Iet Electric Power Applications最新文献

Zhang, Q., et al.: A super-high-speed PM motor drive for centrifugal air compressor used in fuel cell unmanned aerial vehicle. IET Electr. Power Appl. 17(11), 1459–1468 (2023). https://doi.org/10.1049/elp2.12350

In the ACKNOWLEDGEMENTS, the project number for National Key Research and Development Program of China is corrected to 2022YFB4004200.

Original acknowledgement: “This work is supported by the National Key Research and Development Program of China (SQ2022YFB4000103); National Natural Science Foundation of China (52006027); Natural Science Foundation of Hebei Province (E2021501028).”

Corrected acknowledgement: “This work is supported by the National Key Research and Development Program of China (2022YFB4004200); National Natural Science Foundation of China (52006027); Natural Science Foundation of Hebei Province (E2021501028).”

We apologize for this error.

Five-phase permanent magnet synchronous machine (PMSM) can operate under open-circuit faults with the appropriate current regulations. However, the traditional current regulations based on the five-phase half-bridge inverter have to satisfy the constraint of zero neutral point current, which limits the fault-tolerant performance of the machine. Thus, this paper investigates the current regulation based on the five-phase six-leg (FPSL) inverter, which relieves the zero-neutral point current limitation, and both current regulations considering the maximum torque (MT) and minimum copper loss (MC) are derived under different open-circuit conditions. The cost of the system has increased, but with the new regulations, better fault-tolerant operation capability of the machine can be obtained under all kinds of open-circuit faults. Moreover, with the introduction of the neutral point current, the machine can work under triple-phase open-circuit faults. In conclusion, the proposed control scheme based on the FPSL inverter expands the fault-tolerant operation capability of the five-phase PMSM. The experiment results prove the effectiveness of the proposed current regulation on increasing average torque or reducing copper loss under fault-tolerant operation.

A variable coefficient segmented iron loss calculation model is proposed for high-voltage multi-pole asynchronous motors, which fully considers the influence of high-order harmonic magnetic density on such motors. This model introduces additional hysteresis loss, improved eddy current loss coefficients, and rotation magnetisation coefficients to account for changes in losses due to small hysteresis loops and skin effect, as well as rotation magnetisation losses. A 710 kW 10-pole asynchronous motor is used as the research object. A full-domain iron loss calculation model considering stator and rotor tooth top surface losses is developed. The model can be used to accurately calculate the overall and local harmonic iron losses in the stator and rotor cores of high-voltage multi-pole asynchronous motors. And quantitatively analyse the distribution pattern of any harmonic iron loss at any location in the motor core. It realises the refinement calculation and analysis of iron losses in the whole domain of high-voltage multi-pole asynchronous motor. Finally, no-load and load tests were conducted on the 710 kW 10-pole asynchronous motor. The iron losses of the 710 kW 10-pole asynchronous motor at no load and load are calculated using the above mentioned model and the classical trinomial constant factor model. The results are compared with the measured iron loss values. It is proved that the iron loss model is more accurate in calculating the iron loss of high-voltage multi-pole asynchronous motor. The result helps researchers to calculate the iron loss distribution of high voltage motors more accurately. Thus, the design and operating parameters of the motor can be optimised to improve the efficiency and performance of the motor. It provides the necessary technical support and key basis for the reduction of loss and energy saving of high-voltage motors and the optimisation of their core structure.

A novel levitating hybrid linear actuator is proposed for magnetically levitated carrier systems. This hybrid linear actuator, “MaglevMotor”, is capable to levitate itself under the passive rail and thrusts itself in a single longitudinal direction. The proposed structure aims to reduce complexity by minimising the necessary number of components, resulting in simplified geometry, improved assembly convenience, and decreased manufacturing tolerances. The MaglevMotor is optimised in multi-physics aspect. Optimisation objectives are maximising thrust force, minimising total mass and minimising mechanical deformation of yoke with constraints of a user defined magnetic flux density (B) in yoke and zero power condition for specific air gap value. The authors present pre-optimisation studies, optimisation results and final MaglevMotor designs step by step. By utilising three of the MaglevMotors units, the carrier is able to achieve motion in six degrees of freedom. The carrier's performance targets, including a 0.2 G acceleration, a total mass of less than 10 kg, and a 5 mm levitation air gap, have been both attained and validated through corresponding experiments.

