This paper presents a novel distributed reconfiguration strategy to enable the secure and reliable operation of the zonal shipboard power system (SPS). To adapt to the latest distributed control structure, the proposed strategy features two levels of reconfiguration: zonal reconfiguration and global reconfiguration. An extended hybrid model of SPS is first developed to accurately capture the interactions between the discrete events and the continuously evolving system dynamics involved in the reconfiguration process. The concept of zonal and global reconfigurability is then proposed along with the evaluation criteria to examine if the system can be reconfigured to return to steady-state operation. Provided that the SPS is reconfigurable, executable algorithms are proposed to determine the optimal sequence of operation events for both zonal and global reconfiguration. To evaluate the performance of the proposed strategy, four case studies are presented in which a four-zone SPS is faced with multiple random faults and requires reconfiguration. Simulation results demonstrate that the proposed reconfiguration strategy outperformed previous algorithms in examining the reconfigurability of the system and determining the optimal reconfiguration solution.

After decades of research, X-band marine radars have been broadly used for wind measurement. For retrieving the wind direction based on the wind-induced streaks, a lot of effort has been expended on three celebrated approaches—the local gradient method (LGM), the adaptive reduced method (ARM), and the energy spectrum method (ESM). This paper presents a scientific study of these methods. The contrast of retrieving the real measured marine radar images and vane measured results is evaluated, in perspective of the error statistics and algorithm operation efficiency. Interference factors, such as the historical information of the measured area, reference wind speed, and sea condition showing in the monitoring equipment are also concerned. The tentative results showed that LGM is robust, which can be implemented in most radar images, because it allows for a lower selection of requirements compared with the other two methods. For ARM, the better retrieval performance is a tradeoff with extra computation, which is expensive. ESM is superior to the other two algorithms in terms of accuracy and computation load; however, this algorithm is sensitive in rain-contaminated radar images, meaning it is a good choice for data post-processing in the lab.

This paper presents a hybrid control strategy for the LLC resonant converter in X-ray machines, addressing the limitations of voltage gain range under Pulse Frequency Modulation (PFM) and dynamic response under Phase Shift Modulation (PSM). The strategy employs an improved fruit fly optimization algorithm (IFOA) to optimize PI control and integrate Hybrid Phase-Shifted and Frequency-Modulated control. It dynamically adjusts the switching frequency and phase shift angle to maintain output stability and efficiency, ensuring optimal operation under rated conditions. A Matlab-based system simulation model confirmed the stability and accuracy of the IFOA in controlling the converter. Subsequent prototype testing validated the strategy’s effectiveness in reducing conduction losses, enhancing overall efficiency, and demonstrating practical feasibility and superiority.

For the deep flux-weakening speed regulation of interior permanent magnet synchronous motors (IPMSMs), the conventional formula-based tuning method is highly complex. To address this issue, an IPMSM deep flux-weakening control strategy based on an adaptive RNN is proposed. This paper first analyzes traditional deep flux-weakening methods for permanent magnet synchronous motors. Building upon deep flux-weakening control, an adaptive RNN is integrated with the maximum torque per voltage (MTPV) strategy in the deep flux-weakening region. The neural network model is constructed with the input layer comprising the d-axis and q-axis currents from the previous time step, the current motor speed, and the target reference speed, while the output layer provides the required d-axis and q-axis currents for the control system at the current time step. Finally, the algorithm is implemented and validated through a simulation model built in MATLABR2024b/SIMULINK. The simulation results demonstrate that the proposed adaptive RNN-based IPMSM deep flux-weakening control system exhibits improved accuracy and robustness.

In response to the “carbon peak and carbon neutrality” strategy, industrial energy conservation has become increasingly important. Interior Permanent Magnet Synchronous Motors (IPMSMs) exhibit significant potential for efficient flux-weakening control due to their asymmetric rotor reluctance. However, conventional control strategies often cause instability during transitions across speed zones. This paper proposes a novel adaptive fuzzy-based smooth transition strategy to address this issue. First, a composite control framework integrating Maximum Torque per Ampere (MTPA) and leading-angle control is established to enhance flux-weakening capability. Then, within this framework, adaptive fuzzy controllers are designed for different weakening zones, incorporating a Lyapunov-based parameter adaptation mechanism for real-time compensation. Simulation results demonstrate that the proposed strategy achieves smooth switching across the entire speed range of IPMSMs. Quantitatively, it reduces speed overshoot by 5–15%, suppresses torque ripple by over 10%, and virtually eliminates switching current pikes compared to conventional methods, thereby significantly improving system dynamic performance and operational reliability.