A Solution Method for Non-Linear Underdetermined Equation Systems in Grounding Grid Corrosion Diagnosis Based on an...

Area of Science:

• Electrical Engineering
• Computational Intelligence
• Optimization Algorithms

Background:

• Aging power grids face critical vulnerabilities due to grounding grid corrosion.
• Conventional diagnostic methods for corrosion are computationally intensive and uncertain.
• Existing methods struggle with high-dimensional electrical data and nonlinear under-determined systems.

Purpose of the Study:

• To develop a novel, nature-inspired optimization algorithm for diagnosing grounding grid corrosion.
• To improve the accuracy, robustness, and efficiency of corrosion detection in power systems.
• To address the limitations of conventional diagnostic approaches.

Main Methods:

• Proposing the Enhanced Biomimetic Hippopotamus Optimization (EBOHO) algorithm.
• Incorporating three synergistic strategies: beta-function herd seeding, elite-mean cooperative foraging, and lens imaging opposition.
• Benchmarking EBOHO against state-of-the-art meta-heuristics on the CEC 2017 suite and classical design problems.
• Validating EBOHO through an industrial case study on grounding grid corrosion.

Main Results:

• EBOHO demonstrated superior global search capability, robustness, and convergence speed compared to existing algorithms.
• The algorithm effectively resolved under-determined equations associated with grounding grid corrosion.
• EBOHO precisely pinpointed corrosion sites in the industrial case study.

Conclusions:

• The Enhanced Biomimetic Hippopotamus Optimization (EBOHO) algorithm is a highly effective nature-inspired tool for diagnosing grounding grid corrosion.
• EBOHO offers a computationally efficient and precise solution for aging power system infrastructure.
• This algorithm shows significant promise for enhancing the reliability and safety of electrical grids.