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At present, the energy consumption of the pumping unit represents the highest cost in operating a pumping system. Due to this reason, this paper proposes a reliable and robust solution for integrating a wind energy conversion system (WECS) into an already existing pumping system (PS), without being overly intrusive for the PS, and without altering the control strategy or the hardware of the commercial WECS. The current work begins by presenting the modeling undertaken, which includes a WECS with pitchable blades, a doubly-fed induction generator (DFIG) together with its power converter, a centrifugal pump, an induction motor (IM) with its power converter, and an irrigation network with a pressure transducer. The second part consists of the proposed three-level controller: a general controller that regulates the WECS power demand according to the pumping pressure reference; three second-level controllers; two necessary for the WECS and one for the PS; and three first level controllers, two for controlling the power converters of the WECS generator and one for controlling the pump motor power converter. Finally, the simulation results of the proposed system control strategy are presented, showing its technical feasibility and good performance.

Nowadays, the intelligent transportation concept has become one of the most important research fields. All of us depend on mobility, even when we talk about people, provide services, or move goods. Researchers have tried to create and test different transportation models that can optimize traffic flow through road networks and, implicitly, reduce travel times. To validate these new models, the necessity of having a calibration process defined has emerged. Calibration is mandatory in the modeling process because it ensures the achievement of a model closer to the real system. The purpose of this paper is to propose a new multidisciplinary approach combining microscopic traffic modeling theory with intelligent control systems concepts like fuzzy inference in the traffic model calibration. The chosen Takagi–Sugeno fuzzy inference system proves its adaptive capacity for real-time systems. This concept will be applied to the specific microscopic car-following model parameters in combination with a Kalman filter. The results will demonstrate how the microscopic traffic model parameters can adapt based on real data to prove the model validity.

This paper presents two self-tuning control structures synthesized through the minimization of two criterion functions. It is described the computation methodology of the control laws, both being particularized for the case of the synchronous generator's excitation control. The parameters estimator is considered the recursive least square error (RLSE) algorithm. In order to validate the considered control structures, two comparative study cases by computer simulation are presented.

Currently, the most implemented solution for driving a pumping system (PS) at variable speed is using a variable frequency drive (VFD). Because most of the VFDs have integrated only one proportional-integral (PI) frequency controller whose parameters (proportional gain/the integration time) can be off-line tuned but cannot be changed during real-time operation, and many PS must operate in different regimes (at variable speed, variable flow rate, at variable pumping head or even at variable power, e.g., those powered by renewable energy sources), the adaptive control strategy proposed in this paper overcomes these problems with very good performances. The proposed adaptive control strategy uses only simple PI controllers for managing several operating regimes. The adaptive character is not ensured by re-tuning the PI controller parameters as in self-tuning controllers but by readjusting the control law through a change in the control loop depending on the controlled output of the process (pump speed, pump discharge, pumping head or absorbed power). The deviations of the mentioned controlled outputs from their referenced values are converted into electrical frequency error (the deviation of the current value from its reference), which is the regular input to the already tuned controller; therefore, no controller re-tuning is required.