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arXiv 2104.10063eess.SYcs.SY
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基于卡尔曼滤波的风力发电机交互式多模型风速估计器

Kalman-based interacting multiple-model wind speed estimator for wind turbines

Wai Hou Lio, Fanzhong Meng

摘要:

状态估计技术的使用提供了一种仅基于涡轮机的标准测量来推断转子有效风速的方法。为了便于设计和计算,这种估算器通常基于简化的涡轮机模型来构建,该涡轮机模型将涡轮机与刚性叶片连接起来。大的模型失配,特别是在功率系数,可能会导致估计性能的下降。因此,为了有效降低估计器模型中参数不确定性的不利影响,本文提出了一种基于交互式多模型自适应估计概念的风速估计器。该估计器由一组扩展的卡尔曼滤波器组成,每个滤波器模型基于不同的功率系数映射来匹配运行的涡轮机参数。随后,该算法根据每个滤波器的统计特性将其提供的风速估计值进行组合。此外,该估计器不仅可以推断转子有效风速,但也不确定的系统参数,即,功率系数。仿真结果表明,与标准的卡尔曼滤波器方法相比,该方法在转子有效风速和功率系数的估计方面有了更好的改善。

英文摘要:

The use of state estimation technique offers a means of inferring the rotor-effective wind speed based upon solely standard measurements of the turbine. For the ease of design and computational concerns, such estimators are typically built based upon simplified turbine models that characterise the turbine with rigid blades. Large model mismatch, particularly in the power coefficient, could lead to degradation in estimation performance. Therefore, in order to effectively reduce the adverse impact of parameter uncertainties in the estimator model, this paper develops a wind sped estimator based on the concept of interacting multiple-model adaptive estimation. The proposed estimator is composed of a bank of extended Kalman filters and each filter model is developed based on different power coefficient mapping to match the operating turbine parameter. Subsequently, the algorithm combines the wind speed estimates provided by each filter based on their statistical properties. In addition, the proposed estimator not only can infer the rotor-effective wind speed, but also the uncertain system parameters, namely, the power coefficient. Simulation results demonstrate the proposed estimator achieved better improvement in estimating the rotor-effective wind speed and power coefficient compared to the standard Kalman filter approach.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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