基于功率散点的风电机组出力分散性量化表征及挖掘方法

doi:10.19595/j.cnki.1000-6753.tces.250385

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Abstract With the increasing penetration of wind power into modern power systems, the uncertainty of wind turbine output poses significant challenges to grid stability and dispatch optimization. Traditional methods for evaluating wind turbine performance, such as wind-speed-power curve fitting verification, primarily focus on average output levels while neglecting the inherent dispersion of power scatter points. This limitation hinders the accurate assessment of output uncertainty, which is critical for refining scheduling strategies and improving operational efficiency in wind farms. This paper proposes a systematic quantitative characterization method for wind turbine output dispersion based on power scatter points.
The proposed method comprises three key steps: (1) identification and classification of power scatter points, where abnormal outliers (e.g., curtailment clusters and bottom accumulation points) are distinguished from normal scatter points that exhibit band-like distributions around the nominal power curve. (2) distribution fitting using the T-Location-Scale (TLS) model, which demonstrates superior accuracy in capturing the statistical characteristics of normalized power scatter points within wind speed intervals. (3) construction of the quantitative indicator “power fluctuation domain width”, defined as the confidence interval of the TLS distribution at a 90% confidence level. This indicator effectively quantifies the dispersion range of output power under normal operating conditions.
Case studies using operational data from wind farms in China’s Zhangbei region validate the method’s effectiveness. (1) Correlation with Power Increment: A Pearson correlation coefficient exceeding 0.95 confirms that output dispersion is highly correlated with power increments in specific wind speed intervals. Higher power increments amplify deviations in output caused by wind resource perception errors, thereby increasing dispersion. (2) Impact of Yaw Quality: By categorizing yaw error angles into four intervals, the study reveals that yaw misalignment significantly reduces output levels in medium-to-high wind speed regions. Implementing a reliable yaw interval division strategy reduces output dispersion by 9.1% in targeted wind speed ranges. (3) Multi-Model Turbine Comparison: Analysis of four turbine models (A, B, C, D) highlights the influence of factors such as turbine type, rated capacity, and operational age on output dispersion. For instance, direct-drive turbines (Model C) exhibit lower dispersion than doubly-fed turbines (Model A) due to reduced mechanical losses. Larger-capacity turbines (Model D) also demonstrate superior stability compared to smaller ones. (4) Seasonal Evolution Trends: Output dispersion varies significantly across seasons, with winter showing a 26.6% higher dispersion than summer. It is attributed to more substantial wind resource fluctuations, lower temperatures that affect air density, and icing that degrades turbine performance.
The proposed method offers a novel perspective on wind turbine performance evaluation, overcoming the limitations of traditional power-curve-based assessments. By integrating temporal-spatial feature extraction and probabilistic modeling, it enables more profound insights into the causes and evolution of output dispersion. Practical applications include optimizing yaw-control strategies, refining maintenance schedules, and improving power-prediction accuracy for wind farms. Future research directions include refining probability models for scatter-point fitting and developing multidimensional indicators to characterize output dispersion.

Key words： Wind turbine output dispersion power scatter points quantitative indicators data mining

Received: 10 March 2025

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	Shen Xiaojun
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Shen Xiaojun,Shen Xinyan,Yang Weixin等. Quantitative Characterization and Mining Method of Wind Turbine Output Dispersion Based on Power Scatter Points[J]. Transactions of China Electrotechnical Society, 2026, 41(4): 1311-1323.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.250385 OR https://dgjsxb.ces-transaction.com/EN/Y2026/V41/I4/1311

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