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Wind power systems participating in primary frequency regulation have become a novel trend. In order to solve the problem of the over-speed deloading (OSD) control of wind power systems failing to provide reserved capacity for primary frequency regulation while under turbulent winds, this paper analyzes the influence mechanism of turbulence characteristics on the OSD control and the relationship between the reserve capacity of OSD control and the deloading power coefficient under turbulent wind speeds, while also quantifying the relationship between the turbulence characteristic index and deloading power coefficient. The range of the deloading power coefficient is obtained accordingly, based on which improved OSD control is proposed to dynamically optimize the deloading power coefficient according to the turbulence characteristics, which improves the frequency regulation performance of wind power systems under turbulent wind speed. According to the simulations and experimental results, the improved method proposed in this paper has good effectiveness and superiority in frequency regulation effect and rotor speed performance. [ABSTRACT FROM AUTHOR]

• Wind farm layout and collector system optimization are integrated in a double-layer joint-optimization framework. • Essential factors are modelled and integrated , including 3D seabed, multiple substations, and wind turbines' fatigue. • Layer-wise design of solving algorithms is implemented to suit the different problem natures. • The effectiveness of proposed method is verified by reducing the leverlized cost of energy on a benchmark offshore wind farm. Offshore wind power is continuously developing for renewable energy generation. Generally, the overall planning work of offshore wind farms (OWFs) is divided into wind farm layout optimization (WFLO) and wind farm collector system optimization (WFCSO), which focus on wind turbines' (WT) micro-siting and sea cables' network design, respectively. Traditionally, the optimizations on these two subproblems are isolated, which lacks the integral consideration of their co-effect on the optimal planning solution. Moreover, the practicability of these optimizations also remains at a low level, with key practical factors being largely overlooked for the sake of model simplicity. To fill these gaps, this article proposes a complete modelling of the joint-optimization problem for OWF planning with a double-layer optimization framework. It not only considers the interplay between WFLO and WFCSO, but also well integrates all the essential and practical parts together, including the impacts of 3D seabed geography, the scalability to multiple offshore substations, the reliability concerns of WTs' fatigue, and the layer-wise selection of solving algorithms to suit the different problem natures. The proposed optimization method is tested on a 50 WTs system through comparative studies, and the results verify the necessity of each designed part in the proposed joint planning model. [ABSTRACT FROM AUTHOR]

• Focus on low wind sites, a promising area for wind energy utilization in China. • Low mean wind speed decreases the acceleration or deceleration of wind turbine. • High turbulence makes it hard to track maximum power points. • The optimal torque gain decreases with increasing turbulence intensity. • The proposed MPPT control avoids the searching problem caused by wind speed changes. With the rapid development of wind power in China, high wind sites have become scarce and are often located far away from major load centers. Due to technological improvement and close proximity to load centers, low wind sites (wind speeds around 3–7 m/s) have become economically viable in China. However, the characteristics of low mean wind speed and high turbulence intensity at low wind sites make maximum power point tracking (MPPT) control challenging. Therefore, an improved maximum power point tracking (IMPPT) control approach is proposed in this paper. This approach is based on decreasing torque gain (DTG) control algorithm, and is designed to dynamically adjust the gain coefficient aimed for better energy capture at low wind sites. The proposed IMPPT algorithm is compared with traditional MPPT algorithms by simulation using actual wind speeds, including DTG control, adaptive torque gain (ATG) control, the improved MPPT control based on the reduction of tracking range (RTR-MPPT control), and an improved ATG control based on curve fit method (CF-ATG control). Simulation results verify that the proposed approach (IMPPT algorithm) is more effective. [ABSTRACT FROM AUTHOR]