基于状态曲线分析的风力机输出功率建模

Jiaying Huang, Wangqiang Niu, Xiaotong Wang
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引用次数: 1

摘要

在风力发电中,功率曲线可以反映风力发电机组的整体发电性能。如何使功率曲线具有较高的精度和易于解释的特点是一个研究热点。针对当前功率曲线建模方法特征选择不全面的问题,为了避免特征选择,使模型易于解释,引入了风电机组的简化模型和状态曲线。提出了一种基于不同工况的功率建模方法。将风力机系统简化为叶片、机械传动和发电机三个物理模型,并用数学表达式表示能量传递。将风力机的运行过程分为恒功率(CP)、恒转速(CS)和最大功率点跟踪(MPPT)三个阶段,通过对状态曲线的分析,给出各阶段的功率表达式。通过2MW风力发电机组的监控与数据采集(SCADA)数据验证了所提方法的有效性。实验结果表明,基于状态曲线分析的功率建模方法的平均绝对百分比误差(MAPE)指数为11.56%,表明该方法的功率预测结果优于六阶多项式回归方法的平均绝对百分比误差(MAPE)指数为13.88%。结果表明,该方法具有较高的透明性和易于解释的特点。
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Output Power Modeling of Wind Turbine Based on State Curve Analysis
In wind power generation, the power curve can reflect the overall power generation performance of a wind turbine. How to make the power curve have high precision and be easy to interpret is a hot research topic. Because the current power curve modeling method is not comprehensive in feature selection, the simplified model and state curve of a wind turbine are introduced to avoid feature selection and make the model interpret easily. A power modeling method based on different working conditions is proposed. The wind turbine system is simplified into three physical models of blades, mechanical transmission and generator, and the energy transfer is expressed by mathematical expressions. The operation process of the wind turbine is divided into three phases: constant power (CP), constant speed (CS), and maximum power point tracking (MPPT), and the power expression of each phase is given after the analysis of state curves. The effectiveness of the proposed method is verified by the supervisory control and data acquisition (SCADA) data of a 2MW wind turbine. The experimental results show that the mean absolute percentage error (MAPE) index of the proposed power modeling method based on state curve analysis is 11.56%, which indicates that the power prediction result of this method is better than that of the sixth-order polynomial regression method, whose MAPE is 13.88%. The results show that the proposed method is feasible with high transparency and is interpreted easily.
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