Iet Electric Power Applications最新文献

This paper proposes an AI-driven few-shot learning approach for fault diagnosis in permanent magnet synchronous motors (PMSMs), utilising a prototypical network to accurately differentiate among healthy conditions, single-fault states (ITSCF or LDMF) and mixed-fault scenarios (i.e., when the two types of faults occur simultaneously) with limited training data. Addressing these concurrent faults is particularly significant due to the potential interactions between their underlying mechanisms (e.g., high current spikes from an ITSCF causing possible magnet demagnetisation) and the increased complexity of their combined diagnostic signatures, posing significant challenges to accurate diagnosis. The study first simulates and analyses motor stator current characteristics, identifying them as key diagnostic signals for both fault types. Experimental validation measures stator current from both healthy and faulty motors. Through training with a minimal amount of data, the proposed model using a prototypical network achieves over 98% accuracy in diagnosing mixed faults (i.e., ITSCF, LDMF or a combination of both), significantly outperforming convolutional neural network (CNN)-based methods (80%). Furthermore, demonstrating a key advancement for few-shot learning in this domain, when trained on only a few labelled fault patterns, the model correctly classifies unseen faults with 81% accuracy, compared to CNN's 70%, demonstrating strong generalisation and scalability for real-world applications.

This paper addresses the challenge of limited labelled data in induction machine fault diagnosis by applying deep transfer learning with convolutional neural networks to classify ball bearing health conditions. Specifically, the objective is to classify ring and cage failures in ball bearings using a limited dataset acquired from an experimental test bench. Unlike traditional approaches that rely on vibration sensors, this study uses noninvasive current signals. Moreover, this study introduces a novel preprocessing approach that filters out the fundamental frequency of the current signal to enhance fault-related harmonics in time–frequency representations generated via continuous wavelet transform and short-time Fourier transform. Five pre-trained convolutional neural networks—ResNet18, ResNet50, VGG16, AlexNet and GoogLeNet—are fine-tuned on these representations, demonstrating up to a 47% improvement in classification accuracy. Furthermore, the approach maintains high accuracy even with only 10% of the original dataset, showcasing its sample efficiency. This work contributes to a scalable and data-efficient solution for reliable condition monitoring in industrial settings, further advancing the use of current signals for fault diagnosis.

Physics-informed neural networks (PINNs) have attracted much attention recently due to their unique advantages, such as directly fitting the strong form of partial differential equations (PDEs) and not requiring a mesh. These advantages make them suitable for solving numerical analysis problems of complex three-dimensional shapes. Since supervised-learning-based PINNs rely on the solutions obtained from traditional numerical methods, they should be regarded as performing function fitting or numerical approximation rather than truly solving a numerical computation problem. On the other hand, PINNs based on unsupervised learning can successfully solve single-domain electromagnetic analysis problems without access to the value of the physical quantity, which can be considered the ground truth. However, they cannot solve the multidomain electromagnetic analysis problem because they cannot fit the physical quantity at the interface. If the solution at the interface is unknown, PINNs can only enforce the continuity of values at the interface. Still, they cannot express the relationship between the gradients at the interface. To address this problem, this research proposes a novel numerical analysis method that employs PINNs based on unsupervised learning to solve multidomain problems. The discretised direct boundary integral equations are utilised to solve the physical quantity at the interface, and the multidomain problem can be transformed into multiple single-domain problems. Then, PINNs based on unsupervised learning can be utilised to solve all the subdomains. The feasibility of the proposed method is demonstrated through single-domain and multidomain electrostatic box problems as well as the testing electromagnetic analysis methods (TEAM) problem 22. Finally, the results of finite element analysis (FEA), boundary element method (BEM) and PINN based on unsupervised learning are compared, and the accuracy of the proposed method is proved. The FEM and analytical solutions of TEAM problem 22 are compared and discussed to confirm the accuracy of the presented numerical method.

