| Title | Statistical Estimation Framework for State Awareness in Microgrids Based on IoT Data Streams |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Alavi, S. A., Rahimian, A., Mehran, K. |
| Conference Name | The 10th International Conference on Power Electronics, Machines and Drives (PEMD 2020) |
| Date Published | dec |
| Keywords | composability, Predictive Metrics, pubcrawl, Resiliency, situational awareness |
| Abstract | 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. |
| DOI | 10.1049/icp.2021.1090 |
| Citation Key | alavi_statistical_2020 |
Statistical Estimation Framework for State Awareness in Microgrids Based on IoT Data Streams