Iet Science Measurement & Technology最新文献

To address the problem that the process deviation of dry-type air-core reactors cannot be effectively detected, electrical parameters of reactor are numerically analysed at different frequencies. A new method of process deviation detection by the power factor frequency characteristic is proposed. Considering on-site power frequency interference, a harmonic analysis method is proposed, which is based on the integer multiple of the power frequency cycle, to calculate the power factor. The results show that the resistance loss remains relatively constant at power frequency and gradually increases at higher frequencies with the number of deviation turns increases. The eddy current loss remains basically unchanged at different turn deviations and frequencies. The change laws of resistance loss and the resistance loss power factor are the same. In a double logarithmic coordinate system, resistance loss power factor changes from linear to non-linear. The non-linearity degree can be used to detect the process deviation. The test results are consistent with the theory, and the correctness of the detection method is verified.

As a protective component of HV equipment, the primary function of a surge arrester is to mitigate the impact of surge voltages. When a surge arrester fails, the equipment it protects becomes vulnerable to damage. This study integrates chaotic systems with Convolutional Neural Networks (CNN) to diagnose faults in HV surge arresters. The Partial Discharge (PD) test was initially performed on six HV surge arrester fault models. The Discrete Wavelet Transform (DWT) was performed for filtering the PD signals. Subsequently, the Chen-Lee chaotic system converted the filtered PD signals into a dynamic error scatter diagram, creating a feature map of various fault states. This feature map was then used as the input layer to train the CNN model. The results demonstrate that the proposed CNN achieved an accuracy of 97.0%, outperforming AlexNet and traditional methods using Histograms of Oriented Gradients (HOG) combined with Support Vector Machine (SVM), Decision Tree (DT), Backpropagation Neural Network (BPNN), and K-Nearest Neighbor (KNN). This study also incorporates the LabVIEW graphic control software with a fault diagnosis system for HV surge arresters. The PD data can identify the fault type in real-time, enhancing power equipment maintenance efficiency.

V-shaped composite insulator strings exhibit excellent resistance to pollution and wind deviation, resulting in their widespread use in transmission lines. The correlation between inclination angle of the shed surface and pollution flashover voltage can reflect the pollution flashover characteristics of V-shaped composite insulator strings. Therefore, the relationship between inclination angle and pollution flashover voltage was studied through artificial pollution testing methods, and the relationship between inclination angle and degree of wetting was studied using the method of adding water to increase weight. The results of this study indicated that as the inclination angle increased within the range of 0°–180°, the degree of wetting on the surface of contaminated silicone rubber gradually decreased, whereas the pollution flashover voltage continuously increased. A model of water droplet collision and wetting on the surface of contaminated silicone rubber at different inclination angles was established. This model revealed that the gravity-induced difference in the wetting states of the surface of contaminated silicone rubber under different inclination angles was responsible for the variation in pollution flashover voltages, with the maximum variation being 116.52%. The results of this study can inform the design and operation of V-shaped composite insulators.

Recently, data-driven cross-domain fault diagnosis methods for rotating machinery have been successfully developed. However, most existing diagnostic methods assume that the label spaces of the source and target domains are the same. In practice, the relationship between the label space of the source domain and the target domain is unknown, that is, the universal domain adaptation (UDA) problem. Existing overall domain distribution alignment methods are less effective in facing UDA problems. Thus, this article proposes a deep learning-based UDA model. First, the proposed model combines multi-scale learning and dual attention block, which can improve the capability to extract effective features. Then, an entropy optimization strategy is introduced to promote target domain sample clustering without prior knowledge. Finally, the effectiveness of the proposed model is verified on a public dataset of rotating machinery. The results show that the proposed method outperforms six existing cross-domain fault diagnosis methods.

Rolling element bearings are vital components within rotating machinery, making them a central focus of maintenance in the prognostics and health management sector. This involves closely monitoring their condition to accurately predict the remaining useful life, increasing reliability while minimizing unexpected breakdowns, thereby enabling cost savings through planned maintenance, and enhancing operational stability and security. To achieve this goal, it is necessary to build an online intelligent system for degradation monitoring and failure prognosis by the construction of a robust health indicator and making quantitative measure for bearing degradation. In this paper, an efficient and reliable approach is proposed to estimate the remaining useful life of bearing. A new prediction method is presented by the combination of kernel smoothing density (KS-density) and bidirectional long short-term memory (BiLSTM). Firstly, KS-density smoothens the preliminarily estimated probability distribution function using machinery degradation data. Secondly, the obtained KS-density is used in feed deep learning technique based on BiLSTM models. On this basis, the variation of the signal distribution models between the current faulty state and the normal conditions state is quantified for bearing health assessment. The effective recognition of bearing degradation by the proposed Weibull-based health index is demonstrated through experimental validations utilizing run-to-failure datasets, provided by the centre for intelligent maintenance systems. The comparison with the literature's review show that the prediction results of the proposed approach are more accurate.

