Design of an Active Damping System for Vibration Control of Wind Turbine Towers

The vibration of wind turbine towers is relevant to the reliability of the wind turbine structure and the quality of power production. It produces both ultimate loads and fatigue loads threatening structural safety. This paper aims to reduce vibration in wind turbine towers using an active damper named the twin rotor damper (TRD). A single degree of freedom (SDOF) oscillator with the TRD is used to approximate the response of wind turbines under a unidirectional gusty wind with loss of the electrical network. The coincidence between the wind gust and the grid loss is studied to involve the maximum loading on the structure. The performance of the proposed damping system under the maximum loading is then evaluated on the state-of-the-art wind turbine NREL 5 MW. The effectiveness of the TRD is compared to a passive tuned mass damper (TMD) designed with similar requirements. The numerical results reveal that, at the 1st natural mode, the TRD outperforms the passive TMD by three to six times. Moreover, the results show that the TRD is effective in reducing ultimate loads on wind turbine towers.

Day-Ahead Forecasting of Losses in the Distribution Network

Utility companies in the Nordics have to nominate how much electricity is expected to be lost in their power grid the next day. We present a commercially deployed machine learning system that automates this day-ahead nomination of the expected grid loss. It meets several practical constraints and issues related to, among other things, delayed, missing and incorrect data and a small data set. The system incorporates a total of 24 different models that performs forecasts for three sub-grids. Each day one model is selected for making the hourly day-ahead forecasts for each sub-grid. The deployed system reduced the mean average percentage error (MAPE) with 40% from 12.17 to 7.26 per hour from mid-July to mid-October, 2019. It is robust, flexible and reduces manual work. Recently, the system was deployed to forecast and nominate grid losses for two new grids belonging to a new customer. As the presented system is modular and adaptive, the integration was quick and needed minimal work. We have shared the grid loss data-set on Kaggle.

Image restoration for irregular holes based on dual discrimination generation countermeasure network

In order to solve the problem that the global and local generated countermeasure network cannot inpaint the random irregular large holes, and to improve the standard convolution generator, which demonstrates the defects of color difference and blur, a network architecture of inpainting irregular large holes in an image based on double discrimination generation countermeasure network is proposed. Firstly, the image generator is a U-net architecture defined by partial convolution. The normalized partial convolution only completes the end-to-end mask update for the effective pixels. The skip link in U-net propagates the context information of the image to the higher resolution, and optimizes the training results of the model with the weighted loss function of reconstruction loss, perception loss and wind grid loss. Subsequently, the adversary loss function, the dual discrimination network including the synthetic discriminator and the global discriminator are trained separately to judge the consistency between the generated image and the real image. Finally, the weighted loss functions are trained together with generating network and double discrimination network to further enhance the detail and overall consistency of the inpainted area and make the inpainted results more natural. The simulation experiment is carried out on the Place 365 standard database. The subjective and objective experimental results show that the results of the proposed method has reasonable overall and detail semantic consistency than those of the existing methods when they are used to repair random, irregular and large-area holes. The proposed method effectively overcomes the defects of blurry details, color distortion and artifacts.