Date of Award

2016

Document Type

Open Access Master's Thesis

Degree Name

Master of Science in Electrical Engineering (MS)

Administrative Home Department

Department of Electrical and Computer Engineering

Advisor 1

Wayne W. Weaver

Committee Member 1

Nina Mahmoudian

Committee Member 2

John Lukowski

Abstract

Microgrids are power systems that work not only from the main power grid, but also in island mode operation. As each of the power sources and loads have voltage and current limitations, the autonomous microgrid should be able to control voltage and current levels while loads and sources are connected to the grid. Therefore, the microgrid bus voltage remains constant and power transfers safely.

To configure an autonomous microgrid, the load and source type identification comes to mind. An autonomous microgrid needs to know the type of the electrical components which are going to be connected to the grid. In this study, by using voltage trend recognition, the type of the electrical device (DC power supply, battery, and load) is identified. Moreover, by recognizing the battery state of charge (SOC), the electrical power management of the microgrid is optimized.

The experimental setup is able to detect the type of the electrical device (battery, voltage source or load) and then configures the DC microgrid to transfer power efficiently. By implementing PI controller and fuzzy method to control the DC microgrid, various cases in the microgrid such as load sharing, charging the battery, powering the different loads (uncharged battery, DC motor, and resistive loads) and importing new sources and xi loads are used to test the experimental setup of the DC microgrid and related power electronics converters. The experimental results demonstrate the validity and efficiency of the proposed system for the self-configurable autonomous DC microgrid.

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