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The square-wave voltage signal injection method with the proportional-integral (PI) observer is commonly adopted to estimate the rotor position of interior permanent magnet synchronous motor (IPMSM). However, the saliency estimation method with PI observer is a continuous injection method and the polarity identification method is a discrete one, which causes extra transition time and complexity. To solve this problem, a fast unipolar sequence-pulse injection method is proposed to unify the process of saliency and polarity identification and the complexity of the algorithm as well as the identification time are reduced. At the same time, a four-stage search algorithm is proposed to ensure the fast convergence and improve the accuracy by reducing the number of the pulse voltage signals. The experiment on motor drive platform verifies the effectiveness of the proposed method to IPMSMs with different saliency ratios. [ABSTRACT FROM AUTHOR]

Multiple-voltage-region control, in which the bus-voltage range is divided into several regions, is usually implemented for dc microgrid operation in distributed manner. Voltage/power droop relationships are imposed for active power sharing among slack terminals. Conventionally, threshold voltages for voltage region partition are determined with fixed percentage of variation around the nominal value, which may result in unevenness of droop coefficients in different regions. If system droop coefficient is too high, significant bus-voltage step change due to load variation will occur. On the other hand, significant power sharing error among slack terminals will be induced if the droop coefficient is too low. In this paper, a compromised solution with power-capacity-based bus-voltage region partition is proposed to equalize the droop coefficients in different regions. However, the droop coefficients are determined based on the rated power capacity of system units. Bus-voltage discontinuity appears when the power capacity reduces in actual implementation. To eliminate the voltage discontinuity, online droop coefficient tuning according to the real-time power capacity is implemented. Algorithms for local power capacity estimation of solar photovoltaic (PV) and battery energy storage have been proposed. A lab-scale dc microgrid has been developed for verification of the proposed methods. [ABSTRACT FROM AUTHOR]