Automatic electric load identification in self-configuring microgrids

Abstract

A microgrid is the power system of choice for the electrification of rural areas in developing countries. It should be able to adapt to changing load situations without the need for specialists to change the configuration of the microgrid controller. This paper proposes a self-configuring microgrid management system that is able to adjust both generation and demand of the system, so that also in case of growing electricity demand the grid can still be operable by disconnecting unessential loads. A crucial task for the microgrid controller is to automatically identify the connected loads on the basis of their consumption behaviors. For this, a template-matching algorithm is proposed that is based on dynamic time warping, which is primarily used in speech recognition. It has been found that for load profile analysis, simple signal features such as the number of rising edges or the aggregated energy consumption in a given time window is sufficient to describe the signal. In contrast to speech recognition, frequency domain analysis is not necessary.