The authors propose robust non‐linear controllers for a wind system. The proposed controllers are based on a combination of the block control linearisation and super‐twisting algorithm, as a second‐order sliding mode technique. The main control loop is designed to regulate the electromagnetic torque and stator power factor of a doubly‐fed induction generator which is connected to the grid. The generator is mechanically coupled with a DC motor, which can emulate the wind turbine operation. Therefore, an auxiliary control loop is designed to control the DC motor velocity. Additionally, a grid‐side converter controller is proposed to regulate the DC‐link voltage into AC/DC/AC converter, and displacement factor of the energy flow between the rotor and the grid. A robust stability analysis of the complete closed‐loop system under external disturbances is presented. The robustness of the proposed control schemes is validated in real time using a workbench, which consists of a motor–generator group, AC/DC/AC electronic drive and dSPACE DS 1104 controller boards.

In this paper, a real-time robust closed-loop control scheme for controlling the velocity of a Direct Current (DC) motor in a compound connection is proposed. This scheme is based on the state-feedback linearization technique combined with a second-order sliding mode algorithm, named super-twisting, for stabilizing the system and achieving control goals. The control law is designed to track a periodic square reference signal, being one of the most severe tests applied to closed-loop systems. The DC motor drives a squirrel-cage induction generator which represents the load; this generator must work above the synchronous velocity to deliver the generated power towards the grid. A classical proportional-integral (PI) controller is designed for comparison purposes of the time-domain responses with the proposed second-order sliding mode (SOSM) super-twisting controller. This robust controller uses only a velocity sensor, as is the case of the PI controller, as the time derivative of the velocity tracking variable is estimated via a robust differentiator. Therefore, the measurements of field current and stator current, the signal from a load torque observer, and machine parameters are not necessary for the controller design. The validation and robustness test of the proposed controller is carried out experimentally in a laboratory, where the closed-loop system is subject to an external disturbance and a time-varying tracking signal. This test is performed in real time using a workbench consisting of a DC motor—Alternating Current (AC) generator group, a DC/AC electronic drive, and a dSPACE 1103 controller board.

A DC motor velocity control in feedback systems usually requires a velocity sensor, which increases the controller cost. Additionally, the velocity sensor used in industrial applications presents several disadvantages such as maintenance requirements and signal conditioning. In this work, we propose a robust velocity control scheme applied to a DC motor based on estimation strategies using a sliding-mode observer. This means that measurements with mechanical sensors are not required in the controller design. The proposed observer estimates the rotational velocity and load torque of the motor. The controller design applies the exact-linearization technique combined with the super-twisting algorithm to achieve robust performance in the closed-loop system. The controller validation was carried out by experimental tests using a workbench, which is composed of a control and data acquisition Digital Signal Proccessor board, a DC-DC electronic converter, an interface board for signals conditioning, and a DC electric generator connected to an adjustable resistive load. The simulation and experimental results show a significant performance of the proposed control scheme. During tests, the accuracy, robustness, and speed response on the controller were evaluated and the experimental results were compared with a classic proportional-integral controller, which uses a conventional encoder.

The wind power systems of variable velocity using a doubly-fed induction generator dominate large-scale electrical generation within renewable energy sources. The usual control goal of the wind systems consists of maximizing the wind energy capture and streamlining the energy conversion process. In addition, these systems are an intermittent energy source due to the variation of the wind velocity. Consequently, the control system designed to establish a reliable operation of the wind system represents the main challenge. Therefore, emulating the operation of the wind turbine by means of an electric motor is a common strategy so that the controller design is focused on the induction generator and its connection to the utility grid. Thus, we propose to emulate the dynamical operation of a wind turbine through a separately excited DC motor driving by a sensor-less velocity controller. This controller is synthesized based on the state-feedback linearization technique combined with the super-twisting algorithm to set a robust closed-loop system in the presence of external disturbances. A robust velocity observer is designed to estimate the rotor velocity based on the armature current measuring. Furthermore, a robust differentiator is designed for estimating the time derivative of the velocity error variable, achieving a reduction in the computational calculus. Experimental tests were carried using a separately excited DC motor coupled with a dynamometer to validate the proposed wind turbine emulator.

Hoy en día, la mejora de la calidad de la energía ha tomado mucha relevancia debido al crecimiento sostenido de la demanda de carga eléctrica para ser abastecida con altos estándares y eficiencia. Los consumidores de energía tienen la posibilidad de mejorar sus características de carga para evitar una excesiva infraestructura de generación y distribución por parte de las empresas proveedoras energía. En este trabajo se propone un compensador estático de potencia reactiva para mejorar el factor de potencia y contribuir a una mejor regulación de voltaje en la red eléctrica. El compensador está compuesto por la red eléctrica, un filtro LCL, un inversor trifásico y un bus de CD. A partir del modelo en el marco dq, el compensador de potencia reactiva se sintetiza para regular la potencia reactiva y el voltaje del bus de CD de un inversor. La ley de control super-twisting se aplica para proporcionar robustez al desempeño del sistema y cancelar las perturbaciones externas las cuales son originadas por un flujo de potencia activa al controlar la velocidad angular de un motor de inducción con seguimiento de un tren de pulsos arriba de la velocidad de sincronismo, y se acopla en la flecha un generador de inducción que inyecta energía eléctrica a la red eléctrica, como carga del motor. Este trabajo presenta resultados experimentales que validan la robustez del sistema de control aplicado al compensador estático de potencia reactiva.

In this work, a neural super-twisting algorithm is applied to the design of a controller for a flywheel energy storage system (FESS) emulator. Emulation of the FESS is achieved through driving a Permanent Magnet Synchronous Machine (PMSM) coupled to a shaft to shaft DC-motor. The emulation of the FESS is carried out by controlling the velocity of the PMSM in the energy storage stag and then by controlling the DC-motor velocity in the energy feedback stage, where the plant’s states of both electrical machines are identified via a neural network. For the neural identification, a Recurrent Wavelet First-Order Neural Network (RWFONN) is proposed. For the design of the velocity controller, a super-twisting algorithm is applied by using a sliding surface as the argument; the latter is designed based on the states of the RWFONN, in combination with the block control linearization technique to the control of the angular velocity from both machines in their respective operation stage. The RWFONN is trained online using the filtered error algorithm. Closed-loop stability analysis is included when assuming boundedness of the synaptic weights. The results obtained from Matlab/Simulink validate the performance of the proposal in the control of an FESS.