To locate and read the complex pointer meter dial for the images with uneven illumination, blurred dial, and tilted dial, this paper firstly proposes an improved BRISK-FREAK algorithm for dial position. Then, combined with the Line Segment Detector (LSD) algorithm, an automatic identification method for complex pointer meter is proposed. The dial of a large number of SF₆ complex pressure pointer meter images are located and the results reveal that the proposed improved BRISK-FREAK algorithm has good adaptability under strong interference. The computational speed of the proposed algorithm is 33% and 17% higher than the Scale-Invariant Feature Transform (SIFT) algorithm and the Binary Robust Invariant Scalable Keypoints (BRISK) algorithm respectively. The positioning success rate of the proposed algorithm is 40%, 64%, and 32% higher than that of the SIFT, Oriented FAST and Rotated BRIEF (ORB) and BRISK algorithms respectively. The reading success rate of the proposed method is 94.5%, which is 19.5%, 39.9% and 14.8% higher than that of the methods based on the ORB, SIFT and BRISK algorithms respectively. It is particularly suitable for application in the actual substations to realize the identification of complex pointer meters.

This paper introduces finite difference delay modelling (FDDM) for computing the shielding effectiveness (SE) of a conductive enclosure with an aperture against electromagnetic pulses. FDDM offers optimal accuracy and stability for analysing complex structures. The time domain electric field integral equation (TD-EFIE) for the conductive enclosure is derived by imposing boundary conditions on the perfect electrical conductor (PEC) surface in the Laplace domain. Time discretization is based on finite differences, and the Laplace-to-Z transform mapping is utilized. Fast Fourier Transform (FFT) is employed to expedite the FDDM solution process. Both frequency domain shielding effectiveness (FD-SE) and time domain shielding effectiveness (TD-SE) are evaluated using this approach. Finally, to validate the accuracy of the proposed method, results are compared with simulations using CST-MWS software and the frequency domain moment method.

Conventional approaches for determining the optimal locations of surge arresters (SAs) can result in an unnecessary increase in the number of these devices in high-voltage substations. This study presents an effective technique for determining the optimal locations of SAs. The lightning back flashover (BF) and switching over-voltages are predicted with the accurate modeling of the transient behavior of all elements of high-voltage substations and transmission lines, considering the effect of environmental conditions. Also, the proposed limiting parameter Monte Carlo (MC-LP) method is utilized to correctly select the probability distribution of the possible strokes for predicting the insulation risk (IR) of a transformer based on transient over-voltage on the transformer end. Therefore, the most appropriate location to install an SA can be determined with a minimum number of calculations using the structural data of the substation, lines connected to it, and the transformer. Simulations are based on experimental results, and the number of calculations significantly decreases using the proposed algorithm. Simulations of the sample network and implementation of the proposed algorithm with MATLAB and EMTP-RV prove the efficiency of the proposed method for optimum SA placement.

Physics-informed neural networks (PINNs) are neural networks (NNs) that directly encode model equations, like Partial Differential Equations (PDEs), in the network itself. While most of the PINN algorithms in the literature minimize the local residual of the governing equations, there are energy-based approaches that take a different path by minimizing the variational energy of the model. It is shown that in the case of the steady thermal equation weakly coupled to magnetic equation, the energy-based approach displays multiple advantages compared to the standard residual-based PINN: it is more computationally efficient, it requires a lower order of derivatives to compute, and it involves less hyperparameters. The analyzed benchmark problems are the single- and multi-objective optimal design of an inductor for the controlled heating of a graphite plate. The optimized device is designed by involving a multi-physics problem: a time-harmonic magnetic problem and a steady thermal problem. For the former, a deep neural network solving the direct problem is supervisedly trained on Finite Element Analysis (FEA) data. In turn, the solution of the latter relies on a hypernetwork that takes as input the inductor geometry parameters and outputs the model weights of an energy-based PINN (or ePINN). Eventually, the ePINN predicts the temperature field within the graphite plate.

Broadband couplers consist usually of multiple coupled lines that are each a quarter wave-length long. Herein, in contrast to conventional methods, a short length of a microstrip line is coupled to a coaxial line. The length of the coupled microstrip line is small compared to the wavelength at the lowest operating frequency. The coupling is weak and the microstrip line is λ/8 at the highest operating frequency. The proposed structure resembles a pair of coupled lines with a linear coupling response. The coupling exhibits a slope of 6 dB/octave over multi-octave bandwidth. The microstrip line is connected to a lumped element compensating network at the coupled port. The compensating network is a low-pass circuit with a slope of −6 dB/octave. The power handling of the coupler is high due to the coaxial line and the weakly coupled microstrip line. Electromagnetic simulations and analytical formulations are presented. The broadband coupler handles nearly 1 kW of input power. Due to the weak coupling of the microstrip-coaxial coupler, the lumped element circuit needs to handle <1.5 W. A high-power coupler has been fabricated for the frequency range of 30 to 500 MHz. It exhibits a coupling response of 56 ± 0.4 dB.