风电机组现场运行状态的高精度复现是实施性能评估与故障诊断的关键，然而实际应用中常因风速测量不准，导致数据分析与状态还原存在显著偏差，直接影响机组运维的准确性与时效性。为解决这一问题，从风电机组能量捕获与传递的整体机理出发，构建了融合物理机制与数据驱动的功率流模型，并提出一种基于二分法的风轮等效风速估计方法。研究结果表明，该方法在额定风速以下、额定风速附近及高风速区等多种运行工况下，均能实现关键状态变量 (如风轮转速、输出功率和桨距角) 的高一致性复现，其现场复现一致性系数(consistency coefficient of field reproduced，CCFR) 明显优于传统基于实测风速的方法，展现出良好的工程适用性与鲁棒性。所提出的风速估计方法有效提升了运行状态复现的准确度，为风电机组的性能评估、控制参数优化、故障预警等工程应用提供了可靠的数据基础与技术手段，具有重要的现场推广价值。

High-accuracy  reconstruction  of  the  operational  state  is  essential  for  performance  evaluation  and  fault diagnosis of wind turbines. However, in practice, inaccurate wind speed measurements often lead to significant deviations in data analysis and state reconstruction, which directly compromise the accuracy and timeliness of wind turbine operation and maintenance.  To  address  this  issue,  this  study  starts  from  the  overall  mechanism  of  energy  capture  and  transfer  in  wind turbines, establishes a power flow model that integrates physics-based principles with data-driven approaches, and proposes a bisection-based method for estimating the rotor equivalent wind speed. The results demonstrate that the proposed method achieves high-consistency reproduction of key state variables—including rotor speed, output power, and pitch angle—under a wide range of operating conditions, including below rated wind speed, near rated wind speed, and in high wind speed regions. The consistency coefficient of field reproduced (CCFR) obtained by this method significantly outperforms that of conventional  methods  relying  on directly  measured  wind  speeds,  showing  excellent  engineering  applicability  and robustness.  The  proposed  wind  speed  estimation  method  enhances  the  accuracy  of  operating  condition  reproduction  and provides  a  reliable  data  foundation  and  technical  means  for  engineering  applications  such  as  performance  assessment, control parameter optimization, and fault early warning. It holds considerable value for practical field deployment.