A method and system for reactive power and voltage control of power systems based on deep learning

Abstract

The present invention relates to a reactive power and voltage control method and system of a power system based on deep learning, including generating data samples corresponding to various operating scenarios based on the reactive voltage control method; calling the ICNN deep learning method to perform deep learning training based on training samples, After the training is completed, test the fitting effect of the deep learning model based on the test sample; evaluate whether the currently generated deep learning model is suitable for actual control based on the sample average error index and the maximum single sample error index; when the deep learning model passes the index evaluation It is applied to real-time online calculation. During a single calculation process, the parameters of the model input variables corresponding to the current operation scene are received in real time, and the single forward calculation of deep learning is completed to obtain the control instructions of each reactive device. The invention can greatly improve the timeliness of reactive power and voltage control, and can respond more quickly to the fluctuation of reactive power and voltage of the local power grid caused by the access of large-scale distributed energy sources.

Description

technical field [0001] The invention belongs to the field of power system control, in particular to a method and system for controlling reactive power and voltage of a power system based on deep learning. Background technique [0002] As the proportion of distributed energy such as wind power and photovoltaics in the power grid increases year by year, due to its output volatility and randomness, as well as the characteristics of local high-density grid connection, the reactive power and voltage of the local power grid often occurs in a short period of time. Substantial changes. Taking a wind farm in China as an example, in the operating data of the wind farm, the average fluctuation of the 220kV bus voltage exceeded 6kV within 10s, and the maximum fluctuation exceeded 5kV within 2s. These rapid fluctuations of reactive power and voltage brought about by the access of distributed energy resources have undoubtedly brought severe challenges to traditional reactive power and vo...

AI Technical Summary

Problems solved by technology

However, reinforcement learning is still an optimization method in essence. Some scholars have proved that even if the same hyperparameters and reinforcement learning algorithms are used, only because of the different random seed settings, reinforcement learning methods will appear when solving the same task at different times. In the case of different performance, there is a disadvantage of unstable optimization results

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