A Visualization-Based Ramp Event Detection Model for Wind Power Generation

Junwei Fu, Yuna Ni, Yuming Ma, Jian Zhao, Qiuyi Yang, Shiyi Xu, Xiang Zhang () and Yuhua Liu
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Junwei Fu: State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China
Yuna Ni: School of Information Management & Artificial Intelligence, Zhejiang University of Finance & Economics, Hangzhou 310018, China
Yuming Ma: School of Media and Design, Hangzhou Dianzi University, Hangzhou 310018, China
Jian Zhao: School of Information Management & Artificial Intelligence, Zhejiang University of Finance & Economics, Hangzhou 310018, China
Qiuyi Yang: School of Information Management & Artificial Intelligence, Zhejiang University of Finance & Economics, Hangzhou 310018, China
Shiyi Xu: School of Information Management & Artificial Intelligence, Zhejiang University of Finance & Economics, Hangzhou 310018, China
Xiang Zhang: School of Information Management & Artificial Intelligence, Zhejiang University of Finance & Economics, Hangzhou 310018, China
Yuhua Liu: School of Media and Design, Hangzhou Dianzi University, Hangzhou 310018, China

Energies, 2023, vol. 16, issue 3, 1-16

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.

Keywords: wind power ramp events; ramp event detection; interactive optimization; visual analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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