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The control system design of grid-forming (GFM) converters requires prior knowledge of grid parameters such as grid impedance, grid equivalent electromagnetic force (EMF), and short-circuit ratio (SCR), which are normally time-variant and preferably estimated in real time. However, existing estimation methods are either unable to estimate multiple parameters simultaneously or involve complex calculation algorithms for nonintrusive estimations. To address this issue, this study proposes a real-time grid parameter estimation method for GFM converters. Without introducing disturbances into the system, multiple parameters can be simultaneously estimated during the transient state with a simple algorithm, while the effects of the virtual impedance of the GFM converter and the output filter are both considered. In addition, a parameter estimation-based adaptive synchronous power control (SPC) is proposed, which exhibits improved dynamic performance and enhanced robustness of the GFM converter under various grid conditions. The validity of the proposed method is verified by simulation and hardware-in-loop-simulation (HILS) results. [ABSTRACT FROM AUTHOR]

This study proposes an on-line minimum loss (ML) control strategy that considers the iron and copper losses of an interior permanent magnet synchronous motor in torque-controlled applications. The proposed ML control strategy utilizes a constrained optimization problem to satisfy torque reference tracking and loss minimization. An equivalent iron loss resistance circuit is adopted for the loss minimization. To solve the optimization problem, the Lagrange multiplier method is applied through a numerical analysis algorithm. The resulting solution provides the optimal current reference for every sampling instant. The Lagrange multiplier method needs parameters such as magnetic flux linkages, dynamic inductances, and iron loss resistance. Fundamental magnetic flux linkages are estimated using a flux observer, and the dynamic inductances are estimated with high-frequency voltage signal injection. The proposed iron loss resistance estimator estimates the equivalent iron resistance without any preliminary experiments. DC input power is measured using a current sensor for the accurate on-line estimation of iron loss resistance. To analyze the effectiveness of ML control compared with conventional maximum torque per ampere control, finite element analysis is used. The feasibility of the proposed ML control strategy is verified through simulation and experiments. [ABSTRACT FROM AUTHOR]