A Distributed Control Scheme Using SiC-Based Low Voltage Ride-Through Compensator for Wind Turbine Generators

As the penetration of renewable energy power generation, such as wind power generation, increases low-voltage ride-through (LVRT), control is necessary during grid faults to support wind turbine generators (WTGs) in compensating reactive current to restore nominal grid voltages, and maintain a desired system stability. In contrast to the commonly used centralized LVRT controller, this study proposes a distributed control scheme using a LVRT compensator (LVRTC) capable of simultaneously performing reactive current compensation for doubly-fed induction generator (DFIG)-, or permanent magnet synchronous generator (PMSG)-based WTGs. The proposed LVRTC using silicon carbide (SiC)-based inverters can achieve better system efficiency, and increase system reliability. The proposed LVRTC adopts a digital control scheme and dq-axis current decoupling algorithm to realize simultaneous active/reactive power control features. Theoretical analysis, derivation of mathematical models, and design of the control scheme are initially conducted, and simulation is then performed in a computer software environment to validate the feasibility of the system. Finally, a 2 kVA small-scale hardware system with TI’s digital signal processor (DSP) as the control core is implemented for experimental verification. Results from simulation and implementation are in close agreement, and validate the feasibility and effectiveness of the proposed control scheme.

Use of Learning Mechanisms to Improve the Condition Monitoring of Wind Turbine Generators: A Review

The main goal of this paper is to review and evaluate how we can take advantage of state-of-the-art machine learning techniques and apply them in wind energy operation conditions monitoring and fault diagnosis, boosting wind turbines’ availability. To accomplish this, we focus our work on analysing the current techniques in predictive maintenance, which are aimed at acting before a major failure occurs using condition monitoring. In particular, we start framing the predictive maintenance problem as an ML problem to detect patterns that indicate a fault on turbine generators. Then, we extend the problem to detect future faults. Therefore, this review will consist of analysing techniques to tackle the challenges of each machine learning stage, such as data pre-processing, feature engineering, and the selection of the best-suited model. By using specific evaluation metrics, the expected final result of using these techniques will be an improvement in the early prediction of a future fault. This improvement will have an increase in the availability of the turbine, and therefore in energy production.

A new design of control & power management strategies of hybrid ac-dc microgrids toward high power quality

The micro grid idea provides for the lack of several reversing switches to unitary AC-DC grid that enables connection and charges (loads) to the electrical systems with changeable regenerative AC and CC sources. Safe operation and gadget safety involve digital integration with utilities/grid through power converters. Enhanced client reliability, decreased input losses, local voltages are supported, and waste heat efficiency increased, voltage drop or interruptible supply of electricity can be customized to satisfy their unique customer demands. Work at present Analyses the performance in grid tie mode of hybrid AC/DC systems. Here are PV systems, PV systems, For the construction of microgrids wind turbine generators and batteries are employed. Convert procedures for the correct coordination of AC sub-grids to DC subs-grids have also been established for converters. MATLAB/SIMULINK environment results are generated.

Fault diagnosis of wind turbine generator bearings using fast spectral correlation

Bearings are the critical parts that support the rotating of rotor of wind turbine generators. Due to high speed revolution and affected by potential misalignment between rotor and the high speed shaft in wind turbine gearbox, the fault ratio in wind turbine generator bearings is high. Once the bearings fail, it will cause gap eccentricity, even rub, or sweeping chamber between rotor and stator. Under fault conditions, the vibration signals from rotating machinery exhibits distinct second cyclostationarity. In the light of this, the fast spectral correlation based method is applied to the fault extraction of bearings in wind turbine generators. Through converting conventional correlation into summation algorithm, the computational cost is reduced largely, meanwhile, the diagnosis accuracy is guaranteed. The effectiveness of the method in this paper is verified by two fault cases from on-site wind turbines.

Performance Evaluation of Grid-Connected Wind Turbine Generators

The risk of oscillation of grid-connected wind turbine generators (WTGs) is well known, making it all the more important to understand the characteristics of different WTGs and analyze their performance so that the problems’ causes are identified and resolved. While many studies have evaluated the performance of grid-connected WTGs, most lack clarity and precision in the modeling and simulation techniques used. Moreover, most of the literature focuses on a single mode of operation of WTGs to analyze their performances. Therefore, this paper updates the literature by considering the different operating conditions for WTGs. Using MATLAB/SIMULINK it expands the evaluation to the full range of vulnerabilities of WTGs: from the wind turbine to grid connection. A network representing grid-connected squirrel-cage induction generator (SCIG) and doubly-fed induction generator (DFIG) wind turbines are selected for simulation. The performances of SCIG and DFIG wind turbines are evaluated in terms of their energy generation capacity during constant rated wind speed, variable wind speed, and ability of fault-ride through during dynamic system transient operating conditions. The simulation results show the performance of DFIG is better than SCIG in terms of its energy generation capacity during variable wind speed conditions and active and reactive power control capability during steady-state and transient operating conditions. As a result, DFIG wind turbine is more suitable for large-scale wind power plants connected to weak utility grid applications than SCIG.