New energy power grid frequency risk assessment method and device based on deep learning

The invention is suitable for the technical field of artificial intelligence power grid risk assessment, and provides a new energy power grid frequency risk assessment method and device based on deep learning, and the method comprises the steps: obtaining coordinates, wind speeds and illumination intensity data of all wind power stations and photovoltaic power stations in a new energy power grid, and generating two-dimensional grid data; inputting the two-dimensional grid data into the trained CNN neural network to obtain an output result; judging whether the output result is greater than a threshold, and if so, judging that the system frequency domain of the corresponding power station is abnormal; and outputting a judgment result. According to the embodiment of the invention, the neural network based on deep learning can well learn the characteristics of historical data, thereby comprehensively evaluating the frequency line-crossing risk of the power grid through the geographic coordinates, the wind speed c