Because wind power is connected to the grid on a large scale, frequency fluctuation in the power grid, which is defined as a system safety risk to the power grid, occurs from time to time. According to the grid code rules of China, wind turbines are required to be equipped with primary frequency modulation or inertia response control capability, which are used to support the safe and stable operation of the power grid. During the traditional frequency modulation process of the wind turbine, power limiting operation or pitch angle reservation is generally adopted to ensure that the reserved energy can be released at any time to support the frequency change in the power grid. However, the frequency support method leads to a large loss of power generation, and does not consider the coordination between mechanical load characteristics control and primary frequency modulation. In this paper, a mechanical load optimization control strategy for a wind turbine during the primary frequency modulation process, based on LIDAR (light detection and ranging) feed forward control technology, is proposed and verified. Through LIDAR feed forward control, the characteristics of incoming wind speed can be sensed in advance, with the consequence that the wind turbine can participate in, and actively control, the primary frequency modulation procedure. According to the characteristics of incoming wind, for instance the amplitude and turbulence, simultaneously, the size of the reserved pitch angle can be adjusted in real time. This kind of method, coordinating with the mechanical load of the wind turbine, can be used to reduce both the ultimate load and fatigue damage as much as possible. Finally, the mechanical load characteristics of the wind turbine with and without the control strategy are compared and studied through simulation. The research results show that the load optimization control strategy based on LIDAR feed-forward control technology can effectively reduce the fatigue and ultimate loads of the wind turbine while supporting the frequency change in the power grid; especially for the fatigue load of tower base tilt and roll bending moments, the reducing proportion will be about 4.3% and 6.3%, respectively.

Compound faults are common in wind turbine gearbox due to complex structure and harsh operating condition, which are represented by the bearing-gear fault and gear-gear fault etc . It is a challenging task to accurately detect multiple faults from on-site vibration signal potentially contaminated by intensive background noise. To address this issue, a multi-periodicity induced sparse-fidelity representation model is proposed in this paper. The proposed method is based on the periodicity-induced overlapping group shrinkage (POGS) model with the constraint of the sparsity within and across groups (SWAG). The l_p -norm and proposed reweighted minimax-concave penalty function with adjustable order are adopted for SWAG constraints. The hanning sequence is utilized as the periodicity-induced sequence in the POGS model. The closed-form solution of the proposed method is deduced using the majorization minimization algorithm. Eventually, a weighted strategy for the decomposed results is inferred to prevent over-decomposition issue. The proposed method is validated by simulation signals, experimental signal and on-site signal of actual wind turbine, which indicate that the characteristics of compound fault can be extracted plainly and the energy-loss is reduced effectively.