Biblio

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.

The ever-growing threats of cascading failures due to cyber-attacks pose a significant challenge to power grid security. A wrong system state estimate caused by a false data injection attack could lead to a wrong control actions and take the system into a more insecure operating condition. As a consequence, an attack-resilient failure mitigation strategy needs to be developed to correctly determine control actions to prevent the propagation of cascades. In this paper, a recovery-based model predictive control methodology is developed to eliminate power system component violations following coordinated cyber-physical attacks where physical attacks are masked by targeted false data injection attacks. Specifically, to address the problem of wrong system state estimation with compromised data, a developed methodology recovers the incorrect states from historical data rather than utilizing the tampered data, and thus allowing control centers to identify proper control actions. Additionally, instead of using a one-step method to optimize control actions, the recovery-based model predictive control methodology scheme incorporates the effect of controls over a finite time horizon and the attack detection delay to make appropriate control decisions. Case studies, performed on IEEE 30-bus and Illinois 200-bus systems, show that the developed recovery-based model predictive control methodology scheme is robust to coordinated attacks and efficient in mitigating cascades.

The CPS standard can be more objective to evaluate the effect of control behavior in each control area on the interconnected power grid. The CPS standard is derived from statistical methods emphasizing the long-term control performance of AGC, which is beneficial to the frequency control of the power grid by mutual support between the various power grids in the case of an accident. Moreover, CPS standard reduces the wear of the equipment caused by the frequent adjustment of the AGC unit. The key is to adjust the AGC control strategy to meet the performance of CPS standard. This paper proposed a dynamic optimal CPS control methodology for interconnected power systems based on model predictive control which can achieve optimal control under the premise of meeting the CPS standard. The effectiveness of the control strategy is verified by simulation examples.

Large-scale sensing and actuation infrastructures have allowed buildings to achieve significant energy savings; at the same time, these technologies introduce significant privacy risks that must be addressed. In this paper, we present a framework for modeling the trade-off between improved control performance and increased privacy risks due to occupancy sensing. More specifically, we consider occupancy-based HVAC control as the control objective and the location traces of individual occupants as the private variables. Previous studies have shown that individual location information can be inferred from occupancy measurements. To ensure privacy, we design an architecture that distorts the occupancy data in order to hide individual occupant location information while maintaining HVAC performance. Using mutual information between the individual's location trace and the reported occupancy measurement as a privacy metric, we are able to optimally design a scheme to minimize privacy risk subject to a control performance guarantee. We evaluate our framework using real-world occupancy data: first, we verify that our privacy metric accurately assesses the adversary's ability to infer private variables from the distorted sensor measurements; then, we show that control performance is maintained through simulations of building operations using these distorted occupancy readings.