Improved Grounding Grid Imaging via IRLS-Based Electrical Impedance Tomography With Tikhonov–TV Regularization

The grounding grid is a key infrastructure for the safety of the power system, but it is difficult to detect faults because it is buried in the ground. However, the grounding grid in long-term operation may be damaged due to gradual corrosion of the soil or some mechanical failures. To address this problem, this paper proposes an imaging algorithm using Iteratively Reweighted Least Squares (IRLS) based on the forward and inverse problem framework of Electrical Impedance Tomography (EIT) of grounding grid, and combining Tikhonov and Total Variation (TV) regularization to improve reconstruction accuracy and stability. Firstly, this paper establishes the EIT forward and inverse models of grounding grid, uses the IRLS algorithm to convert the L1-paradigm problem into an L2-paradigm optimization problem, and then introduces the soft threshold function to stabilize the solution problem; After integrating the Tikhonov regularization and TV regularization, the joint optimization minimum generalized function model is constructed, and finally, the problem is solved using the Newton-Raphson iterative method. The simulations demonstrated superior performance over other algorithms. On average, the proposed method improved the Image Correlation Coefficient (ICC) by 4.7% and reduced the Voltage Relative Error (VRE) by 48%. Even under 34 dB of noise, it maintained high imaging quality and successfully identified specific corroded branches in a final case simulation, confirming its high accuracy, stability, and practical applicability.