Ultra-Short-Term Wind Farm Power Prediction Considering Correlation of Wind Power Fluctuation

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 20; p. 6538
• Main Authors: Li, Chuandong, Zhang, Minghui, Zhang, Yi, Yi, Ziyuan, Niu, Huaqing
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 10.10.2024; MDPI
• Subjects: Accuracy; adjacent wind farms; Buildings and facilities; Green technology; Methods; prior information period; spatial–temporal correlation; ultra-short-term output prediction; variational Bayesian model; Wind farms; Wind power; China; ultra-short-term output prediction; prior information period; spatial–temporal correlation; variational Bayesian model; adjacent wind farms
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24206538

Accurate ultra-short-term power prediction for wind farms is challenging under rapid wind speed fluctuations, complicating production planning and power balancing. This paper proposes a new method considering spatial and temporal correlations of wind fluctuations among adjacent wind farms. The method first calculates the time difference between power fluctuations based on wind speed, direction, and relative positions, determining the prior information period. The variational Bayesian model is then used to extract implicit relationships between power fluctuations of adjacent wind farms, enabling power prediction during the prior information period. Finally, the non-prior information period is predicted to complete the ultra-short-term power prediction. Using measured data from three wind farms in Fujian Province, compared to other models, the method demonstrates improved accuracy by effectively leveraging the power fluctuation characteristics of adjacent wind farms, and it has a certain amount of generalizability.