Iet Science Measurement & Technology最新文献

This paper presents a workflow to create a reduced order model for the electromagnetic analysis of electrical machines over its operating range so that the stator forces can be exported for mechanical analysis. A permanent magnet synchronous motor developed within the EDISON (Electric Drivetrain Integration by Simulation and OptimizatioN) project is used as a model to validate the workflow. The response surface model approximation method is used to generate the reduced order model, which is then validated by comparing with the results from finite element analyses.

Microgrid can realize energy saving and emission reduction and multi-energy complementation, but the fluctuation of renewable energy output and the error of day-ahead scheduling will threaten the stability of microgrid operation. For this reason, this article proposes a microgrid multi-timescale optimal scheduling method based on new energy output scenario generation. First, the microgrid framework of this article is introduced, and an energy cycle emission reduction model taking into account electricity-to-gas conversion is designed; second, a new energy prediction error model is established based on the prediction box and Gaussian hybrid model, and the scenario of wind and photovoltaic power generation is generated by correcting the sampling intervals for error sampling; and then, based on the day-ahead scheduling plan, an intraday cooling, heating and electricity two-layer rolling optimization model is established to correct the day-ahead scenario. Finally, the example analysis shows that the cyclic emission reduction model can realize the recycling of resources, reduce the fuel cost and carbon emission, and the generation of scenarios for wind power can smooth out the fluctuation of new energy power, which, together with the intra-day two-layer rolling optimization, can reduce the day-ahead scheduling error and improve the stability of microgrid operation.

This paper proposes an online identification method utilizing phasor measurement unit (PMU) measurements, symmetric dot pattern (SDP) graphs, and the ResNet50-L1 M model to facilitate the rapid identification of metering relative errors among interconnected capacitor voltage transformers (CVTs) in wide-area substations within power systems. First, PMUs measured the three-phase voltage data from interconnected CVTs across wide-area substations. The amplitude differences of the three-phase voltages were converted into Euclidean distances, which were then transformed into SDP graphs, with each type of metering relative error corresponding to a distinct SDP graph. The ResNet50-L1 M model was established by integrating an L1 norm-based attention mechanism into the ResNet50 architecture. This model was then trained on CVT SDP graphs representing various error states to develop an online error identification system. The effectiveness of the proposed method was evaluated based on data from the “Houshou transmission line” in Ningxia province, which connects the 330 kV Niushoushan substation and the 330 kV Houqiao substation operated by the State Grid Corporation of China. With 16 different error states tested, the model achieved an identification accuracy rate of 89.39%, demonstrating a notable improvement over other methods in comparative tests.

Incidents in extreme environments can bring unprecedented consequences that would endanger the lives of many humans. Real-time and early detection of cracks and defects in the steel used for the construction of industrial sites can bring many benefits to remote operators and site managers. Although different methods and systems have been proposed in the past, they are not suitable for real-time fault detection or long-term operation in harsh environments. Therefore, this paper proposes a low-power robotic platform with a magnetic actuator mechanism which is capable of wireless inspection of steel structures using the ultrasonic method. The proposed magnetic motion uses the inertia force generated by the piston vibration and the difference in frictional force during movement. The paper evaluates the performance of the steel structure defect detection system from four different perspectives: sensing principle, wireless communication network, driving part, and power supply.

To meet the demand for non-contact displacement detection of magnetic levitation spheres, single-ended and differential variable electrode distance capacitance displacement detection schemes are designed in this paper. For these two schemes, finite element simulation models are established in COMSOL Multiphysics software to obtain the relationship between the test capacitance and the displacement of the magnetic levitation sphere. In the single-ended scheme, there is a non-linear relationship between the test capacitance and the position of the levitated sphere, and this non-linearity becomes more significant as the sphere moves away from the electrode plate. In contrast, in the differential scheme, the test capacitance and the displacement of the levitated sphere follow a linear relationship, but the test sensitivity decreases with the expansion of the measuring range. The simulation results of the differential scheme have been verified by building a spherical displacement detection simulator, and thanks to the mature development of capacitive detection circuits, the scheme is expected to achieve better than nanoscale displacement detection resolution in the 6.6 mm displacement range of a magnetic levitation sphere in the future.

It is my great pleasure to be editing this special section of the IET Science, Measurement and Technology (SMT) to include some representative papers from the recent CEM Conference which took place in Cannes, France on 11–14 April 2023.

The International Conference on Computation in Electromagnetics (CEM) has been the flagship bi-annual event of the Electromagnetics TPN since 1992, but this is the first time it has been run independently from the IET (although we are pleased that they continued to co-sponsored the event). This was the 11th Conference in the series, and as with previous events, it provided a forum for the presentation and discussion of research into computational techniques for solving problems in electromagnetics, with a focus on aspects and applications of numerical modelling based on these techniques.

The technical scope of the conference series includes Techniques of Computational Electromagnetics, Numerical Methods, plus Applications of Computational Electromagnetics. Both low and high-frequency fields were covered, as well as electromagnetic hazards and EMC, and the use of CEM in Education.

More information is available on the Conference web site: www.cem2025.com and we look forward to seeing you in Bruges in 2025.

Dr. Cris Emson studied electronic engineering at Hull University, and received his Bachelor's degree with Honours in 1977. Since then he has obtained a Master's degree in Microwave and Communications Engineering from Leeds University, and a Ph.D. from the University of Wales Swansea on special finite element techniques for unbounded electromagnetic propagation problems.

In 1982 he joined the Rutherford Appleton Laboratory to develop numerical methods for modelling three-dimensional eddy current problems, and after its formation he joined Vector Fields Ltd, developing software packages for 3D eddy current and high-frequency analyses. In 2004 he moved to the USA to take over as Vice-President of Vector Fields Inc.

On returning to the UK in 2007 he joined Infolytica Europe. Following the acquisition of the Infolytica software by Mentor (a Siemens Business) in 2017, he took the European arm of the company and changed its name to Infologic Design Ltd, to continue with the sales and support of the electromagnetic software and related services.

He has published over 90 technical papers and reports, mainly in the areas of eddy current modelling and special techniques for computational modelling. He has also been actively involved with the IET (formerly IEE) for many years and is a past Chairman of the Science and Engineering Technology Board. He has been a member of the organising committee of each IEE CEM Conference since the second, held in 1994.

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.