多随机激励下风电机组在线辨识建模研究

This proposal aims to identify model parameters for wind turbine generators(WTGs) by online identification algorithms. It studies the model parameter identifiability and how to identify them for each component (wind turbine, generator/converter,converter control) of WTGs following multiple stochastic excitations. Firstly, the measured ambient data excited by wind turbulence and load random variations are decomposed into different time-scale components, with the purpose of making the fast and slow model parameters to be identified separately. Secondly, the parameter identifiability of WTGs is studied. Thirdly, the sensitivity index, which takes into account both frequency-domain sensitivity and power spectral density of input signals is proposed to measure the difficulty of identification, which means the possibility of obtaining the accurate values for the parameters. Fourthly, the two step identification process is presented: (1) use the stochastic subspace identification (SSI) to estimate the parameters corresponding to fast/slow dynamic performance; (2)apply the coordinate optimization technique to get all parameters. Lastly, the identified results following severe disturbances are used as references to validate the parameters identified from ambient data. This project attempts to attain accurate representation of WTG model during system operation, and to provide the proper values for dynamic performance analysis of wind power interconnected systems.

本项目以在线辨识风电机组各组成模块的模型参数为目标，以构建有效的参数辨识算法为基础，研究多随机激励下，风电机组的驱动系统、风力发电机、风电控制系统的参数是否可以辨识、如何辨识的机理。根据激励信号的时间尺度及风电机组快变/慢变动态特性的对应关系，将受扰轨线进行多尺度分解，解耦辨识与快变/慢变动态对应的模型参数；基于此，分析多随机激励下风电机组参数的可辨识性；提出综合考虑频域灵敏度与激励信号强度的功率谱灵敏度指标，分析风电机组参数辨识的难易度；采用2步辨识的思路，先将风电机组模型参数在时间尺度上解耦辨识，再协调优化获得风电机组的所有参数；以大扰动激励下的参数辨识结果为基准，校核并修正小扰动激励下的辨识值。本研究将确立符合实际运行工况的风电机组模型参数，为大规模风电场并网研究提供有效的参数。

大规模风电并网给电力系统安全稳定运行带来了重要影响。目前亟需建立风电机组的通用模型，实测其模型参数，为我国风力发电大规模接入电网提供可信的分析基础。本项目的主要内容包括：. 1、构建了风电机组动态仿真推荐模型。在全面梳理现有风电机组模型基础上，针对电力系统不同时间尺度动态仿真的需求，通过奇异摄动理论和模式分析，给出了适合电力系统电磁暂态、机电暂态以及中长期动态仿真分析的风电机组推荐模型。. 2、研发了风电机组的参数辨识技术体系。提出了风电机组“分合协调”参数辨识策略；发明了基于湍流风激励的风电机组参数辨识技术。. 3、基于国内主流风电机组，提出了参数实测技术及模型验证方案，计算了仿真与实测偏差指标。 模型验证结果表明，本课题构建的风电机组通用模型结构能表征实际风电机组的主导动态，所提出的建模技术具有较高的精度。. 在本项目的资助下，出版专著1部，发表论文12篇，其中SCI论文1篇，EI论文11篇，申请发明专利6项。