A Comparative Study Between Bayesian Network and Hybrid Statistical Predictive Models for Proactive

Enhancing the operational resilience of the distribution system network (DSN) proactively in a hurricane-prone region requires a pre-hurricane event DSN optimization model, built on accurate hurricane-induced DSN line damage prediction scenarios. In the past, the resilience evaluation methods such as statistical sequential and non-sequential Monte Carlo simulation (MCS) contingency-based technique, and Machine learning-based Bayesian Networks (BN) technique, have been proposed to strengthen the operational resilience of the DSN proactively against forecasted oncoming hurricane events. However, a comparative study is largely unexplored to evaluate which of these two methods is best for proactive operational planning decision-making against forecasted oncoming hurricane events. In this paper, the Bayesian network (BN) and hybrid statistical DSN’s Fragility-curve (FC)-Monte Carlo simulation (MCS)-Scenario reduction (SCENRED) predictive algorithms were developed. The DSN line fault prediction scenarios simulated leveraging the predicted oncoming hurricane Ewiniar data were utilized to perform pre-hurricane DSN optimization to proactively decrease the DSN expected load loss. The pre-event system optimization problems were formulated in a mixed integer linear programming (MILP) approach and solved using a CPLEX solver in the general algebraic modelling system (GAMS) on a redesigned 48-bus DSN. The simulated initial expected load loss of 39% of 35 MW was decreased to 35.34%, and then to 30.71% with the use of hybrid statistical DSN’s FC-MCS-SCENRED, and the BN-DSN predictive models. These results were validated using the Electrical transient analyzer program (ETAP). This study confirmed that the BN-DSN predictive model is a better operational planning tool compared to hybrid statistical DSN’s line FC-MCS-SCENRED predictive model.