MATLAB-Based Distribution Network Reliability Calculation Using Bayesian Networks

Distribution network reliability assessment is a critical component in power system planning and operation. Traditional methods often require complex fault tree analysis or Monte Carlo simulations, whereas Bayesian network-based solutions provide a more intuitive probabilistic modeling approach. Bayesian networks represent causal relationships between variables through directed acyclic graphs, where nodes correspond to distribution network components (such as transformers and lines) and edges represent fault propagation paths. Each node contains a conditional probability table that quantifies the influence of parent node states. For example: an upstream circuit breaker failure may cause outages at multiple downstream load points, and such cascade effects can be automatically captured by the network structure. In MATLAB implementation, the Probability Toolbox is typically utilized to construct the network architecture: • Define node variables (e.g., "line aging degree," "protection device sensitivity") • Determine prior probabilities based on historical data or expert knowledge • Compute load point outage probabilities using probabilistic inference algorithms like the junction tree algorithm Compared to traditional methods, this approach offers several advantages: • Supports real-time updates: When a node failure is detected, failure probabilities of other nodes can be dynamically adjusted • Handles uncertainties: Vague component states (e.g., "moderate corrosion") can be converted into probability intervals • Visual diagnostics: Network structure diagrams directly identify system vulnerabilities Typical application scenarios include fault prediction during typhoon conditions and reliability re-evaluation after distributed generation integration. Developers should consider data quality impacts on probability parameters and balance network complexity with computational efficiency.