Biblio

Multi-active bridge (MAB) converter has played an important role in the power conversion of renewable-based smart grids, electrical vehicles, and more/all electrical aircraft. However, the increase of MAB submodules greatly complicates the control architecture. In this regard, the conventional centralized control strategies, which rely on a single controller to process all the information, will be limited by the computation burden. To overcome this issue, this paper proposes a decentralized power management strategy for MAB converter. The switching frequencies of MAB submodules are adaptively regulated based on the submodule local information. Through this effort, flexible electrical power routing can be realized without communications among submodules. The proposed methodology not only relieves the computation burden of MAB control system, but also improves its modularity, flexibility, and expandability. Finally, the experiment results of a three-module MAB converter are presented for verification.

In state estimation of steady-state power networks, a cyber attack that cannot be detected from the residual (i.e., the estimation error) is called a false data injection attack. In this paper, to enforce security of power networks, we propose a method of detecting a false data injection attack. In the proposed method, a false data injection attack is detected by randomly choosing sensors used in state estimation. The effectiveness of the proposed method is presented by two numerical examples including the IEEE 14-bus system.

In order to suppress the spread of zero-day virus in the network effectively, a zero-day virus suppression strategy was proposed. Based on the mechanism of zero-day virus transmission and the idea of platform dynamic defense, the corresponding methods of virus transmission suppression are put forward. By changing the platform switching frequency, the scale of zero-day virus transmission and its inhibition effect are simulated in a small-world network model. Theory and computer simulation results show that the idea of platform switching can effectively restrain the spread of virus.

Model predictive control (MPC) has become one of the most attractive control techniques due to its outstanding dynamic performance for motor drives. Besides, MPC with constant switching frequency (CSF-MPC) maintains the advantages of MPC as well as constant frequency but the selection of weighting factors in the cost function is difficult for CSF-MPC. Fortunately, the application of artificial neural networks (ANN) can accelerate the selection without any additional computation burden. Therefore, this paper designs a specific artificial neural network optimized by genetic algorithm (GA-ANN) to select the optimal weighting factors of CSF-MPC for permanent magnet synchronous motor (PMSM) drives fed by three-level T-type inverter. The key performance metrics like THD and switching frequencies error (ferr) are extracted from simulation and this data are utilized to train and evaluate GA-ANN. The trained GA-ANN model can automatically and precisely select the optimal weighting factors for minimizing THD and ferr under different working conditions of PMSM. Furthermore, the experimental results demonstrate the validation of GA-ANN and robustness of optimal weighting factors under different torque loads. Accordingly, any arbitrary user-defined working conditions which combine THD and ferr can be defined and the optimum weighting factors can be fast and explicitly determined via the trained GA-ANN model.

This paper deals with effects of current sensor bandwidth and time delays in a system controlled by a Phase-Shift Self-Oscillating Current Controller (PSSOCC). The robustness of this current controller has been proved in former works showing its good performances in a large range of applications including AC/DC and DC/AC converters, power factor correction, active filters, isolation amplifiers and motor control. As switching frequencies can be upper than 30kHz, time delays and bandwidth limitations cannot be neglected in comparison with former works on this robust current controller. Thus, several models are proposed in this paper to analyze system behaviours. Those models permit to find analytical expressions binding maximum oscillation frequency with time delay and/or additional filter parameters. Through current spectrums analysis, quality of analytical expressions is proved for each model presented in this work. An experimental approach shows that every element of the electronic board having a low-pass effect or delaying command signals need to be included in the model in order to have a perfect match between calculations, simulations and practical results.