Boosting the Development and Management of Wind Energy: Self-Organizing Map Neural Networks for Clustering Wind Power Outputs

Yanqian Li, Yanlai Zhou (), Yuxuan Luo, Zhihao Ning and Chong-Yu Xu
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Yanqian Li: State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China
Yanlai Zhou: State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China
Yuxuan Luo: State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China
Zhihao Ning: State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China
Chong-Yu Xu: State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China

Energies, 2024, vol. 17, issue 21, 1-15

Abstract: Aimed at the information loss problem of using discrete indicators in wind power output characteristics analysis, a self-organizing map neural network-based clustering method is proposed in this study. By identifying the appropriate representativeness and topological structure of the competition layer, cluster analysis of the wind power output process in four seasons is realized. The output characteristics are evaluated through multiple evaluation indicators. Taking the wind power output of the Hunan power grid as a case study, the results underscore that the 1 × 3-dimensional competition layer structure had the highest representativeness (72.9%), and the wind power output processes of each season were divided into three categories, with a robust and stable topology structure. Summer and winter were the most representative seasons. Summer had strong volatility and small wind power outputs, which required the utilization of other power sources to balance power supply and load demand. Winter featured low volatility and large wind power outputs, necessitating cooperation with peak-shaving power sources to enhance the power grid’s absorbability to wind power. The seasonal clustering analysis of wind power outputs will be helpful to analyze the seasonality of wind power outputs and can provide scientific and technical support for guiding the power grid’s operation and management.

Keywords: wind power output; clustering analysis; self-organizing map neural network; data mining; Hunan grid (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: 2024
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