Journal
IEEE TRANSACTIONS ON POWER ELECTRONICS
Volume 38, Issue 10, Pages 12126-12138Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPEL.2023.3294328
Keywords
Diode-clamped modules; Kalman filter; modular multilevel converter (MMC); sensorless voltage balancing; state-space model; voltage estimation; voltage estimator
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This article introduces a sensorless voltage-balancing strategy and a simple controller for modular multilevel converters. By using diodes to provide a unidirectional balancing path, diode-clamped MMCs offer a simple and effective solution. The open-loop operation of the model compensates for the lack of bidirectional energy transfer. While sensorless operation reduces costs, precise knowledge of module voltages still improves operation in certain applications.
Modular multilevel converters are well known in the energy sector. Generally, their stable operation is at the expense of numerous sensors, communication burden, and computationally expensive balancing strategies that challenge their expansion to cost-driven applications. Hence, introducing a sensorless voltage-balancing strategy with a simple controller is an attractive objective. Diode-clamped modular multilevel converters (MMCs) offer a simple and effective solution by providing a unidirectional balancing path between two modules through a diode. Ideally, the modulation technique should compensate for the lack of bidirectional energy transfer; hence open-loop operation is possible. Although the sensorless operation is desirable to reduce costs, good knowledge of the modules' voltages for system monitoring, and protection functions still improves operation in some applications or is mandatory in others. However, information should not be at the cost of additional sensors and communication bandwidth. This article develops a compensated state-space model for diode-clamped MMCs to estimate module voltages using an optimal estimator without any direct measurement at module levels. The model considers the effect of the diode-clamped branches and their balancing effect, resulting in 30%-50% reduction in estimation error compared to the conventional models using similar estimators. Simulations and experiments further confirm the provided analysis, where the estimator achieves >97.5% accuracy.
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