Research and Implementation of Building a Digital Twin Model for Electric Grid Based on Deep Learning

In the production of the power industry, digital twin technology is mainly used for the dispatch and distribution of electricity. Through real-time monitoring and analysis of key indicators in the dispatch center of the power company, the operation level and power supply service quality of the power company are improved. At present, the digital twin applications in the power industry are mainly used for indicator monitoring and process analysis, with the main goal of monitoring being existing collected data, lacking real-time analysis of higher-order data. With the continuous expansion of electricity consumption scale, the electricity demand of electricity customers is also becoming increasingly rich, which correspondingly drives the data generated in various business processes of power enterprises to develop towards a trend of high-dimensional and high-order. Traditional data processing and mining technologies are no longer able to quickly characterize and describe such high-order and high-dimensional data. Only by utilizing more efficient data analysis methods can we meet the current demand for power digital twin services. This article proposes a method for constructing a power grid digital twin model based on deep learning methods, which conducts deep learning on high-dimensional and high-order data in the power grid, identifies its features, and constructs a data category classifier to promote the construction of a power grid digital twin model that is more needed for load power business analysis, and improve the usability and applicability of the power grid digital twin model.