Microgrid state and frequency estimation using Kalman filter: an approach considering an augmented measurement Jacobian matrix.

A novel state estimation methodology is proposed in this paper for microgrids monitoring using synchronized and non-synchronized measurements. A Kalman filter model is proposed to track both system states and frequency along the time considering the measurements gathered from the grid by PMUs and smart meters allocated in the system. An augmented measurement Jacobian matrix is formed considering system states and frequency as variables to be estimated along the time. Distributed generation is connected to the grid by voltage source inverters (VSI) considering frequency and voltage droop control characteristics. A radial 18-bus test system is used for the computational simulations in order to prove the efficiency of the proposed methodology. The algorithm is tested considering microgrid in grid-connected and islanded operation modes providing satisfactory results with reduced estimation errors when compared to the RSCAD (Real-Time Simulation Software Package) software. The main contributions of this work include: (i) the explicit model of frequency and voltage droop control of VSI in a Kalman filter model; (ii) the capability of tracking microgrids frequency and system states along the time; (iii) the explicit model of derivatives of phasor measurement units and smart meters measurements with respect to the system states and frequency. [ABSTRACT FROM AUTHOR]