Abstract:
Due to the increase in pollution free renewable energy resources and efficient energy
storage systems, the need for a proper Microgrid control structure is increasing.
Information communication technology (ICT) plays a key role in the efficient
performance of Microgrid. With the development of new technologies like 4G, 5G, and
fast sensing devices and embedded systems, the internet of things (IoT) can play a huge
role in Power systems. The combination of Machine learning and IoT technology can
produce more efficient and accurate results in real time. In this work, IoT based
hierarchical control structure for remotely located Distribution Generation (DGs) has
been developed along with a Machine learning based island detection system. Microgrid
has two operational mode i.e. grid connected mode and islanding mode. During grid
connected mode, the voltage and frequency are utility dominant parameters, while
Microgrid injects power in the system. In islanded mode, the microgrid acts as standalone
and the main function of control is to maintain voltage and frequency into the desired
range. In this work for the primary layer, Proportion integral (PI) based current following
control algorithm was developed for a grid connected mode, whereas droop controller
was used for islanding mode operation of the microgrid. In the secondary layer, to remove
deviance of frequency and voltage occurred in the primary layer, IoT based
communication system was developed to share voltage and frequency among each DG.
The context aware policy was also applied to reduce the amount of data in the
communication channel. In tertiary layer, a remote Machine learning algorithm was
applied using cloud through which the microgrid shifts to gird connected mode or
standalone mode. The data for the training of machine learning model was taken by
simulating different islanding scenarios i.e increase in load demand, decrease in load
demand, or during faulty conditions. The proposed system was simulated on IEEE- 13
bus system in MATLAB/ SIMULINK. The Microsoft Azure cloud services were used for
the IoT and implementation of the Machine learning model.
