DESIGN OF POWER GENERATION PREDICTION MODEL FOR WIND FARMS DURING SHORT-TERM HEAVY RAINFALL WEATHER

Hu Jiaqiu; Tang Jian; Zhuo Yixin; Huang Kui; Li Run; Gao Xuefei

doi:10.19912/j.0254-0096.tynxb.2023-0829

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (9) : 543-548. DOI: 10.19912/j.0254-0096.tynxb.2023-0829

DESIGN OF POWER GENERATION PREDICTION MODEL FOR WIND FARMS DURING SHORT-TERM HEAVY RAINFALL WEATHER

Hu Jiaqiu¹, Tang Jian¹, Zhuo Yixin¹, Huang Kui¹, Li Run², Gao Xuefei²

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Abstract

To ensure the normal power supply of the power plant, a short-term heavy rainfall weather wind farm power generation prediction model is designed to improve the power generation prediction effect. Expanding small samples of short-term heavy rainfall weather through the principles of Euclidean distance and angle; using the improved depth separable convolution algorithm, the spatio-temporal characteristics of weather power are extracted from the normal weather samples, and input into the long-term and short-term memory network to establish the prediction model of normal weather wind farm power generation reference value, and obtain the power generation reference value; in the Transformer algorithm, input an expanded sample to establish a prediction model for power generation loss value under short-term heavy rainfall weather; using a Sequence to Sequence network based on attention mechanism, combined with expanding samples, a prediction model for the time point of power generation loss is constructed. Combined with a loss value prediction model, the final power generation loss value is obtained; subtract the loss value from the reference value to obtain the prediction results of wind farm power generation under short-term heavy rainfall weather. The experiment proves that the model can effectively expand the small samples of short-term heavy rainfall weather; this method can accurately predict the time point of power generation loss, obtain the value of power generation loss, and complete power generation prediction; under different wind speeds, the key error index and deviation degree of the model's power generation prediction are relatively low, indicating a higher accuracy of power generation prediction.

Key words

short term heavy rainfall / wind farms / power generation / prediction model / separable convolution / attention mechanism

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Hu Jiaqiu, Tang Jian, Zhuo Yixin, Huang Kui, Li Run, Gao Xuefei. DESIGN OF POWER GENERATION PREDICTION MODEL FOR WIND FARMS DURING SHORT-TERM HEAVY RAINFALL WEATHER[J]. Acta Energiae Solaris Sinica. 2024, 45(9): 543-548 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0829

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