-Average-based Optimization Algorithm (SAOA) has limitations in search capabilities.
  • Optimizing power system components like Thyristor Controlled Series Capacitors (TCSCs) is vital for efficiency.

Purpose of the Study:

  • To develop an enhanced optimization algorithm, the Improved SAOA (ISAOA), with superior search capabilities.
  • To apply the ISAOA for optimal TCSC allocation to reduce power system losses.
  • To compare the ISAOA's performance against standard SAOA and other leading optimization algorithms.

Main Methods:

  • The study proposes an ISAOA incorporating cooperative learning with a leader solution.
  • The ISAOA was tested on benchmark functions and applied to TCSC allocation in power grids.
  • Performance was evaluated against SAOA, Artificial Ecosystem Optimizer (AEO), AQuila Algorithm (AQA), Grey Wolf Optimizer (GWO), and Particle Swarm Optimizer (PSO) on the IEEE-30 bus system.

Main Results:

  • The ISAOA demonstrated significant superiority over the standard SAOA in initial tests.
  • Simulations confirmed the ISAOA's effectiveness in reducing power system losses.
  • The ISAOA achieved greater power loss reductions compared to GWO, AEO, PSO, and AQA across three case studies.

Conclusions:

  • The ISAOA is a highly effective evolutionary technique for engineering optimization.
  • The ISAOA provides a superior method for optimizing TCSC placement to minimize power system losses.
  • The proposed ISAOA offers a promising approach for enhancing electrical grid efficiency.