This paper presents an event-triggered statistical 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 provides enormous data collection capabilities from smart meters in order to realize an adequate level of SA in microgrids. Thingsboard Internet of things (IoT) platform is used for the SA visualization with a customized dashboard. It is shown that 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.
|Title of host publication||The 10th International Conference on Power Electronics, Machines and Drives (PEMD 2020) - Proceedings|
|Number of pages||6|
|Publication status||Published (in print/issue) - Jul 2021|
|Event||The 10th International Conference on Power Electronics, Machines and Drives (PEMD 2020) - Online|
Duration: 15 Dec 2020 → 17 Dec 2020
|Conference||The 10th International Conference on Power Electronics, Machines and Drives (PEMD 2020)|
|Period||15/12/20 → 17/12/20|
Bibliographical notePublisher Copyright:
© PEMD 2020.All right reserved.
- Event-triggered Kalman filter
- Situational awareness
- Statistical estimation