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

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.