N-1 static security assessment method for power grids with high penetration rate of renewable energy generation

Abstract

A new N-1 static security assessment method based on deep convolutional neural network (DCNN) for the power grid with a high penetration rate of renewable energy generation is proposed in this paper. First, to generate realistic wind and solar scenarios for better training of DCNN, we built a dual discriminator generative adversarial network (D2GAN). Second, a DCNN for calculating the N-1 static security index of the power grid was built. The inputs are operating conditions constructed by the generated wind and solar power scenarios and the line outages, and the output is the corresponding N-1 static security index. Finally, the numerical experimental results based on the historical wind and solar power shows that the scenarios generated by D2GAN fit the real scenarios better than the scenarios generated by the commonly used Monte Carlo (MC) method. And the numerical results of applying the proposed approach to IEEE 14-bus, 30-bus and 118-bus test system demonstrate its effectiveness for N-1 contingency analysis. In terms of accuracy and computation speed, the comparison with traditional method based on load flow analysis, the ANN, and the DCNN trained with the scenarios generated by MC indicate that the proposed model is reliable and more efficient.