基于卡尔曼滤波器的海上风电机组阻尼子空间辨识
Damping Identification of an Operational Offshore Wind Turbine using Kalman filter-based Subspace Identification
摘要：
运行模态分析（OMA）提供了对海上风力涡轮机（OWT）结构动力学的基本见解。在这些动力学中，阻尼被认为是一个特别重要的参数，因为它决定了固有频率下的响应幅度。由于转子旋转引起的谐波激励，违反了经典OMA算法的稳态白噪声激励要求，这给运行中的OWT的识别带来了困难。最近，提出了一种新的算法，该算法通过使用卡尔曼滤波器估计谐波子信号并从响应信号中正交地去除该信号来减轻谐波，之后使用随机子空间识别算法来识别系统。在本文中，该算法进行了测试，从多兆瓦的业务OWT使用一个经济的传感器设置与两个加速度计水平的现场数据。该算法可以区分前三阶塔架弯曲模态，并通过对多个数据集进行线性Q分解，进一步提高识别结果。与已建立的谐波抑制算法，修正的最小二乘复指数和PolyMAX，进行了比较，以验证结果。
英文摘要：
Operational Modal Analysis (OMA) provides essential insights into the structural dynamics of an Offshore Wind Turbine (OWT). In these dynamics, damping is considered an especially important parameter as it governs the magnitude of the response at the natural frequencies. Violation of the stationary white noise excitation requirement of classical OMA algorithms has troubled the identification of operational OWTs due to harmonic excitation caused by rotor rotation. Recently, a novel algorithm was presented that mitigates harmonics by estimating a harmonic subsignal using a Kalman filter and orthogonally removing this signal from the response signal, after which the Stochastic Subspace Identification algorithm is used to identify the system. In this paper, the algorithm is tested on field data obtained from a multi-megawatt operational OWT using an economical sensor setup with two accelerometer levels. The first three tower bending modes could be distinguished, and, through the LQ-decomposition used in the algorithm, the identification results could be improved further by concatenating multiple datasets. A comparison against established harmonics-mitigating algorithms, Modified Least-squared Complex Exponential and PolyMAX, was done to validate the results.
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