Improved Impedance Method for Cable Incipient Fault Location Based on Multiobjective Optimization

Abstract

The safe and stable operation of power systems imposes increasingly stringent requirements on the accurate localization of incipient cable faults. To address the limitation of traditional impedance methods, where the use of lumped-parameter models leads to decreasing localization accuracy with increasing fault distance, this article proposes an improved impedance-based fault location method based on multiobjective optimization using a time-domain distributed model. First, the characteristics of incipient cable faults are analyzed, the principle of fault location using the time-domain distributed model is established, and the factors affecting localization accuracy under this method are examined. Next, to tackle the complexity of solving the model, a multiobjective optimization approach is introduced. In particular, the phase difference between voltage and current at the fault location and the voltage difference across the line section are taken as objective functions. The signals are first filtered using the ensemble empirical mode decomposition (EEMD) algorithm, followed by parameter optimization using the multiobjective particle swarm optimization (MOPSO) algorithm to achieve precise fault localization. Comprehensive simulation results demonstrate high localization fidelity with a mean relative error of 0.21 % across various scenarios, exhibiting excellent immunity to sampling frequency limitations (down to 10 kHz), load fluctuations, cable length, and intense noise. The experimental results show a fault localization error of 0.77 % for coaxial cables, confirming the good engineering applicability of the proposed method. Moreover, the MOPSO algorithm demonstrates good applicability in this method due to its strong global search capability and fast convergence. This positioning algorithm provides a novel measurement paradigm for online incipient fault localization of power cables, characterized by low cost, high robustness, and ease of engineering implementation.