Artificial Neural Network and Kalman Filter for Estimation and Control in Standalone Induction Generator Wind Energy DC Microgrid
Aman A. Tanvir and
Adel Merabet
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Aman A. Tanvir: Division of Engineering, Saint Mary’s University, Halifax, NS B3H 3C3, Canada
Adel Merabet: Division of Engineering, Saint Mary’s University, Halifax, NS B3H 3C3, Canada
Energies, 2020, vol. 13, issue 7, 1-16
Abstract:
This paper presents an improved estimation strategy for the rotor flux, the rotor speed and the frequency required in the control scheme of a standalone wind energy conversion system based on self-excited three-phase squirrel-cage induction generator with battery storage. At the generator side control, the rotor flux is estimated using an adaptive Kalman filter, and the rotor speed is estimated based on an artificial neural network. This estimation technique enhances the robustness against parametric variations and uncertainties due to the adaptation mechanisms. A vector control scheme is used at the load side converter for controlling the load voltage with respect to amplitude and frequency. The frequency is estimated by a Kalman filter method. The estimation schemes require only voltage and current measurements. A power management system is developed to operate the battery storage in the DC-microgrid based on the wind generation. The control strategy operates under variable wind speed and variable load. The control, estimation and power management schemes are built in the MATLAB/Simulink and RT-LAB platforms and experimentally validated using the OPAL-RT real-time digital controller and a DC-microgrid experimental setup.
Keywords: induction generator; estimation; control; artificial neural network; Kalman filter; rotor speed; frequency; power management; DC-microgrid (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (8)
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