Microgrid reactive automatic control method based on convolutional neural network

Abstract

The invention provides a microgrid reactive automatic control method based on a convolutional neural network. According to the microgrid reactive automatic control method, an SCADA is adopted to collect real-time operation data of a microgrid system to generate two-dimensional power matrix data; the optimal reactive power of a reactive device corresponding to the two-dimensional power matrix datais calculated by utilizing the optimal power flow, and the optimal reactive power of the reactive device is used as a label value; and a convolutional neural network model is trained, so that the optimal reactive power of each reactive device can be determined according to system operation data. The method is characterized in that the two-dimensional convolution operation sparse interaction, the weight sharing and equivariant representation are utilized, the convolutional neural network model is established and model training is carried out, so as to realize automatic feature extraction on theoperation state of the microgrid, so that the optimal reactive power of each reactive device is determined; and meanwhile, the voltage deviation and the network loss during microgrid operation are taken into consideration, and the method has high economical efficiency and safety.

Description

technical field [0001] The invention relates to the field of automatic reactive power control of power systems, in particular to a method for automatic reactive power control of microgrids based on convolutional neural networks. Background technique [0002] With the gradual maturity of distributed power supply technology and the continuous advancement of commercialization projects, the micro-grid system has been vigorously developed and constructed as an important technical platform for efficient management and flexible control of distributed power supply. However, the fluctuation, intermittency and uncontrollability of wind power output make the operating state of the microgrid system complex and changeable, and it is difficult to predict accurately. Therefore, as one of the means to ensure the stable economic operation of the microgrid system, the reactive power automatic control strategy has become one of the current research hotspots. [0003] At present, the research ...

AI Technical Summary

Problems solved by technology

However, the intelligent optimization algorithm takes a long time to solve, and it is easy to fall into local optimum, which is not suitable for real-time control of reactive power, and is mostly used for day-ahead scheduling and real-time scheduling

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