Research on multi-source heterogeneous data fusion method of substation based on cloud edge collaboration and AI technology

As a key critical of the power system, a substantial number of intelligent devices are deployed in the substation. The data generated by these devices exhibit exponential growth, and the data types are diverse and complex, encompassing both structured and unstructured data. The integration of multi-source heterogeneous data is of significant importance for enhancing the operational efficiency, fault early warning capability, and intelligent level of the substations. However, the integration of multi-source heterogeneous data in substations has consistently presented a challenge for AI technology intervention. The effective integration of data from different platforms remains a significant challenge at present. Consequently, this paper proposes a multi-source heterogeneous data fusion method for substations based on cloud edge collaboration and AI technology. This method is based on artificial intelligence cloud services and constructs a substation cloud edge collaborative network based on AI Cloud architecture. It utilizes the 5G PaaS platform to establish substation cloud PaaS and edge PaaS, respectively, and employs a dynamic task scheduling strategy for substation cloud edge collaboration to achieve the collaboration of multi-source heterogeneous data in the substation cloud and edge. A heterogeneous data resource pool is established, and the data fusion module uses the dynamic Bayes network model in AI technology to achieve the fusion of multi-source heterogeneous data in the substation. The experimental results demonstrate that the method proposed in this paper can more effectively control the energy consumption of edge nodes, exhibits high efficiency in data fusion, and can effectively integrate and display multi-source heterogeneous data. All information gain values exceed 0.96, with the integrity value being the highest, approaching 100%. The method demonstrates a strong capability in fusing multi-source heterogeneous data of substations.