Iet Science Measurement & Technology最新文献

Currently, the perception of temperature rise inside the gas insulated switchgear (GIS) is mainly achieved by fitting and training steady-state temperature values, whereas abnormal temperature rise is a long and slow transient process, so there is a lack of real-time monitoring of the temperature rise inside the GIS busbar for early warning. Therefore, this article proposes and derives the GIS busbar transient temperature rise mathematical model (TTRMM) considering ambient temperature. The GIS busbar TTRMM demonstrates that the ratio of the temperature rise rate variation to the temperature rise relationship is within a constant interval, thus defining the influence coefficient K. Subsequently, a conductor temperature pre-evaluation criterion is established, which links the ambient, enclosure, and conductor temperature evolution characteristics. A temperature rise experiment platform was built to validate the model. The experiment results proved the correctness of TTRMM, and the temperature evolution trend of the conductor conforms to an exponential function and is divided into three stages. This article not only reveals the transient evolution mechanism between the ambient environment, enclosure, and conductor but also achieves the purpose of sensing the rate of temperature rise of the GIS busbar conductors by real-time monitoring of the ambient environment and enclosure temperatures.

Currently, the perception of temperature rise inside the gas insulated switchgear (GIS) is mainly achieved by fitting and training steady-state temperature values, whereas abnormal temperature rise is a long and slow transient process, so there is a lack of real-time monitoring of the temperature rise inside the GIS busbar for early warning. Therefore, this article proposes and derives the GIS busbar transient temperature rise mathematical model (TTRMM) considering ambient temperature. The GIS busbar TTRMM demonstrates that the ratio of the temperature rise rate variation to the temperature rise relationship is within a constant interval, thus defining the influence coefficient K. Subsequently, a conductor temperature pre-evaluation criterion is established, which links the ambient, enclosure, and conductor temperature evolution characteristics. A temperature rise experiment platform was built to validate the model. The experiment results proved the correctness of TTRMM, and the temperature evolution trend of the conductor conforms to an exponential function and is divided into three stages. This article not only reveals the transient evolution mechanism between the ambient environment, enclosure, and conductor but also achieves the purpose of sensing the rate of temperature rise of the GIS busbar conductors by real-time monitoring of the ambient environment and enclosure temperatures.

High-tech systems require precise motion control in the subnanometer position error range. With no magnetic interference and low mass, piezoelectric actuators are ideal for sensitive and high-speed environments. The proposed piezoelectric actuator, a multi-layer piezo stack made of lead zirconate titanate (PZT) material, is affected by non-linearities (e.g., hysteresis, dielectric relaxation). Therefore, to deliver high accuracy, precise mathematical modelling is essential to enable control. Current mathematical models often fail to capture all static and dynamic effects related to charge and voltage prediction. This paper introduces a comprehensive physics-based model that holistically integrates key physical phenomena, including inertia and saturation in the operational hysteresis. In this work, the model's parameters are identified and its performance is validated against experimental data across a range of operating conditions. The results demonstrate that the proposed model achieves a superior prediction accuracy, improving performance significantly, as compared to established methods. This outcome confirms the model's ability to reliably predict actuator behaviour.

High-tech systems require precise motion control in the subnanometer position error range. With no magnetic interference and low mass, piezoelectric actuators are ideal for sensitive and high-speed environments. The proposed piezoelectric actuator, a multi-layer piezo stack made of lead zirconate titanate (PZT) material, is affected by non-linearities (e.g., hysteresis, dielectric relaxation). Therefore, to deliver high accuracy, precise mathematical modelling is essential to enable control. Current mathematical models often fail to capture all static and dynamic effects related to charge and voltage prediction. This paper introduces a comprehensive physics-based model that holistically integrates key physical phenomena, including inertia and saturation in the operational hysteresis. In this work, the model's parameters are identified and its performance is validated against experimental data across a range of operating conditions. The results demonstrate that the proposed model achieves a superior prediction accuracy, improving performance significantly, as compared to established methods. This outcome confirms the model's ability to reliably predict actuator behaviour.

High-tech systems require precise motion control in the subnanometer position error range. With no magnetic interference and low mass, piezoelectric actuators are ideal for sensitive and high-speed environments. The proposed piezoelectric actuator, a multi-layer piezo stack made of lead zirconate titanate (PZT) material, is affected by non-linearities (e.g., hysteresis, dielectric relaxation). Therefore, to deliver high accuracy, precise mathematical modelling is essential to enable control. Current mathematical models often fail to capture all static and dynamic effects related to charge and voltage prediction. This paper introduces a comprehensive physics-based model that holistically integrates key physical phenomena, including inertia and saturation in the operational hysteresis. In this work, the model's parameters are identified and its performance is validated against experimental data across a range of operating conditions. The results demonstrate that the proposed model achieves a superior prediction accuracy, improving performance significantly, as compared to established methods. This outcome confirms the model's ability to reliably predict actuator behaviour.

