In this paper, a novel virtual negative inductor stabilizing strategy is proposed for the dc microgrid with constant power loads. It is known that in the dc-based power system, the constant power load will generate a virtual negative incremental resistance, which may deteriorate the whole system stability. The situation will be more serious for the dc power system with a large line inductance. In the proposed stabilizing strategy, a virtual negative inductor is built on the source-side converter through the droop control method. The built virtual negative inductor counteracts the large line inductance, thus enhancing the system damping effect. Small-signal models of the studied dc microgrid system under the proposed stabilizing strategy are carefully derived. A root-locus based parameter designing approach is proposed for obtaining the optimal parameter value for the stabilizer. An explicit Nyquist stability criterion for the studied dc microgrid system is proposed, with the system minor loop gain carefully derived. Several comparative stability analyses are taken for showing the system robustness to the parameter perturbations. Detailed numerical simulations are also conducted for validating the effectiveness of the proposed stabilizing strategy.

For the dc microgrid system with constant power loads (CPLs), the dc bus voltage can easily cause high-frequency oscillation owing to the complicated impedance interactions. The large line inductance and the CPL-side capacitance will form an undamped LC circuit on the dc bus, which, together with the CPL, will make the system fall into the negative-damping region, thus causing the system instability. To address this problem, a virtual negative inductor (VNI) is built on the source side converter in this paper, which can effectively counteract the large line inductance, thus alleviating the instability problem. Moreover, a nonlinear disturbance observer (NDO) is proposed for estimating the converter output current, which relieves the strong dependence of the proposed VNI strategy on the output current measurement. And the proposed strategy is implemented in a totally decentralized manner, thus alleviating the single-point-failure problem in the central controller. For assuring the optimal parameter value for the proposed stabilizing strategy, a system root-locus diagram based parameter designing approach is adopted. And comparative Nyquist diagram based stability analyses are taken for studying the robustness of the proposed strategy to the system perturbations. Finally, detailed real-time simulations are conducted for validating the effectiveness of the proposed stabilizing strategy.

With the in-depth penetration of renewable energy in the shipboard power system, the uncertainty of its output power and the variability of sea conditions have brought severe challenges to the control of shipboard integrated power system. In order to provide additional accurate signals to the power control system to eliminate the influence of uncertain factors, this study proposed an adaptive kernel based online sequential extreme learning machine to accurately predict shipboard electric power fluctuation online. Three adaptive factors are introduced, which control the kernel function scale adaptively to ensure the accuracy and speed of the algorithm. The electric power fluctuation data of real-ship under two different sea conditions are used to verify the effectiveness of the algorithm. The simulation results clearly demonstrate that in the case of ship power fluctuation prediction, the proposed method can not only meet the rapidity demand of real-time control system, but also provide accurate prediction results.

Shipboard integrated power systems, the key technology of ship electrification, call for effective failure mode power management control strategy to achieve the safe and reliable operation in dynamic reconfiguration. Considering switch reconfiguration with system dynamics and power balance restoration after reconfiguration, in this paper, the optimization objective function of optimal management for ship failure mode is established as a hybrid model predictive control problem from the perspective of hybrid system. To meet the needs for fast computation, a hierarchical hybrid model predictive control algorithm is proposed, which divides the original optimization problem into two stages, and reduces the computation complexity by relaxing constraints and the minimum principle. By applying to a real-time simulator in two scenarios, the results verify the effectivity of the proposed method.

The existing research on teaching development of teachers fails to effectively quantify the teaching development trend. This paper deeply mines the evaluation data on the teaching quality of college teachers, before analyzing and predicting the trend features for teaching development of college teachers based on knowledge discovery. Firstly, the knowledge features of the teaching development trend of college teachers were examined. Next, the fluctuation features of the time series on the teaching quality development of college teachers were described based on chaotic time series. In addition, a prediction model for teaching development of college teachers was established for weighted first-order chaotic time series, and used to simulate the nonlinear features of the time series on the teaching quality development of college teachers. The prediction model was proved effective through experiments.