热烈祝贺专硕钟天伟一作论文Bayesian Learning-Based Multi-Objective Distribution Power Network Reconfiguration被影响因子大于10的智能电网顶刊IEEE Trans on Smart Grid录用为长文！

    Abstract：This paper proposes a scheme aiming at solving the reconfiguration problem of distribution power network (DPN) with high wind power penetrations. The virtue of the present scheme lies in balancing the voltage stability and the absorption rate of wind energy. First, the DPN reconfiguration is formulated as a multi-objective optimization problem, where a curtailment strategy is introduced with the assistance of the secure operations of DPN. Thereby, the absorption rate of the generated wind power is maximized and voltage stability level is improved as well. Meanwhile, a modified multi-objective Bayesian learning-based evolutionary algorithm is applied to yield a Pareto front, which is a trade-off between absorption rate and voltage stability. Afterwards, the technique for order preference is used to determine the dispatching solution by similarity to an ideal solution. Finally, numerical case studies are conducted on a modified IEEE-33 bus system to verify the effectiveness of the proposed scheme. 

    Index Terms—Wind power, distribution power network reconfiguration, multi-objective optimization, Bayesian learning.