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AC/DC hybrid distribution network can realize the high penetration utilization of renewable energy and modular multilevel converter (MMC) is the key equipment to connect AC grid and DC grid. In this paper, the direct modulation based on the virtual resistor and the reference value of dc-bus voltage is adopted, and instantaneous-value model of three-phase half-bridge MMC is derived by introducing differential-mode and common-mode component representation. Then, the decoupling control of AC active power and reactive power is designed based on the linear active disturbance rejection control theory (LADRC). The total disturbances including the coupling term and capacitor voltage fluctuation can be estimated by extended state observer (ESO), and then cancelled exactly. Based on the simplified average-value model of MMC, the small signal stability analysis of three-terminal AC/DC distribution network with 26-level MMCs corresponding to the Tangjiawan-Jishan1-Jishan2 demonstration project is carried out by root locus and bode diagram method, which gives the guidance for the choice of the main circuit component and controller parameters, and shows that the virtual resistor greatly reduces the resonant peak of AC/DC distribution network. Simulation and experiment results verify the effectiveness of the power decoupling control strategy of MMC and the AC/DC distribution network can realize complex multi-directional power flow.

Attribute reduction is an important method in data analysis and machine learning, and it usually relies on algebraic and informational measures. However, few existing informational measures have considered the relative information of decision class cardinality, and the fusion application of algebraic and informational measures is also limited, especially in attribute reductions for interval-valued data. In interval-valued decision systems, this paper presents a coverage-credibility-based condition entropy and an improved rough decision entropy, further establishes corresponding attribute reduction algorithms for optimization and applicability. Firstly, the concepts of interval credibility, coverage and coverage-credibility are proposed, and thus, an improved condition entropy is defined by virtue of the integrated coverage-credibility. Secondly, the fused rough decision entropy is constructed by the fusion of improved condition entropy and roughness degree. By introducing the coverage-credibility, the proposed uncertainty measurements enhance the relative information of decision classes. In addition, the nonmonotonicity of the improved condition entropy and rough decision entropy is validated by theoretical proofs and experimental counterexamples, with respect to attribute subsets and thresholds. Then, the two rough decision entropies drive monotonic and nonmonotonic attribute reductions, and the corresponding reduction algorithms are designed for heuristic searches. Finally, data experiments not only verify the effectiveness and improvements of the proposed uncertainty measurements, but also illustrate the reduction algorithms optimization through better classification accuracy than four comparative algorithms. [ABSTRACT FROM AUTHOR]