Browsing by Author "Pena, Ruben"

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    A Modified Multi-Winding DC-DC Flyback Converter for Photovoltaic Applications
    (2021) Pesce, Cristian; Riedemann, Javier; Pena, Ruben; Degano, Michele; Pereda Torres, Javier; Villalobos, Rodrigo; Maury, Camilo; Young, Hector; Andrade, Ivan
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    An Active/Reactive Power Control Strategy for Renewable Generation Systems
    (2021) Andrade, Ivan; Pena, Ruben; Blasco-Gimenez, Ramon; Riedemann, Javier; Jara, Werner; Pesce, Cristian
    The development of distributed generation, mainly based on renewable energies, requires the design of control strategies to allow the regulation of electrical variables, such as power, voltage (V), and frequency (f), and the coordination of multiple generation units in microgrids or islanded systems. This paper presents a strategy to control the active and reactive power flow in the Point of Common Connection (PCC) of a renewable generation system operating in islanded mode. Voltage Source Converters (VSCs) are connected between individual generation units and the PCC to control the voltage and frequency. The voltage and frequency reference values are obtained from the P-V and Q-f droop characteristics curves, where P and Q are the active and reactive power supplied to the load, respectively. Proportional-Integral (PI) controllers process the voltage and frequency errors and set the reference currents (in the dq frame) to be imposed by each VSC. Simulation results considering high-power solar and wind generation systems are presented to validate the proposed control strategy.
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    Sensorless Control for a Switched Reluctance Wind Generator, Based on Current Slopes and Neural Networks
    (2009) Echenique Subiabre, Estanislao Juan Pablo.; Dixon, Juan; Cardenas, Roberto; Pena, Ruben
    In this paper, the analysis, design, and implementation of a novel rotor position estimator for the control of variable-speed switched reluctance generators (SRGs) are presented. The rotor position is obtained using the unsaturated instantaneous inductance. This unsaturated inductance is estimated calculating the slope of the phase current and using a reduced-size neural network (NN) whose inputs are the average current and the saturated inductance. The proposed estimator requires less processing time than traditional methods and can be fully implemented using a low-cost DSP with very few additional analog/digital components. The rotor position estimator presented in this paper can be applied to a wind energy conversion system where the SRG is used as a variable-speed generator. This application is currently being studied because the SRG has well-known advantages such as robustness, low manufacturing cost, and good size-to-power ratio. Simulation and experimental results are presented using a 2.5-kW 8/6-SRG prototype.