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Simultaneous sparsity-based binary hypothesis model for real hyperspectral target detection

Abstract : The modular multilevel converters (MMCs) have emerged as the most suitable converter technology for HVDC applications. Besides the recognized advantages over conventional voltage source converters, one of the remarkable features of the MMC is its ability to store energy in the distributed submodule capacitors. This important feature can be used to mitigate the fluctuations of the DC voltage, which is inherently volatile against power disturbances compared to the frequency of conventional AC systems. This paper proposes a novel control strategy, called virtual capacitor control, which enables the utilization of the energy storage capability of the MMC to attenuate voltage fluctuations of HVDC systems. With the proposed control, the MMC behaves as if there were a physical capacitor whose size is adjustable and can be even bigger than the physical capacitor embedded in the converter. This control allows the system operator to optionally adjust this virtual capacitor of each MMC station and, thus, it provides an additional degree of freedom to the HVDC system operation. The EMT simulations of a 401-level MMC-based HVDC link system show the effectiveness of the proposed control to improve dc voltage transient dynamics.
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Simultaneous sparsity-based bi...
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Ahmad Bitar, Loong-Fah Cheong, Jean-Philippe Ovarlez. Simultaneous sparsity-based binary hypothesis model for real hyperspectral target detection. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2017), Mar 2017, New Orleans, United States. ⟨10.1109/ICASSP.2017.7953031⟩. ⟨hal-01692408⟩



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