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Soft normally open points (SNOPs) connected to distribution networks are instrumental in maintaining uninterrupted power supply and improving power quality during the fault period. In order to achieve the optimal service restoration scheme, a novel bi-level service restoration model based on the robust optimization method is proposed in this paper. The upper level of the proposed model aims at minimizing the risk of load loss. The switch statuses and range of power transmitted by SNOP terminals can be obtained. In the lower level of the model, the interval robust optimization method is adopted to cope with the uncertainties of DGs and loads. If there are no optimal solutions, the upper level model needs to be solved once more to adjust the network topology. Finally, a test system is established to verify the effectiveness of the proposed model and solution method. The effects of forecasting errors, the DG penetration, and the ratio of single terminal capacity of SNOP to line capacity on the optimization results are analyzed.

System identification plays an important role in improving the structure and parameters of a system, but there are many problems encountered in actual operation. The identification of dynamic systems is not as simple as it is for static systems; thus, choosing effective model structures and parameters is the key to solving this problem. This paper proposes a novel algorithm based on a combination of a broad learning system (BLS) and particle swarm optimization (PSO) to identify nonlinear dynamical systems. The proposed method first uses the dimension expansion of the data set as the input of the BLS and then optimizes the model weight by the PSO algorithm. To verify the effectiveness of our proposal, we use four second-order systems for simulation experiments. The simulation results clearly show the efficiency and anti-interference ability of the proposed method.