Power System Reliability Assessment Based on Big Data

With the application of big data technology and the improvement of its level, more and more enterprises and organizations begin to use big data analysis and mining technology to support business decisions. Big data technology has found applications in the electric power industry, particularly in predicting and evaluating the reliability of distribution systems. By utilizing big data information from the distribution network, the reliability prediction and evaluation method based on big data technology can possess the capability to pinpoint critical factors that exert a substantial influence on reliability indicators. These factors are then used to establish an efficient prediction model[1]. Among them, the rough set information entropy theory can help determine factors that are highly correlated and independent of each other, thereby improving the accuracy and reliability of predictions. Furthermore, real-time monitoring, fault prediction and diagnosis, fault location, and recovery of distribution systems can be effectively conducted using big data technology. These applications contribute to enhancing the efficiency and stability of power system. To sum up, the application of big data technology provides new ideas and methods for reliability analysis and management of distribution systems, and is expected to provide better solutions for reliability improvement and improvement in the power field.