In order to achieve carbon peak and neutrality goals, many low-carbon operations are implemented in ports. Integrated energy systems that consist of port electricity and cooling loads, wind and PV energy devices, energy storage, and clean fuels are considered as a future technology. In addition, ports are important hubs for the global economy and trade; logistics optimization is also part of their objective, and most port facilities have complex logistics. This article proposes an energy–logistics collaborative optimization method to fully tap the potential of port-integrated energy systems. A logistics–energy system model is established by deeply examining the operational characteristics of logistics systems and their corresponding energy consumption patterns, considering ships’ operational statuses, quay crane distribution constraints, and power balances. To better represent the ship–energy–logistics optimization problem, a hybrid system modeling technique is employed. The case of Shanghai Port is studied; the results show that costs can be reduced by 3.27% compared to the traditional optimization method, and a sensitivity analysis demonstrates the robustness of the proposed method.

Five-phase induction motors have the characteristics of high torque density, low torque ripple, and flexible control, making them suitable for medium- and low-voltage power supply situations. However, with the expansion of application scenarios, five-phase motors need to cope with increasingly complex operating conditions. Five-phase motors for propeller propulsion will face various complex sea conditions during actual use, and five-phase motors for electric vehicles will also face various complex road conditions and operating requirements during use. Therefore, as a propulsion motor, its speed control system must have strong robustness and anti-disturbance performance. The use of traditional PI algorithms has problems, such as poor adaptability and inability to adapt to various complex working conditions, but the use of an active disturbance rejection controller (ADRC) can effectively solve these problems. However, due to the significant coupling between the variables of induction motors and the large number of parameters in the ADRC, tuning the parameters of the ADRC is complex. Traditional empirical tuning methods can only obtain a rough range of parameter values and may have significant errors. Therefore, this paper uses ADRC based on genetic algorithm(GAADRC) to tune the parameters of the control and design an objective function based on multi-objective optimization. The parameters to be adjusted were obtained through multiple iterations. The simulation and experimental results indicate that GAADRC has lower startup overshoot, faster adjustment time, and lower load/unload speed changes compared to the empirically tuned PI controller and ADRC. Meanwhile, using a genetic algorithm for motor ADRC parameter tuning can obtain optimal control parameters while the control parameter range is completely uncertain; therefore, the method proposed in this paper has strong practical value.

The use of multiphase electric drives in industrial applications has increased in the last few years. These machines’ advantages over the three-phase system make them appropriate for harsh working situations. To increase their inherent reliability, some authors have been working in sensorless control schemes, where the absence of an encoder ensures proper system performance. Nevertheless, these sensorless control systems present some problems due to the uncertainties of the parameters. In this regard, using extended Kalman filters overcomes this situation, since Kalman filters consider the system error and measurement error in the estimation process. However, when the three-phase Kalman filters are extended to the five-phase case of study, the complexity of the problem increases substantially. In this work, the authors propose an extended Kalman filter, which discomposes the original state equation, reducing the complexity of the estimation stage. In addition, the system suppresses the third-harmonic injection, which enhances the overall phase-current quality.

The high-performance control technology of multi-phase motors is a key technology for the application of multi-phase motors in many fields, such as electric transportation. The model predictive current control (MPCC) strategy has been extended to multi-phase systems due to its high dynamic performance. Model-predictive current control faces the problem that it cannot effectively regulate harmonic plane currents, and thus cannot obtain high-quality current waveforms because only one switching state is applied in a sampling period. To solve this problem, this paper uses the virtual vector-based MPCC to select the optimal virtual vector and apply it under the premise that the average value of the harmonic plane voltage in a single switching cycle is zero. Taking a five-phase induction motor as an example, the steady-state and dynamic performance of the proposed virtual vector MPCC and the traditional model predictive current control were simulated, respectively. Simulation results demonstrated the effectiveness of the proposed method in improving waveform quality while maintaining excellent dynamic performance.

Transient stability during grid fault is experienced differently in modern power systems, especially in wind-turbine-dominated power systems. In this paper, transient behavior and stability issues of a direct drive wind turbine during fault recovery in DC-link voltage control timescale are studied. First, the motion equation model that depicts the phase and amplitude dynamics of internal voltage driven by unbalanced active and reactive power is developed to physically depict transient characteristics of the direct drive wind turbine itself. Considering transient switch control induced by active power climbing, the two-stage model is employed. Based on the motion equation model, transient behavior during fault recovery in a single machine infinite bus system is studied, and the analysis is also divided into two stages: during and after active power climbing. During active power climbing, a novel approximate analytical expression is proposed to clearly reveal the frequency dynamics of the direct drive wind turbine, which is identified as approximate monotonicity at excitation of active power climbing. After active power climbing, large-signal oscillation behavior is concerned. A novel analysis idea combining time-frequency analysis based on Hilbert transform and high order modes is employed to investigate and reveal the nonlinear oscillation, which is characterized by time-varying oscillation frequency and amplitude attenuation ratio. It is found that the nonlinear oscillation and even stability are related closely to the final point during active power climbing. With a large active power climbing rate, the nonlinear oscillation may lose stability. Simulated results based on MATLAB^® are also presented to verify the theoretical analysis.