Corrosion Diagnosis in Grounding Grids Using the Enhanced MCP‐IWHO Algorithm

Diagnostic methods based on electrical network theory are commonly employed for locating corrosion in grounding grids. Due to the relatively small number of nodes available for measurement compared to the total number of nodes, the resulting diagnostic equations for corrosion are underdetermined and nonlinear, making it challenging to accurately determine the location and extent of corrosion in branches. To address this issue, this study proposes an improved Wild Horse Optimization algorithm (MCP‐IWHO) that incorporates MCP regularization for solving corrosion equations. This enhanced algorithm achieves a balance between global and local search capabilities and controls the sparsity of the solution to reduce deviations. Simulation calculations were conducted using the actual grounding grid of a 110 kV substation in a specific region as a case study. The results demonstrate that this algorithm can accurately determine the position and severity of corrosion in branches, outperforming both the standard Wild Horse Optimization algorithm (WHO) and traditional optimization algorithms in terms of error minimization and convergence speed. This provides a more efficient and reliable method for diagnosing corrosion in underground grounding grids.