Wind Power Output Limitation Pattern Recognition under Cold Wave Weather Conditions Based on Spatiotemporal Characteristics and GoogLeNet

The limited wind power output caused by cold wave weather poses challenges to the safe and stable operation of the power system. In order to improve the accuracy of wind power output recognition under cold weather conditions, this paper proposes a wind power output limitation pattern recognition method under cold weather conditions based on spatiotemporal characteristics and GoogLeNet. Firstly, construct a spatiotemporal characteristic set of wind power in the target wind farm; Then, the characteristic matrix is imaged to construct a wind power output limited characteristic mapping; Finally, a wind power output limitation pattern recognition model is constructed based on GoogLeNet. The calculation results show that based on the proposed method, the accuracy of wind power output recognition under cold weather conditions reaches 84.2%, which can effectively achieve wind power output limited warning.