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Scientists and researchers are exploring different methods of generating and delivering electrical energy in an economical and reliable way, enabling them to generate electricity focusing on renewable energy resources. All of these possess the natural property of self-changing behavior, so the connection of these separate independent controllable units to the grid leads to uncertainties. This creates an imbalance in active power and reactive power. In order to control the active and reactive power in wind turbine generators with adjustable speed, various control strategies are used to allay voltage and current variations. This research work is focused on the design and implementation of effective control strategies for doubly fed induction generator (DFIG) to control its active and reactive power. A DFIG system with its control strategies is simulated on MATLAB software. To augment the transient stability of DFIG, the simulation results for the active and reactive power of conventional controllers are compared with three types of feed forward neural network controllers, i.e., probabilistic feedforward neural network (PFFNN), multi-layer perceptron feedforward neural network (MLPFFN) and radial basic function feedforward neural network (RBFFN) for optimum performance. Conclusive outcomes clearly manifest the superior robustness of the RBFNN controller over other controllers in terms of rise time, settling time and overshoot value.

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Abstract:The use of wireless communications for real-time control applications poses several problems related to the comparatively low reliability of the communication channels. This paper is concerned with adaptive and predictive application-level strategies for ameliorating the effects of packet losses and burst errors in industrial sampled-data Distributed Control Systems (DCSs), which are implemented via one or more wireless and/or wired links, possibly spanning multiple hops. The paper describes an adaptive compensator that reconstructs the best estimates (in a least squares sense) of a sequence of one or more missing sensor node data packets in the controller node. At each sample time, the controller node calculates the current control, and a prediction of future controls to apply over a short time horizon; these controls are forwarded to the actuator node every sample time step. A simple design method for a digital Proportional Integral Derivative (PID)-like adaptive controller is also described for use in the controller node. Together these mechanisms give robustness to packet losses around the control loop; in addition, the majority of the computational overhead resides in the controller node. An implementation of the proposed techniques is applied to a case study using a Hardware in the Loop (HIL) test facility, and favorable results (in terms of both performance and computational overheads) are found when compared to an existing robust control method for a DCS experiencing artificially induced burst errors.