In the excitation control system of the electrically excited synchronous generators (EESGs), the conventional fuzzy proportional-integral-derivative (FPID) excitation controller has inherent defects, such as control lag and limited adjustment speed, caused by feedback measurement delay and signal processing delay. Grey prediction control is a typical feedforward control method with advantages such as low requirements for raw data, fast response speed and flexible adjustment strategies. However, it also inevitably has limitations such as excessive reliance on model precision and limited prediction accuracy. Hence, this paper improves the FPID excitation controller based on grey prediction theory and proposes an adaptive grey FPID excitation control strategy. The proposed adaptive grey FPID excitation control strategy exhibits multiple advantages, such as parameter adaptation, fast adjustment speed and strong robustness. Both simulation and experimental results have also confirmed the significant advantages of the proposed control strategy compared to conventional FPID control.

As one of the key main equipment of the power system, the transformer directly affects the safe and reliable operation of the system. In the process of transformer reclosing, the internal electromagnetic environment is obviously different from that of the initial short circuit. The nonperiodic component of the short-circuit current exceeds that of the initial short circuit, increasing the electromagnetic force on the winding and severely threatening its dynamic stability. In order to study the influence of reclosing short-circuit impact on the radial dynamic stability of transformer winding, this paper takes SFSZ9-50000/110 transformer as an example. Firstly, the numerical model of short-circuit current of infinite power system is constructed by Matlab/Simulink, and the short-circuit current of initial short-circuit and reclosing process is calculated. The increase value of the reclosing short-circuit current compared with the initial short-circuit current is calculated, and the accuracy of the short-circuit current numerical calculation model is verified by comparison with the theoretical value. The results show that for the power transformer studied in this paper, the three-phase short-circuit current of the transformer under the reclosing short-circuit condition increases by about 6% compared with the initial short-circuit current. Then, based on the short-circuit current, the finite element method is used to analyse the leakage magnetic field of the transformer winding by using ANSYS Maxwell and the variation law of the electromagnetic force under the reclosing short-circuit impact. Finally, various kinds of radial stress of transformer winding under initial short-circuit and reclosing short-circuit conditions are compared and checked. The results show that under the reclosing condition, the average circumferential stress and inner winding radial bending stress of the transformer increase by about 12%, which leads to the decrease of the dynamic stability margin of the transformer. The research results have reference significance for optimising the dynamic stability verification method of transformer winding under reclosing short-circuit impact.

The zeroth-order vibration and noise are worthy of attention in integer slot motor vibration when it is applied to underwater vehicles. In this article, the source of the zeroth-order vibration and the deep relationship between the rotor modulation and the radial force are demonstrated for the wound field synchronous machines (WFSM). First, based on the Maxwell tensor method and the modulation effect of the stator and rotor teeth, the generation mechanism of zeroth-order vibration on the WFSM is clarified. Then, taking an integer slot distributed salient pole WFSM as an example, based on the modulation effect of the rotor teeth on the radial force density, the weakening mechanism of the zeroth-order vibration is demonstrated in detail. The rotor structural parameters of the distributed salient pole WFSM are adjusted to weaken zeroth-order vibration. In the finite element model, the radial force influence components of the zeroth-order vibration, key electromagnetic performance, and vibration spectrum of the machine before and after improvement are compared. Finally, the vibration experiment on the prototype is carried out to verify the correctness and effectiveness of the analysis and improvement.

