Calculation and Analysis of Theoretical Line Loss Rate Based on Deep Learning Mechanism

Abstract

In the power industry system, line loss and line loss rate (L L R) are very important comprehensive indicators. The value of line loss directly affects the economic benefits of power companies, and is related to the vital interests of companies. It is not only important for the country to assess the power sector Economic indicators, colleagues can also reflect whether the grid structure and operation mode of a power grid are reasonable, and reflect the level of grid planning, power generation technology, and operation management. Based on this, the purpose of this article is to calculate and analyze the theoretical L L R based on the deep learning mechanism. This article first summarizes the theoretical basis of deep learning, and then studies the existing theoretical L L R calculation methods. On its basis, it is researched and analyzed in combination with the deep learning mechanism. This paper systematically explains the analysis process of the theoretical L L R based on the electrical network, the calculation method of the theoretical L L R based on DBN-DNN (D B N), and the theoretical L L R analysis based on the deep confidence network. And use comparative analysis method, observation method and other research methods to study the theme of this article. Experimental studies have shown that when the grid structure is unchanged, the DBN-DNN combined deep learning model proposed in this paper has a faster calculation speed, and the calculation result has a smaller deviation compared with the real result. The maximum error is 0.0077 and the minimum is 0.0011. Therefore, the deep learning model based on the DBN- DNN combination can accurately and quickly calculate the theoretical line loss rate when the grid structure is unchanged.