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One of the most important points of the doubly fed induction generator operation is to control the active and reactive stator power when voltage dips occur. With this objective in mind, a model based predictive control mapped and reproduced by an artificial neural network of multilayer perceptron topology is proposed. The motivation is to reproduce the characteristic of predicting the future behavior...
The paper presents nonlinear model predictive control of a doubly fed induction generator (DFIG). The stator power is controlled considering stator voltage dip occurrences as well as rotor speed variation. The nonlinear predictive controller rejects the active and reactive stator power errors by predicting the optimized control voltage over the next sample time and providing reactive power to the...
Distributed generation systems have been widely implemented in power systems all over the world, and so have the non-linear loads. Then, it is worthwhile to analyze the possibility of providing these generation systems with power conditioning functionalities. In these terms, this work presents a DFIG-based wind power distributed generation system with active filtering and utility voltage regulation...
Doubly-Fed Induction Generators (DFIG) are widely used nowadays in grid-connected Wind Turbine Systems (WTS). The typical control strategy for WTS is the maximum power coefficient tracking method. However, this method limits desirable ancillary services from WTS, such as power quality improvement in the network. Therefore this paper derives the optimal reference power coefficient of the WTS that can...
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