A Comparative Study between Bayesian Network and hybrid Statistical Predictive models for Proactive Power System Network Resilience Enhancement Operational Planning

• Published in: IEEE access Vol. 11; p. 1
• Main Authors: Omogoye, Samuel O., Folly, Komla A., Awodele, Kehinde O.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Bayes methods; Bayesian analysis; Bayesian network (BN) predictive algorithm; combined statistical DSN’s line FC-MCS-SCENRED predictive algorithm; Comparative studies; Computer simulation; Contingency; Decision making; distribution system network (DSN); Fault diagnosis; Fragility; hurricane events; Hurricanes; hybrid statistical DSN’s line FC-MCS-SCENRED predictive algorithm; Integer programming; Linear programming; Machine learning; Mixed integer; Monte Carlo simulation; Optimization; Optimization models; Power system reliability; pre-hurricane event’s optimal DSN reconfiguration and resource allocation; Prediction algorithms; Prediction models; Predictions; Predictive models; Resilience; Resource management; Statistical methods; Statistical prediction; Strategic planning
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3263490

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 fault 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 the 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 the forecasted oncoming hurricane event. In this paper, the Bayesian network (BN) and combined 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 general algebraic modelling system (GAMS) on a redesigned 48-bus DSN. The simulated initial expected load loss of 39% of 35 MWh was decreased to 35.34%, and then to 30.71% with the use of combined 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 combined statistical DSN's line FC-MCS-SCENRED predictive model.