Precise electromagnetic transient simulation of induction motors (IMs) under voltage sag condition is necessary in Electro-Magnetic Transient Programme. Although traditional fifth-order model can accurately reflect the electromagnetic transient behaviour of IMs, the disadvantage of massive computation limits its application in the large-scale system simulation. Considering voltage sag is a typical power quality issue, a novel reduced-order electromagnetic transient model based on equivalent flux linkage derivative is proposed to shorten simulation time during voltage sag. When characteristic parameters of voltage sag are given, an equivalent flux linkage derivative can be obtained simply, and the proposed model can effectively shorten the simulation time while ensuring accuracy of the fifth-order model. Simulation results of small capacity (5.5-kW) and large capacity (315-kW and 1600-kW) IMs in simple system and IEEE 9-bus system confirm the applicability of the proposed model. It is estimated that simulation time of the proposed model decreases by 76% compared with the fifth-order model. Finally, the experimental validation with the 5.5-kW IM is carried out to show the effectiveness of the theory.

To facilitate the regulation of the air-gap magnetic field of permanent magnet synchronous machines, a novel topology of a high-speed hybrid excitation synchronous machine (HESM) was presented. The electromagnetic performance of the proposed HESM was evaluated. The air-gap magnetic field regulation principle was introduced first. Then, a mathematical model of the HESM was established. Based on the characteristics of a 3D magnetic circuit, the HESM equalled three types of 2D magnetic circuit machines in axial parallel superposition, and thus the expressions of inductance parameters were deduced. Subsequently, the voltage regulation performance of the HESM was assessed according to the mathematical model and inductance parameters. The loss and efficiency were calculated. Finally, a HESM prototype was tested and verified.

Currently, the digital transformation of the power grid is underway, and the intelligent health management technology for power transformers is rapidly advancing. However, there are issues in the operation and maintenance process, such as weak information correlation and low decision-making efficiency. Knowledge graphs have been applied in other industrial fields, such as spacecraft maintenance, to significantly improve knowledge query efficiency. However, there is a lack of literature on knowledge graph construction in the field of power transformer operation and maintenance. Additionally, there is limited publicly available data and difficulties in effectively mining operation and maintenance knowledge in this field. A method for constructing a knowledge graph for power transformer operation and maintenance based on ALBERT is proposed. Firstly, publicly available literature in the field of power transformers is collected, and a sample enhancement method using regular matching is used to enrich the semi-structured corpora, such as power system accident investigation reports, to construct a training dataset for power transformer operation and maintenance. Then, the ALBERT-BiLSTM-CRF deep learning algorithm is applied to extract power transformer operation and maintenance entities from the relevant literature and accident investigation reports, and this method is compared with traditional deep learning algorithms to demonstrate its superiority. Subsequently, the ALBERT-BiLSTM-Attention deep learning algorithm, which incorporates ALBERT and attention mechanism, is utilised to extract relationships between power transformer operation and maintenance entities. Compared to other deep learning algorithms, this algorithm demonstrates better performance in the domain-specific texts of power transformer operation and maintenance. Finally, the Neo4j graph database is used to visualise and present the knowledge graph, enabling decision support based on the power transformer operation and maintenance knowledge graph.

The permanent magnet eddy-current coupler (PMEC) is a kind of non-contact, stepless speed regulation device and has been widely used in transmissions. However, the current PMECs are coaxial and cannot achieve transmission and speed regulation in intersecting axes. An intersecting-axis permanent magnet (PM) eddy current coupler (IPMEC), which consists of a disk-type PM rotor and a barrel-type conductive sheet (CS) rotor arranged with intersecting axes, is proposed. It generates eddy currents through the speed difference between the two rotors and uses the non-contact electromagnetic force between the eddy currents and permanent magnets to transmit torque. During operation, the speed can be adjusted by altering the coupling area or air gap. A theoretical model of the IPMEC based on the equivalent magnetic circuit method is presented and verified by finite element simulation using ANSYS. Then, the effects of the pole number of the PMs, the radius of the CS rotor and the PM rotor on the performance were analysed, and the design method of the IPMEC was proposed. This study can be used to expand the application range of PMECs.

Intelligent motor fault diagnosis in industrial applications requires identifying key characteristics to differentiate various fault types effectively. Solely relying on statistical features cannot guarantee high classification accuracy, while complex feature extraction techniques can pose challenges for industry practitioners. Conversely, advanced feature extraction may not ensure that the model effectively learns these features for classification. A feature fusion approach that combines statistical and deep learning features to address these challenges is proposed. Since statistical features form the foundation for general feature extraction, statistical and deep learning features are combined using Extreme Gradient Boosting (XGBoost) algorithm with Particle Swarm Optimization (PSO). The PSO algorithm automates parameter tuning for XGBoost. A deep neural network (DNN) adaptively extracts hidden features, improving bearing fault classification precision using t-SNE representation. Results successfully prove the DNN's ability to classify diverse motor faults using deep learning features. Thus, integrating statistical features with XGBoost further enhances DNN's performance. To ensure robustness, the proposed method has been compared with different motor fault classification methods and validated across different motor fault datasets, showcasing improved classification accuracy and robust performance, even amidst varying noise levels. This approach represents a promising advancement in intelligent fault diagnosis within industrial contexts.