Aman Abdulla Tanvir
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Aman Abdulla Tanvir has a total of 2 co-authors. Here are the top co-authors by number of shared publications:
- Adel Merabet
Division of Engineering, Saint Mary’s University, Halifax, NS B3H 3C3, Canada
2 shared publications - Rachid Beguenane
Department of Electrical and Computer Engineering, Royal Military College, Kingston, ON K7K 7B4, Canada
1 shared publication
Latest Publications
- Open Access
Artificial Neural Network and Kalman Filter for Estimation and Control in Standalone Induction Generator Wind Energy DC Microgrid
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.
Open AccessArtificial Neural Network and Kalman Filter for Estimation and Control in Standalone Induction Generator Wind Energy DC Microgrid
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.
- Open Access
Real-Time Control of Active and Reactive Power for Doubly Fed Induction Generator (DFIG)-Based Wind Energy Conversion System
This paper presents the modeling, rapid control prototyping, and hardware-in-the-loop testing for real-time simulation and control of a grid-connected doubly fed induction generator (DFIG) in a laboratory-size wind turbine emulator for wind energy conversation systems. The generator is modeled using the direct-quadrature rotating reference frame circuit along with the aligned stator flux, and the field-oriented control approach is applied for independent control of the active and reactive power and the DC-link voltage at the grid side. The control of the active, reactive power and the DC-link voltage are performed using a back-to-back converter at sub- and super-synchronous as well as at variable speeds. The control strategy is experimentally validated on an emulated wind turbine driven by the Opal-RT real-time simulator (OP5600) for simultaneous control of the DC-link voltage, active and reactive power.
Open AccessReal-Time Control of Active and Reactive Power for Doubly Fed Induction Generator (DFIG)-Based Wind Energy Conversion System
This paper presents the modeling, rapid control prototyping, and hardware-in-the-loop testing for real-time simulation and control of a grid-connected doubly fed induction generator (DFIG) in a laboratory-size wind turbine emulator for wind energy conversation systems. The generator is modeled using the direct-quadrature rotating reference frame circuit along with the aligned stator flux, and the field-oriented control approach is applied for independent control of the active and reactive power and the DC-link voltage at the grid side. The control of the active, reactive power and the DC-link voltage are performed using a back-to-back converter at sub- and super-synchronous as well as at variable speeds. The control strategy is experimentally validated on an emulated wind turbine driven by the Opal-RT real-time simulator (OP5600) for simultaneous control of the DC-link voltage, active and reactive power.