Back-to-back Converter Control of Grid-connected Wind Turbine to Mitigate Voltage Drop Caused by Faults

Power electronic converters enable wind turbines, operating at variable speed, to generate electricity more efficiently. Among variable speed operating turbine generators, permanent magnetic synchronous generator (PMSG) has got more attentions due to low cost and maintenance requirements. In addition, the converter in a wind turbine with PMSG decouples the turbine from the power grid, which favors them for grid codes. In this paper, the performance of back-to-back (B2B) converter control of a wind turbine system with PMSG is investigated on a faulty grid. The switching strategy of the grid side converter is designed to improve voltage drop caused by the fault in the grid while the maximum available active power of wind turbine system is injected to the grid and the DC link voltage in the converter is regulated. The methodology of the converter control is elaborated in details and its performance on a sample faulty grid is assessed through simulation

A Highly Reliable Propulsion System with Onboard Uninterruptible Power Supply for Train Application:Topology and Control

Providing uninterrupted electricity service aboard the urban trains is of vital importance not only for reliable signaling and accurate traffic management but also for ensuring the safety of passengers and supplying emergency equipment such as lighting and signage systems. Hence, to alleviate power shortages caused by power transmission failures while the uninterruptible power supplies installed in the railway stations are not available, this paper suggests an innovative traction drive topology which is equipped by an onboard hybrid energy storage system for railway vehicles. Besides, to limit currents magnitudes and voltages variations of the feeder during train acceleration and to recuperate braking energy during train deceleration, an energy management strategy is presented. Moreover, a new optimal model predictive method is developed to control the currents of converters and storages as well as the speeds of the two open-end-windings permanent-magnet-synchronous-machines in the intended modular drive, under their constraints. Although to improve control dynamic performance, the control laws are designed as a set of piecewise affine functions from the control signals based on an offline procedure, the controller can still withstand real-time non-measurable disturbances. The effectiveness of proposed multifunctional propulsion topology and the feasibility of the designed controller are demonstrated by simulation and experimental results

A Machine Learning Method for Modeling Wind Farm Fatigue Load

Wake steering control can significantly improve the overall power production of wind farms. However, it also increases fatigue damage on downstream wind turbines. Therefore, optimizing fatigue loads in wake steering control has become a hot research topic. Accurately predicting farm fatigue loads has always been challenging. The current interpolation method for farm-level fatigue loads estimation is also known as the look-up table (LUT) method. However, the LUT method is less accurate because it is challenging to map the highly nonlinear characteristics of fatigue load. This paper proposes a machine-learning algorithm based on the Gaussian process (GP) to predict the farm-level fatigue load under yaw misalignment. Firstly, a series of simulations with yaw misalignment were designed to obtain the original load data, which considered the wake interaction between turbines. Secondly, the rainflow counting and Palmgren miner rules were introduced to transfer the original load to damage equivalent load. Finally, the GP model trained by inputs and outputs predicts the fatigue load. GP has more accurate predictions because it is suitable for mapping the nonlinear between fatigue load and yaw misalignment. The case study shows that compared to LUT, the accuracy of GP improves by 17% (RMSE) and 0.6% (MAE) at the blade root edgewise moment and 51.87% (RMSE) and 1.78% (MAE) at the blade root flapwise moment