Distribution Network Security Assessment Based on Maximum Entropy and Weight Self-Learning

Situational awareness plays a pivotal role in comprehending the operational state and evaluating the security level of a distribution network. This study focuses on enhancing the stability and power supply capacity of the network by investigating its situational awareness through security risk assessment. Six carefully selected security risk assessment indicators are applied to the distribution network. The hierarchical analysis method is employed to establish a comprehensive security risk assessment system, with their respective weights determined using a self-learning approach based on Bayesian and maximum entropy. Through power flow calculations, the scores of the fundamental-level indicators are obtained, facilitating the derivation of an overall score to evaluate the security risk of the power network. Furthermore, the causes behind the low score observed in the exemplified power grid are identified, effectively achieving the objectives of the distribution network's security risk assessment.