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The wind turbine codes predict wind turbine loads and response in high accuracy and can be used in controller design with the development of the computer technologies. In this paper open source FAST code developed by NREL is studied. The wind turbine controller is designed in the Matlab/Simulink environment and the FAST code is modeled as an S-function. The simulation of the control algorithms with...
In order to investigate the impacts of the integration of wind farms into utilities network, it is necessary to analyze the transient performances of wind power generation systems. In this paper, an assessment of the impact that the different representations of drive-train dynamics have on the electrical transient performances of doubly fed induction generator (DFIG) wind turbines with different operationally...
With rapid development of wind power technologies and significant growth of wind power capacity installed worldwide, various wind generator systems have been developed and built. The objective of this paper is to evaluate various wind generator systems by optimization designs and comparisons. In this paper, seven variable speed constant frequency (VSCF) wind generator systems are investigated, namely...
In order to deal with the problem in which the conventional Kalman filtering may be instable or divergent when noise statistics is unknown, a new adaptive filtering is presented, which is defined as memory-attenuated least square filtering (MALSF). The error covariance is multiplied by a decay factor to avoid the divergence and an adaptive estimation for decay factor is developed, and a recursive...
The integration of Cu metallization with porous ultra low-k dielectrics requires a more robust diffusion barrier than the conventional physical vapor deposited (PVD) Ta or TaN barrier. In this work, a composite sidewall diffusion barrier consisting of dielectric/metal bilayer structure is successfully integrated in Cu/porous ultra low-k interconnects to improve the interconnect performance and reliability...
In this paper, we propose a simple but powerful neural decision-feedback equalizer trained with a fast-converging adaptive filter algorithm, which is efficient, simple, and numerically robust. The equalizer can be viewed as a neuron with fixed or adaptive sigmoidal nonlinearity. The simulation results on various time-invariant and time-varying channel equalization benchmarks have shown its surprisingly...
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