MATLAB-Based Distribution Network Reliability Calculation Using Bayesian Networks

MATLAB-based development refers to creating computational tools using MATLAB software, which serves as a powerful computer-based platform for performing various calculations and analytical tasks. In this project, MATLAB is employed to compute distribution network reliability, which measures the network's ability to maintain stable operation under both normal and abnormal conditions. The implementation involves creating probabilistic models using MATLAB's Statistics and Machine Learning Toolbox, where network components and their interdependencies are represented as nodes in a Bayesian network. Bayesian networks, as probabilistic graphical models, are utilized to simulate and predict the occurrence probabilities of various events and scenarios within the distribution system. This approach enables the calculation of reliability indices such as SAIFI (System Average Interruption Frequency Index) and SAIDI (System Average Interruption Duration Index) through probability propagation algorithms. The MATLAB implementation typically includes functions for network structure learning, parameter estimation using historical outage data, and probabilistic inference calculations. This MATLAB-based Bayesian network tool provides power companies with a valuable solution for better managing and maintaining their distribution network systems, thereby enhancing overall reliability and operational efficiency through data-driven decision support.