Abstract
Wind power ramp events (WPREs) are a common phenomenon in wind power generation. This unavoidable phenomenon poses a great harm to the balance of active power and the stability of frequency in the power supply system, which seriously threatens the safety, stability, and economic operation of the power grid. In order to deal with the impact of ramp events, accurate and rapid detection of ramp events is of great significance for the formulation of response measures. However, some attribute information is ignored in previous studies, and the laws and characteristics of ramp events are difficult to present intuitively. In this paper, we propose a visualization-based ramp event detection model for wind power generation. Firstly, a ramp event detection model is designed considering the multidimensional attributes of ramp events. Then, an uncertainty analysis scheme of ramp events based on the confidence is proposed, enabling users to analyze and judge the detection results of ramp events from different dimensions. In addition, an interactive optimization model is designed, supporting users to update samples interactively, to realize iterative optimization of the detection model. Finally, a set of visual designs and user-friendly interactions are implemented, enabling users to explore WPREs, judge the identification results, and interactively optimize the model. Case studies and expert interviews based on real-world datasets further demonstrate the effectiveness of our system in the WPREs identification, the exploration of the accuracy of identification results, and interactive optimization.