This study proposes a hybrid feature selection method (HBPSWOA) based on binary particle swarm optimisation and whale optimisation algorithm for bearing fault diagnosis. To validate the proposed method, a fault diagnosis framework integrating feature extraction, feature selection and classification is constructed. In the feature extraction stage, variational mode decomposition and fast Fourier transform are combined to capture both time-domain and frequency-domain features. In the feature selection stage, HBPSWOA integrates the local search efficiency of BPSO with the global exploration ability of WOA. This integration is further enhanced by introducing a Hamming distance-based position update mechanism and a roulette wheel selection strategy, improving solution diversity and robustness. Selected features are evaluated using k-nearest neighbours and support vector machine. The method is validated across three datasets, and its robustness is demonstrated through experiments with Gaussian white noise of varying intensities. Compared to four traditional feature selection methods (BPSO, BWOA, GA and BGWO), the proposed method achieves higher classification accuracy while selecting fewer and more informative features. This optimisation not only enhances classification accuracy but also improves computational efficiency, including under varying noise conditions. The highest accuracy of 99.51% was achieved on the CWRU benchmark dataset using an SVM classifier. Future research directions include exploring its scalability on larger datasets and leveraging deep learning classifiers to fully exploit the potential of the selected features, further enhancing diagnostic performance.

As the core hub equipment of offshore wind power low-frequency transmission systems, low-frequency transformers generate complex harmonic disturbances during internal faults, severely compromising the reliability of traditional current differential protection. To address this engineering challenge, this paper innovatively proposes a transformer fast main protection method based on excitation inductance parameter identification. Rooted in the unique application scenarios of offshore wind power, the research focuses on overcoming the limitations of existing ratio-restraint differential protection constrained by magnetising inrush current identification. Specifically, the distinctive harmonic characteristics exhibited during low-frequency transformer faults can invalidate second-harmonic restraint principles. A novel identification model based on the dynamic characteristics of instantaneous excitation inductance is developed, which breaks through the limitations of traditional harmonic analysis methods and achieves precise discrimination between fault currents and magnetising inrush currents using single-terminal current-voltage data. Simulation experiments demonstrate that this method can reduce protection operation time to less than 10 ms, particularly suitable for special offshore platform conditions characterised by space constraints and maintenance difficulties. The proposed approach provides critical technical support for enhancing low-frequency transformer protection in offshore wind farm grid-connected low-frequency transmission systems, demonstrating significant engineering application value.

Relative to the traditional pumped storage machines, the variable speed pumped storage machine (VSPSM) has strong frequency and voltage regulation capabilities by the AC excitation. However, the enhancement of the regulation capability also leads to the increment of the temperature rise. For the purpose of investigating the temperature variation in the end zone of the VSPSM, a 336-MVA VSPSM is chosen as the research reference in this article. The electromagnetic-fluid-thermal coupled heat transfer calculation model in the end zone of the VSPSM is constructed. The flux density and loss distribution in the end zone of the VSPSM are calculated under different power factors and slips. The losses of the stator end parts are less influenced by the slip compared to the rotor end parts, which are more affected. The variations of temperature rise in the tooth plate, end core, clamping plate and the rotor retaining ring are revealed along with the power factor and slip. It is found that the stator tooth plate is the end part with the peak temperature. This research is able to lay a theoretical basis for optimising and designing the end structure of the VSPSM.

A position sensorless control method for interior permanent magnet synchronous motors (IPMSMs) has been developed to reduce cost and improve reliability. The performance of position estimation largely depends on the motor parameters. Inductance varies due to magnetic saturation during operation. Therefore, model-based position estimation deteriorates if the inductance variation is not taken into account. Traditional position estimation methods use an ideal IPMSM model that assumes the d-axis and q-axis are completely magnetically decoupled, that is, only the d-axis and q-axis self-inductances are considered. However, in reality, a cross-coupling effect exists in actual IPMSMs, resulting in mutual inductance between the d-axis and q-axis. This mutual inductance also degrades position estimation performance, particularly under heavy load conditions. Thus, it is important to identify the inductance while considering both magnetic saturation during operation and cross-coupling, in order to achieve accurate position estimation. In this paper, we propose a novel flux observer that accounts for the cross-coupling inductance and present an adaptive approach. Using the adaptive scheme, time-varying parameter identification can be effectively addressed. The effectiveness of the proposed method is verified through experimental results.