This paper presents a new cascade control based on type 2 fuzzy logic applied to direct current motors to regulate the mechanical power produced. The study aims to eliminate significant drawbacks of cascade control based on PI controllers to ensure robustness and stability, achieving precise and continuous control. A comparison between our cascade control structure based on type 2 fuzzy controllers and other cascade control structures based on PI controllers or type 1 fuzzy controllers is also the focus. The results in the Matlab/Simulink environment demonstrated the effectiveness and best performance of the cascade control based on type 2 fuzzy controllers.

The reconstruction of impact forces is critical for structural health monitoring of aero-composite wings. However, due to its complex structure with a limited sensor array, the impact force cannot be accurately determined simply by using an equal-weight transfer function. Meanwhile, the introduction of complex models can improve the accuracy of reconstruction but also increase the computational complexity and running time. To address this issue, a method combining lightweight spatiotemporal attention mechanism and extreme learning machine (ELM) (LSA-HELM) is proposed. By introducing a lightweight spatiotemporal attention mechanism, the input data are weighted to capture key features effectively. Then, the mapping relationship between impact force and strain array is constructed by using Hermite polynomials as the ELM of activation function. The suggested method is verified on an aircraft composite plate. The experimental results show that the peak relative error (PRE) is 4.62% for LSA-HELM, 11.03% for Bayesian, 13.33% for convolutional neural network (CNN), 7.31% for Tiny1DCNN and 9.82% for transformer. It shows under the condition of limited sample number and scarce data features, the proposed method has obvious advantages in terms of reconstruction accuracy and real-time performance and is superior to other methods based on machine learning and traditional analysis methods.

Reverberation chambers are essential facilities for electromagnetic compatibility testing, and their internal field uniformity critically determines the accuracy of test results. The current IEC 61000-4-21 standard primarily restricts the size of the Equipment Under Test (EUT) based on its physical volume, recommending it not exceed 8% of the total RC volume. However, this empirical guideline overlooks critical factors such as the EUT's electrical size and geometric shape, leading to significant limitations. This paper employs both the Plane Wave Superposition (PWS) method and full-wave Multi-Level Fast Multipole Method (MLFMM) simulations to systematically investigate the impact of various EUT types on field uniformity. Our findings reveal that an EUT's electrical size and shape are often dominant factors in perturbing the field uniformity, demonstrating that the physical volume ratio criterion alone is insufficient for ensuring test validity. Simulation results show that some EUTs compliant with the 8% rule still cause excessive field degradation, while some larger EUTs do not. The comparison between simulation methods also highlights that full-wave simulations, which capture the complete EUT-cavity coupling, are essential for accurately assessing the actual loading effect. This research provides in-depth analysis and a comprehensive simulation dataset that challenge the adequacy of the current standard.

A magnetisation estimation method that uses an autoencoder (AE) model with a convolutional neural network (CNN) is proposed. The proposed method achieved high accuracy even for complex magnetisation distributions, including defective regions. The proposed model improved estimation accuracy by over 50.62% compared to the conventional multi-layer perceptron method. Additionally, the proposed model is effective for multi-output regression problems involving multidimensional vectors. This method enables non-destructive and rapid estimation of internal magnetisation in permanent magnets from external magnetic flux density.

Electrostatic discharges (ESD) remain a significant challenge in electromagnetic compatibility testing due to their high-frequency spectral content and the increasing complexity of modern electronic systems, which incorporate higher integration densities and faster signal transitions. The upcoming revision of the system level ESD immunity testing Standard IEC 61000-4-2 introduces a new parameter regulating the second peak of the ESD current waveform produced by the ESD test generators. This revision is expected to render many existing ESD generators non-compliant, posing a costly issue for testing laboratories as it necessitates expensive modifications or complete generator replacements. To address this challenge, a cost-effective alternative is proposed, utilising ferrite clamps around the grounding cable of the ESD generator to modify the second peak parameters. A workflow-based methodology is introduced, including the identification of non-compliance, evaluation and ranking of ferrite configurations and an investigation of saturation effects to ensure sustained performance. Beyond ensuring compliance, this methodology can also be applied to optimise the second peak waveform parameters, improving the overall match to the ideal waveform of IEC 61000-4-2. By providing an effective and adaptable solution, this approach enhances test accuracy, improves reproducibility and reduces costs associated with equipment replacement and upgrades.