Research on Fault Identification Algorithm of Grounding Grid

Introduction: Grounding grids are crucial for power system safety, but corrosion can lead to failures. Traditional corrosion diagnosis methods have limited accuracy due to environmental interference, particularly in multi-branch corrosion detection. This article proposes an improved algorithm that combines minimum error, Tikhonov regularization, and iterative techniques to enhance fault detection. Methods: The proposed method divides the grounding grid into "internal" and "external" networks, allowing for the accurate modeling of fault conditions. A diagnostic equation is formulated that relates the change in port resistance to the branch fault, and the least-squares Tikhonov algorithm is applied to refine the solution. This reduces the ill-conditioned nature of the equation, thereby improving accuracy. The algorithm is verified for its feasibility through Multisim simulations and further tested in MATLAB to evaluate its robustness under different fault conditions. Results: Simulation results confirm that the proposed algorithm significantly outperforms traditional methods in diagnosing multi-branch corrosion. The least squares Tikhonov regularization effectively alleviates the ill-conditioned problem and obtains a solution closest to the preset fault value with the minimum error. Multisim simulation proves the success of fault location. And MATLAB verification confirms the reliability of this method under different grid conditions. The algorithm achieves higher accuracy in fault detection, particularly in complex multi-branch scenarios. It should have practical applicability for a large-scale grounding grid. Discussions: The proposed grounding grid fault diagnosis algorithm integrates the minimum error method, Tikhonov regularization, and iterative techniques to achieve optimal results. Compared to the traditional method, it offers significant improvements in accuracy, robustness, and applicability to multi-branch corrosion scenarios. The least-squares Tikhonov regularization effectively addresses the ill-conditioned nature of the fault diagnosis equation, alleviating the instability of the solution caused by measurement noise and environmental interference. Conclusion: The proposed method enhances the accuracy of grounding grid fault diagnosis, particularly in complex scenarios involving corrosion. It provides a practical solution for grounding grid maintenance of the power system. Future work can focus on product development and field application.