In this study, the multi-objective optimization considering renewable energy uncertainty and electric vehicles (EVs) is proposed for regional integrated energy systems (RIES), which can balance the economy and environmental friendliness of the system. Firstly, the influence of factors such as disordered access and charging/discharging of EVs on system operation is considered, an orderly charging and discharging model of EVs is established according to the driving laws. Then, a robust optimization model with polyhedral uncertainty set is constructed to handle the uncertainty of renewable energy output. Moreover, the minimizing operation cost and carbon emissions is developed as the multi-objective function, and the carbon emission penalty factor is introduced to convert the multi-objective into single objective solution. Finally, the validation is carried out by an actual RISE. The case studies verify the proposed method can well balance the economy and environmental protection of the system, and enhance robustness against the uncertainty while realizing the economic and optimal operation of the system.

An adaptive droop-based hierarchical optimal voltage control (DHOVC) scheme is proposed for voltage-source converter high-voltage-direct-current (VSC-HVdc) connected offshore wind farms (WFs). The wind turbines (WTs) and WF side VSC (WFVSC) are coordinated to minimize the voltage deviations of buses inside the WF from the nominal voltage and mitigate reactive power (Var) fluctuations of WTs. The model predictive control is used to improve the performance of the DHOVC scheme during a certain predictive horizon. A hierarchical solution method based on the alternating direction method of multipliers is developed to reduce the calculation burden of the central controller while improving the information privacy protection. During the predictive horizon, the WTs and WFVSC are coordinated to achieve the near global optimal performance without global information. A WF with 32 × 5MW WTs was used in the MATLAB/Simulink to test the proposed DHOVC scheme.

The permanent magnet synchronous motor (PMSM) has become a core component of electromechanical energy conversion in the modern industrial field. In order to expand the application of the PMSM in the field of high power traction, a robust predictive torque control (R-PTC) strategy for the N-segment three-phase PMSM (N*3-phase PMSM) is proposed in this paper. Firstly, the output characteristics of the N*3-phase PMSM are illustrated with the finite element analysis method, and the mathematical model is established. Then, the six-segment three-phase PMSM predictive control system driven by six parallel inverters is designed to generate the required torque. Further, the influence of the parameter mismatch on the predicted torque and stator flux is taken into consideration based on the conventional predictive torque control (PTC). Finally, a novel R-PTC method with the proportional controller is developed for the N*3-phase PMSM, which can effectively improve accuracy and robustness of predictive control performance under parameters mismatch. Simulation and experimental results verify that, compared with the conventional PTC, the proposed R-PTC method can make the predicted stator flux and torque value accurately track its reference values while achieving lower stator flux and torque ripple.

Traditional fault diagnosis methods are easy to be affected by different working conditions. This article proposed a motor fault diagnosis method based on visual knowledge, to reduce the impact of changes in working conditions and improve the feature extraction ability. The mapping relationship between actual faults and image intuitive features by symmetrized dot pattern and scale-invariant feature transform is established in this article. The fault state is obtained by statistics of the matching point with the dictionary templates generated from signals of normal and unnormal motors. Compared with other machine learning algorithms, this method does not need too much data training and learning. The efficiency of this method is validated by experiments, and the data image processing technology has great industrial application value in the field of motor fault detection or monitoring in the age of intelligence.

Fault prediction of electromechanical equipment can greatly reduce its maintenance cost and prevent catastrophic damage. In order to realize the accurate fault prediction of electromechanical equipment, a fault prediction method based on spatial-temporal graph information is proposed in this paper. In the proposed method, the signal data of each intermittent monitoring time period are expressed by Markov field graph information, and the spatial-temporal correlation features of graph information are extracted and studied by multivariate spatial-temporal graph neural networks. The effectiveness of Markov graph information for different state expression is demonstrated by motor fault data from motor fault experimental platform, and the bearing fault prognostics data is used to demonstrate the feasibility and accuracy of the proposed fault prediction method. The results show that based on local spatial state information and global time correlation information of monitoring signals, this method could accomplish the accurate long-term and short-term fault prediction tasks respectively.

In this study, a novel predictive torque and stator flux control (PTSF) method is proposed for N-segment three-phase PMSM (N*3-phase PMSM), which can effectively eliminate the influence of parameter mismatch on stator flux and torque. The proposed PTSF method has two loops, stator flux loop and torque loop, both implemented with robust predictive control algorithm. First, the stator flux predictive controller with parameter robustness is designed, and the sensitivity of its parameters is analyzed. In the dq stator flux space, the robust stator flux predictive control with one-step delay compensation is then proposed, which can effectively enhance robustness against parameters mismatch. Moreover, the robust torque predictive control based on unknown torque disturbance observer is developed, which can effectively strengthen the robustness against the load torque disturbance and parameter mismatch. Finally, the validity and feasibility of proposed PTSF method are verified by simulation and experiment. Excellent simulation and experimental results were achieved with respect to the stator flux tracking error, torque /flux ripple reduction, and stator current distortion over conventional predictive torque control (PTC).