State Monitoring for Situational Awareness in Rural Microgrids Using the IoT Infrastructure
This work addresses the need for cost-effective situational awareness in rural microgrids, but the approach is incremental as it combines existing methods (event-triggered Kalman filter, LoRAWAN, Thingsboard) without novel theoretical contributions.
The paper proposes an event-triggered Kalman filter-based estimation strategy and IoT architecture using LoRAWAN for situational awareness in rural microgrids, achieving accurate state estimation with minimal installation and communication costs.
This paper presents an event-triggered estimation strategy and a data collection architecture for situational awareness (SA) in microgrids. An estimation agent structure based on the event-triggered Kalman filter is proposed and implemented for state estimation layer of the SA using long range wide area network (LoRAWAN) protocol. A setup has been developed which can provide enormous data collection capabilities from smart meters, in order to realise an adequate SA level in microgrids. Thingsboard Internet of things (IoT) platform is used for the SA visualisation with a customised dashboard. It is shown by using the developed estimation strategy, an adequate level of SA can be achieved with a minimum installation and communication cost to have an accurate average state estimation of the microgrid.