Research on distribution network assistant decision system based on big data analysis

With the rapid development of China’s economy, the demand for electricity is continuously increasing, leading to increased complexity in the power system. To process the exponentially growing data in the distribution network in real-time and accurately assist the power industry in making prompt decisions, this paper proposes a distribution network auxiliary decision-making system. It utilizes distributed computing to construct a streaming computing framework based on Storm, implementing the Very Fast Decision Tree (VFDT) algorithm, and a batch processing computing framework based on Spark, implementing the Random Forest algorithm. These frameworks enhance the scalability, real-time capability, and accuracy of big data analysis in the power grid. Through simulation experiments, we validate the efficient and accurate classification of various types of large-scale data using the VFDT algorithm based on Storm. Additionally, we verify the effectiveness and good scalability of the batch processing computing framework based on Spark for handling massive data processing.