Iet Electric Power Applications最新文献

The transformer is an important equipment in power systems. However, prolonged abnormal conditions can lead to significant damage of the transformer equipment. The current finite element analysis (FEA) method for calculating the internal physical field of transformers is time-consuming, limiting its practicality for fast simulation. This paper focuses on predicting the internal magnetic fields of transformers under overvoltage conditions, which cause irregular changes in the transformer magnetic fields due to overvoltage. Simulation datasets of transformer magnetic field under overvoltage conditions were acquired via the COMSOL software. Subsequent analysis elucidated the influence of overvoltage parameters on the electrical characteristics of transformers. Furthermore, the dimensionality of input features relevant to magnetic field prediction was expanded. Convolutional neural network (CNN) model was then employed to forecast the internal magnetic fields of transformers under overvoltage conditions. Experimental results were compared with Random Forest (RF), eXtreme Gradient Boosting (XGBoost) and deep neural network (DNN) models, demonstrating the efficiency of deep learning methods in predicting transformer magnetic fields under overvoltage conditions.

To design the permanent magnetic eddy current couplers (PMECCs), modelling the magnetic field is essential. Traditional equivalent magnetic circuit methods and analytical methods often rely heavily on expert experience, whereas finite element methods (FEM) demand significant computational resources and time. Recently, the physics-informed neural network (PINN) has emerged as a novel approach for modelling electromagnetic fields. To fully harness the potential of PINN, eliminate reliance on data sets, and enhance the generalisation ability of multi-scale physical systems, we simplify the physical model of PMECCs and analyse its inherent boundary conditions based on the fundamental properties of electromagnetic fields. A dimensionless and unsupervised PINN, employing dimensional analysis to reduce the dimensions of the physical variables in the system was proposed. The dimensionless PINN (DPINN) is trained through unsupervised learning to solve the magnetic field equations and predict PMECC performance. Furthermore, dimensional analysis and transfer learning method are applied to enable the network to address a broader class of problems, resulting in a 92% reduction in training cost. The solution results, compared with those from the finite element method and analytical solution, exhibit similar error magnitudes (10⁻⁴ Wb/m), confirming the method's high accuracy.

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.