Research on multidimensional data analysis method of electric power based on tensor network model

Tensor networks can describe entity objects from multiple dimensions to more accurately reflect all the features of the entity. There are a large number of physical devices in the power grid, with multiple basic attributes and frequent updates of device status. The use of tensor network models can effectively characterize the equipment in the power grid and the operational situation of the power grid. This paper proposes the use of tensor network models to analyze the equipment and dynamic characteristics of the power grid. Construct a tensor network model from multiple dimensions such as device type, voltage level, affiliation, and device status. In response to the large number and scale of equipment in the power grid, analyzing the characteristics of concentrated application scenarios, reducing the dimensions of tensor network models and highlighting commonly used data description dimensions to meet the needs of power grid applications. Based on this model, carry out application verification such as power supply reliability analysis and energy loss analysis. Extract corresponding data resources from different coordinate dimensions, and then calculate the inner product of the data from different dimensions. Based on the calculation results, perform neural network correlation analysis on different data resources, and solve the analysis results and solutions.