Long Jin

To address the problem of asymmetric two-phase vibration in ultrasonic motors and overcome the limitations of conventional equal-amplitude excitation methods, this paper proposes a novel adaptive model-free active disturbance rejection control (AMFADRC) strategy for vibration amplitude tracking under model-free and sensorless conditions. Unlike existing approaches, the proposed method estimates the vibration amplitude in real time based on the excitation voltage and current, and adaptively adjusts the control parameters using a compensation coefficient derived from the ratio between the initial and regulated voltages. This eliminates the reliance on accurate system models or external sensors. After voltage regulation, the controller ensures both phases have equal amplitudes to maintain a stable traveling wave. The proposed strategy effectively compensates for steady-state nonlinear disturbances and adapts to variations in motor parameters, ensuring robust and high-precision real-time tracking performance. Extensive comparative simulations and prototype experiments validate the superior capability of the proposed method in suppressing periodic fluctuations and enhancing motor speed stability, demonstrating its practical applicability and robustness. Nevertheless, challenges related to limited sampling precision and sudden severe input faults remain to be addressed in future work.

Soft magnetic materials are widely used for magnetic components in power electronic converters. Their hysteresis effect leads to the nonlinear impedance characteristics of the magnetic cores, which can cause loss increase, waveform distortion, and electromagnetic interference issues in the converter. Due to the nonlinear behavior and the diversity of material characteristics, current Preisach hysteresis modeling remains challenging in parameter identification and probability distribution function (PDF) modeling procedure. In parameter identification, the classical methods rely on complex and restrictive double integration or repeated measurements. In PDF modeling, conventional PDF forms are constrained by a few degrees of freedom. To achieve high modeling precision, PDF forms with higher degrees of freedom are required. It also restricts the universality of the PDF, so different PDFs are needed for different magnetic materials. To address these two issues, a parameter identification approach based on the geometric interpretation of integration is proposed, which requires only a single B–H curve for ferrite. It avoids repeated testing and improves the identification efficiency significantly. Moreover, a generalized PDF form with more independent adjustable parameters is proposed. It can encompass different PDFs and model different hysteresis loop shapes of various magnetic materials. Experiments on different ferrites and silicon steels are conducted for verification. Compared with the conventional method, the proposed approach demonstrates significant improvements in accuracy, universality, simplification, and rapidity across different materials and shapes of magnetic cores.

In this paper, a thermal analysis method of electric motor based on the magnetic equivalent circuit method and the ensemble thermal network method is proposed, which is convenient for modelling, fast for calculation, and is able to obtain the accurate temperature distribution of the motor quickly. Firstly, on the basis of the proposed unitary magnetic circuit model, all kinds of losses of the electric motor are analyzed and calculated. Subsequently, a thermal network model is established, and multilayered modelling is carried out on the rotor and the stator part in order to obtain more accurate calculation results. In the thermal network solution, the two cases of temperature affecting only the copper resistance and temperature affecting the working performance of permanent magnets at the same time are considered, respectively, and the calculation results in the two cases are compared with the finite element temperature field calculation results, which verifies the validity of the calculation method proposed in this paper.

Determining threshold values in model based fault detection (MBFD) is a longstanding challenge, which is often addressed through empirical and ambiguous adjustments. To tackle this issue, this paper proposes an uncertainty-informed framework for quantitative threshold assessment. The framework comprises three stages: 1) identifying uncertainties by explicitly understanding the implemented MBFD method, 2) quantifying fault detection residual through uncertainty propagation, and 3) determining and optimizing threshold values based on the quantified misdiagnosis rates. To validate the effectiveness of the proposed approach, a case study of a modular multilevel converter is selected. The proposed method not only enables a quantified threshold assessment but also enhances the robustness of the fault detection by accounting for uncertainties.

Protective relays play a fundamental role in power systems, whose functions consist of three parts, namely protection, automation, and control, thus called the PAC altogether. It turns out that the architecture of a PACS (protection, automation, and control system) may exert a direct impact on the reliability of protective relay systems, previous studies of which are often conducted on the assumption that intelligent electronic devices (IEDs) have a constant fault rate with only mission reliability of the systems considered. To obtain a more accurate evaluation of system reliability bearing system maintenance in mind, this article proposes a novel method to appraise the reliability of PACSs with integrated consideration of basic and mission reliability. Firstly, the basic reliability of IEDs is calculated by parts count prediction, which will provide a more precise fault rate of IEDs. Secondly, the reliability of PACSs is analyzed by the reliability diagram and Markov model. By using the parts count prediction method to calculate the failure rate of the device, the steady-state probability rate will be accurate more than 0.0107%. At last, a quantitative criterion is finalized for selecting a suitable architecture easily.

This paper proposes a novel voltage estimation scheme for the modular multilevel converter (MMC) based on model predictive control (MPC). The developed strategy is presented by combining a disturbance observer (DOB) with an adaptive neural network (ANN) for voltage estimation in the MMC. Firstly, the ac-side and dc bus voltages are estimated as the disturbance items of the DOB which acts as the cost function during each control cycle and ensures the minimal computational cost. Then, the submodule (SM) capacitor voltage estimation is achieved based on the ANN with the estimated ac-side and dc bus voltages. The proposed method requires only one current sensor per arm and has a simple structure with three weights to be adjusted. Comprehensive simulation studies and experiments are presented to demonstrate its effectiveness and feasibility. The results indicate that the proposed method has a high accuracy, a fast dynamic response, and no effects on the original MPC performance.

Condition monitoring has proven to be an effective means of enhancing the reliability of power semiconductors. This paper proposes an innovative and practical data acquisition scheme that can serve various condition monitoring purposes for power semiconductors in traction inverter applications. It is achieved through the DC-link discharging operation mode of the traction inverter after the system is shut down, minimizing the new risks introduced by condition monitoring into system operation, which is critical to the safety of traction applications. Moreover, due to the extremely low dynamics in the discharging operation mode, the sampling requirements for the health indicators and operating conditions of power semiconductors are greatly reduced, and the measurement disturbances from parasitic parameters are eliminated. The proposed method is promising for electric vehicle (EV) applications as the required operation condition is an inherently programmed and frequent mode. Compared to conventional online data acquisition schemes, it outperforms in complexity, accuracy, safety, and practicality. The proof-of-concept experiments based on a three-phase inverter prototype verify the effectiveness of the proposed method.