Advanced Kalman Filter-Based Backstepping Control of AC Microgrids: A Command Filter Approach

The stability of alternating current microgrids (ac MGs) is significantly affected by the procedure of collecting precise and sufficient information of the system and tightly controlling power inverters. Whereas using many sensors increases ac MG ripples and cost, integrating cost-effective and low number of sensors is preferred. Furthermore, assuring the stability and tracking issue of ac MGs in different operating modes and in the presence of unknown time-varying loads is a hard task. Aiming at these issues, this article proposes an improved augmented-Kalman filter to estimate the state vector and disturbance inputs and a nonlinear backstepping controller with a command filter to design the control law. Compared to the conventional Kalman filters, the developed approach is able to estimate the external disturbances, which improves the state estimation performance and provides extra information about the power system. The proposed command filter-based backstepping has the key feature of avoiding the calculation of time-derivatives of desired references of virtual inputs, which is a common drawback of conventional approaches. Whereas the dynamics of the disturbance time-varying load are not available, the command filter is utilized to avoid the time derivatives terms of the disturbance inputs. Simulation results illustrate the estimation performance of the augmented Kalman filter and the tracking performance of the command filter-based backstepping controller.

Automated summary

This article proposes an improved augmented-Kalman filter to estimate the state vector and disturbance inputs and a nonlinear backstepping controller with a command filter to design the control law. The developed approach is able to estimate the external disturbances, which improves the state estimation performance and provides extra information about the power system.