A Method for Estimating Self-Healing Performance Based on Regional Distribution Network Operation

In this paper, the pivotal role of distribution automation switches in the fault self-healing process within regional distribution systems is examined, taking the limited number of devices into consideration. Furthermore, the significant impact of the probability of restoring power supply to users on the overall self-healing processing efficiency is highlighted. A simulation model is developed to investigate the effects of key parameters, such as switchgear distribution density and fault isolation range, on the self-healing effectiveness following line faults in the distribution network. Via data fitting, we aim to uncover the intrinsic correlations and relative importance of various factors influencing the self-healing capability. This provides scientific grounds for prioritizing operational maintenance tasks and resource allocation strategies. The method proposed in this paper is validated through the analysis of a practical case from an actual city's power grid, offering a detailed account of its specific manifestations. Based on this, a predictive analysis of self-healing performance is conducted using a multiple linear regression model. This not only facilitates the upgrading of power grid operation and maintenance, but also provides valuable insights for enhancing self-healing abilities in similar areas.