A Comprehensive Control Scheme for the Effective Power Management Based on Power Pools in DC Microgrid

Currently, high-performance power conversion requirements are of increasing interest in microgrid applications. In fact, isolated bidirectional dc-dc converters are widely used in modern dc distribution systems. The dual active bridge (DAB) dc-dc converter is identified as one of the most promising converter topology for the mentioned applications, due to its benefits of high power density, electrical isolation, bidirectional power flow, zero-voltage switching, and symmetrical structure. This study presents a power management control scheme in order to ensure the power balance of a dc microgrid in stand-alone operation, where the renewable energy source (RES) and the battery energy storage (BES) unit are interfaced by DAB converters. The power management algorithm, as introduced in this work, selects the proper operation of the RES system and BES system, based on load/generation power and state-of-charge of the battery conditions. Moreover, a nonlinear robust control strategy is proposed when the DAB converters are in voltage-mode-control in order to enhance the dynamic performance and robustness of the common dc-bus voltage, in addition to overcoming the instability problems that are caused by constant power loads and the dynamic interactions of power electronic converters. The simulation platform is developed in MATLAB/Simulink, where a photovoltaic system and battery system are selected as the typical RES and BES, respectively. Assessments on the performance of the proposed control scheme are conducted. Comparisons with the other control method are also provided.

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Artificial Neural Network and Kalman Filter for Estimation and Control in Standalone Induction Generator Wind Energy DC Microgrid

Energies ◽

10.3390/en13071743 ◽

2020 ◽

Vol 13 (7) ◽

pp. 1743 ◽

Cited By ~ 3

Author(s):

Aman A. Tanvir ◽

Adel Merabet

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Kalman Filter ◽

Wind Energy ◽

Power Management ◽

Induction Generator ◽

Rotor Speed ◽

Dc Microgrid ◽

Control Scheme ◽

Rotor Flux

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.

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