Microgrid Optimal State Estimation Over IoT Wireless Sensor Networks With Event-Based Measurements

TL;DR

This paper presents a real-time, event-based linear state estimator for microgrids using wireless sensor networks, reducing communication load and computational costs through a send-on-delta strategy and IoT implementation.

Contribution

It introduces a novel event-based state estimation method for microgrids that leverages send-on-delta measurements and a Kalman filter, with a practical IoT prototype using LoRaWAN.

Findings

Reduced communication load due to event-triggered data transmission

Effective real-time state estimation with low computational resources

Successful implementation on a LoRaWAN-based IoT platform

Abstract

In a microgrid, real-time state estimation has always been a challenge due to several factors such as the complexity of computations, constraints of the communication network and low inertia. In this paper, a real-time event-based optimal linear state estimator is introduced, which uses the send-on-delta data collection approach over wireless sensors networks and exhibits low computation and communication resources cost. By employing the send-on-delta event-based measurement strategy, the burden over the wireless sensor network is reduced due to the transmission of events only when there is a significant variation in the signals. The state estimator structure is developed based on the linear Kalman filter with the additional steps for the centralized fusion of events data and optimal reconstruction of signals by projection onto convex sets. Also for the practical feasibility analysis, this paper developed an Internet of things prototype platform based on LoRaWAN protocol that satisfies the requirements of the proposed state estimator in a microgrid.