Effect of doubly fed induction generatortidal current turbines on stability of a distribution grid under unbalanced voltage conditions

This paper analyses the effects of doubly fed induction generator (DFIG) tidal current turbines on a distribution grid under unbalanced voltage conditions of the grid. A dynamic model of an electrical power system under the unbalanced network is described in the paper, aiming to compare the system performance when connected with and without DFIG at the same location in a distribution grid. Extensive simulations of investigating the effect of DFIG tidal current turbine on stability of the distribution grid are performed, taking into account factors such as the power rating, the connection distance of the turbine and the grid voltage dip. The dynamic responses of the distribution system are examined, especially its ability to ride through fault events under unbalanced grid voltage conditions. The research has shown that DFIG tidal current turbines can provide a good damping performance and that modern DFIG tidal current power plants, equipped with power electronics and low-voltage ride-through capability, can stay connected to weak electrical grids even under the unbalanced voltage conditions, whilst not reducing system stability

The influence of power-take-off control on the dynamic response and power output of combined semi-submersible floating wind turbine and point-absorber wave energy converters

Floating offshore wind turbines (FOWTs) have received extensive attention in recent years, particularly after the successful demonstration of several pilot projects, such as Hywind and WindFloat. Integrating wave energy converters (WECs) into FOWTs could potentially help reduce cost of energy by absorbing additional power from waves and introduce restoring moments and extra damping to the floating platform thus reducing motion responses and fatigue loads. In this work, we propose a hybrid floating wind and wave power generation platform, consisting of a semi-submersible FOWT and three point-absorber WECs. Preliminary feasibility study of this concept is performed with verified integrated aero-hydro-servo-mooring numerical simulations. Dynamic response and power output of this hybrid concept are evaluated under several typical environmental conditions. Particularly, different WEC power-take-off control strategies have been comparatively studied, which have shown considerable influences on the platform dynamics and power generation. More specifically, reactive control generally worsen the platform motion responses, while spring–damping control is able to mitigate the pitch motion to certain extent. Regarding power output, reactive control leads to the highest wave power generation, almost twice as much as that of spring–damping, which has been used in most existing works on hybrid power generation system. Moreover, it is found the optimal control design for point-absorber WEC attached to fixed structures is no longer optimal for the combined floating wind and wave energy production platform, which needs further investigations in the future

Empirical Study and Improvement on Deep Transfer Learning for Human Activity Recognition

Human activity recognition (HAR) based on sensor data is a significant problem in pervasive computing. In recent years, deep learning has become the dominating approach in this field, due to its high accuracy. However, it is difficult to make accurate identification for the activities of one individual using a model trained on data from other users. The decline on the accuracy of recognition restricts activity recognition in practice. At present, there is little research on the transferring of deep learning model in this field. This is the first time as we known, an empirical study was carried out on deep transfer learning between users with unlabeled data of target. We compared several widely-used algorithms and found that Maximum Mean Discrepancy (MMD) method is most suitable for HAR. We studied the distribution of features generated from sensor data. We improved the existing method from the aspect of features distribution with center loss and get better results. The observations and insights in this study have deepened the understanding of transfer learning in the activity recognition field and provided guidance for further research