Analysis of Network Loss Energy Measurement Based on Machine Learning

摘要

The real-time network loss calculation of the power system has particularly important significance for the optimal scheduling and economic operation of the power system. With the development of artificial intelligence, the real-time data collected by the power system is more and more accurate and efficient. It is convenient for the artificial intelligence computer to calculate network loss in real time and make scheduling instructions in a short time, which makes it possible to implement scheduling optimization instantaneously. This paper analyzes the traditional network loss calculation methods, proposes a new method to calculate network loss using machine learning algorithms, and studies the application of machine learning algorithms in load image classification. This paper combines the machine learning algorithm with the new method of calculating network loss proposed in this paper to calculate and classify the network loss and obtain the optimal load distribution map under the target network loss. This method is simulated and verified in Matlab. The results show that the inductive learning algorithm can better realize the classification and prediction of network loss. Through this new method, we can achieve the classification and prediction of the load corresponding to a certain network loss value, so as to achieve the purpose of real-time monitoring and prediction of network loss and load, and provide a reliable basis for economic dispatch. This article has obtained the "deep research project based on three-state grid data fusion and verification technology" and technical support for power system dispatch and cloud platform.