Wind power ramp event detection using a multi-parameter segmentation algorithm

• Main Authors: Danielle Lyners, Hendrik Vermeulen, Matthew Groch
• Format: Article
• Language: English
• Published: Elsevier 2021-11-01
• Series: Energy Reports
• Subjects: Wind power generation; Wind power ramp events; Signal processing algorithms; Ramp detection algorithms; Optimized swinging door algorithm
• Online Access: http://www.sciencedirect.com/science/article/pii/S235248472100740X

The variable nature of wind power and the associated ramp events poses a number of operational challenges to grid operators, especially under high penetration of wind energy. These challenges typically relate to system stability, frequency control and dispatch. The adverse impacts of wind power ramps can be mitigated in practice through optimal scheduling and dispatch of flexible reserves and rapid response ancillary services. This, however, requires appropriate ramp detection algorithms, together with accurate ramp forecasting. This paper proposes a novel multi-parameter segmentation algorithm for the detection of wind power ramps. Ramp detection results are presented for a utility-scale wind farm, and the performance of the proposed algorithm is compared with existing algorithms, including the L1-ramp detect with sliding window and the optimized swinging door algorithm. The results show that the proposed algorithm is superior, particularly with reference to criteria such as ramp detection accuracy, computational expedience and ramp start- and end-point accuracy.