基于改进的最大似然法识别风力发电机转子的载荷参数

风机不对中故障是多载荷参数耦合而成,工程师难以凭经验了解载荷参数的大致范围。针对因转子结构参数和测试条件等不确定性参数的存在及载荷参数先验信息未知,最大似然法识别转子不对中故障的载荷参数时采用的搜索计算面临计算量大,且迭代过程中灵敏度会带来一些数值问题。引入搜索区间进退法到敏感矩阵法与最大似然法中,对最大似然法进行改进。改进后的最大似然法中的迭代适于处理复杂工程优化问题,避免传统迭代数值法对搜索空间的苛刻要求。以输入尺寸和输出测试响应具有随机性测量误差的风力发电机转子系统为例,在三种测量误差下的识别结果表明,改进的最大似然法提高了最大似然法的辨识效果,可以减少不确定性因素对识别结果的影响,鲁棒性好。

Identification of load parameters of wind turbine rotor based on improved maximum likelihood method

The misalignment of wind turbines is caused by the coupling of multiple load parameters, and it is difficult for engineers to understand the approximate range of load parameters by experience. For the existence of uncertain parameters,such as rotor structural parameters, test conditions and the prior information of load parameters are unknown, the maximum likelihood method for identifying the load parameters of rotor misalignment faults is computationally intensive, and the sensitivity during iteration will bring some numerical problems.By introducing the search interval back and forth method to the sensitive matrix method and the maximum likelihood method, the maximum likelihood method was improved. And the iterative method in the maximum likelihood method is suitable for dealing with complex engineering optimization problems, avoiding the harshness of traditional iterative numerical methods on search space. The wind turbine rotor system with input size and output test response with random measurement error was taken as an example,the recognition results under three kinds of measurement errors show that the improved maximum likelihood method improves the recognition effect of the maximum likelihood method, and can reduce the influence of uncertainty factors on the recognition results, and the robustness is good.