Adaptive Flood Prevention System for Substations Using Integrated Fuzzy Bayesian Network and MILP Optimization
Jianhong Huang, Ri He, Xiaoming Zhang, Wenzhan Tan, Jibin Zhang · International Journal of Reliability Quality and Safety Engineering · 2025
This paper presents an integrated flood prevention framework for substations, combining a fuzzy Bayesian network (FBN) for continuous flood risk assessment with mixed integer linear programming (MILP) for real-time resource allocation. Building on recent studies of substation resilience and flood mitigation, the proposed approach captures both static site data and dynamic meteorological inputs to generate accurate, up-to-the-minute predictions of flood probability. The MILP model then allocates resources, such as pumps, barriers, and drainage systems, based on the FBN’s probabilistic estimates. In tests conducted on a 500 kV substation under simulated flood scenarios, the proposed system achieved higher accuracy and lower computational overhead relative to mainstream methods, including decision trees (DT), k-nearest neighbors (KNNs), and support vector machines (SVMs). A sensitivity analysis further revealed the model’s robust performance under varying flood severities and resource constraints, highlighting its potential for broader deployment. While the results underscore notable advantages in adaptability and operational resilience, challenges remain in gathering consistent, high-quality data and scaling the model for large or complex networks. Overall, this study offers a proactive, data-driven strategy for enhancing substation flood prevention efforts and reducing outage risks under extreme weather conditions.