Iet Science Measurement & Technology最新文献

The low-frequency transformer is a key measurement equipment for low-frequency transmission engineering; the accuracy level of the low-frequency transformer is generally difficult to be better than 0.05% due to the influence of core depth saturation at low frequency. Explore low-frequency current proportional converters with higher accuracy, such as zero-flux current transformers or compensated current comparators, because the current proportional standards of the above principles all work in the state of zero magnetic flux of the iron core, which is less affected by frequency and can be used to trace the value of low-frequency current proportional value. In view of this, a self-balancing low-frequency current comparator was developed to compensate the excitation current of the iron core by using electronic circuits. In this paper, the basic principle of this low-frequency self-balancing current comparator is expounded, its error performance is analysed theoretically, and the error calibration test is carried out. On the surface of the test results, the accuracy of the low-frequency self-balancing current comparator meets the requirements of class 0.001, which can be used for the traceability of the measurement value of low-frequency current.

This research proposes a data processing pipeline employing Fourier analysis and deep neural networks to replicate the phenomenon of magnetic hysteresis, in particular frequency components derived from experimental data gathered using a newly developed 3D-printed material. The characterisation of hysteresis is essential for enhancing material performance and constructing precise models to anticipate material behaviour under diverse operating circumstances, especially in 3D-printed materials where properties can be meticulously regulated to ensure successful applications. The experimental signals were used for training and testing a neural network, exploiting Fourier coefficients to condense signals into the frequency components. This compression extracts fewer parameters and thus reduces and optimises the resources required by the neural network. It also improves the generalisation performance of the model, allowing it to make more accurate predictions on unseen data. This therefore optimises traditional modelling that requires a complete representation of hysteresis loops in the time domain, which must be addressed with the use of complex neural networks and large datasets. The experimental results show lower computational costs during the prediction process and a smaller memory footprint. Furthermore, the proposed model is easily adaptable for the loss estimation in different types of materials and input signals.

Due to the untimely deployment of sensors and the early retirement of high-voltage circuit breakers, life-cycle data is missing, leading to an inability to accurately predict the mechanical performance degradation trend. A new modelling and prediction method of mechanical performance degradation of high-voltage circuit breakers considering censored data was proposed. Firstly, multiple imputation by chained equations was used to impute the missing values in the dataset of circuit breaker closing time, creating an interpolated dataset. Secondly, the Nadaraya–Watson kernel regression method was employed to smoothly estimate the interpolated dataset and eliminate data measurement errors. Then, the functional principal component analysis method was utilized to extract the common degradation trend component and deviation component to construct the degradation model. Finally, Bayesian inference was applied to dynamically update the degradation model parameters and predict the degradation trend of the high-voltage circuit breaker. The results showed that the proposed method was capable of achieving better interpolation accuracy under the condition of different closing time censored data of high-voltage circuit breakers. Moreover, as the degradation progressed, the dynamic prediction effect improved. The research can be used to provide an effective decision-making basis for the operation and maintenance strategy of high-voltage circuit breakers.

Micro-electro-mechanical system (MEMS) technology is becoming increasingly popular in geotechnical and hydrological monitoring due to their distinct features, including its small size, low cost, low energy consumption, and resistance to vibration shock. Due to the nature of design, they exhibit a series of errors, and most importantly, hysteresis and nonlinearity, which should be compensated before any practical application in order to achieve the highest degree of accuracy and precision. This article proposes a practical approach to enhance the accuracy of a newly developed MEMS instrument for groundwater monitoring. The hysteresis and nonlinearity errors were compensated using an integrated two-step approach with an optimized Preisach model and an optimized spline approximation, respectively. A hybrid differential evolution-hill climbing algorithm was utilized to achieve optimum models. This optimization algorithm integrates global and local search, offering higher accuracy and computational stability with a lower calibration point requirement. Analysing the results indicates that the nonlinearity error shows an improvement of more than 94% and hysteresis decreased up to 67%. The results illustrate that the compensation can be performed very well using the proposed methodology with lower uncertainty, mean square error, and standard deviation compared to non-optimized models.

The acoustic signals in the operation of low-frequency transformer have the state information of the body, which can be monitored online by arranging acoustic measuring points. An optimization method for the layout of acoustic measuring points for low-frequency transformers is proposed in this study. First, a model of the low-frequency transformer acoustic field is constructed by measuring the environmental size of the transformer acoustic field. Then, the virtual acoustic source measured by the transformer acoustic signal is input into the model, and the time-varying acoustic pressure in the acoustic field is calculated. Finally, the spectral correlation coefficients between the acoustic signals at the candidate measurement points and the virtual acoustic sources are calculated to evaluate and improve the layout of the measurement points for the low-frequency transformer. The method in this study was used to improve the layout of acoustic measurement points for the low-frequency transformer in the frequency conversion station in Hangzhou. The results show that the method can effectively evaluate the monitoring ability of the acoustic measurement points on the overall acoustic state of the transformer in different positions. The study provides a reference for the layout optimization of the acoustic online monitoring device for low-frequency transformers.

The International Electrotechnical Commission (IEC) has introduced the IEC 61967-8 stripline measurement method for the measurement of radiated emissions in integrated circuits (ICs). This method stipulates that the stripline's voltage standing wave ratio (VSWR) must be less than 1.25 within the frequency range of 150 kHz to 3 GHz. The established IEC 61967-8 stripline measurement method can accurately identify the location of EMI in an IC and hence mitigation measure can be effectively implemented. In this work, the IC stripline design is adapted for ICs with various functions and applications with the aid of Ansys HFSS 3D full-wave simulations and actual measurements to study the impedance and reflection characteristics of IC striplines on test boards. A novel IC stripline structure design process is proposed to enhance the VSWR performance of IC striplines. Subsequently, a novel IC stripline has been fabricated and tested using the IEC 61967-8 stripline measurement method. The designed novel IC stripline demonstrated compliance with IEC 61987-8 specification and the measurement results showed consistency with the simulation results. The works can benefit the promotion of high frequency circuit design and the development of testing process and in turn, enabled the improvement of the reliability and performance of the designed products.

The programmable Josephson voltage standard (PJVS) provides a new way and higher accuracy for the traceability of traditional measurement standards. To improve the applicability and reduce the uncertainty of the PJVS, a PJVS-based analog-to-digital converter (ADC) scheme for the alternating-current (AC) voltage measurement is proposed and the theoretical error model is derived based on discrete Fourier transform. The influence of the amplitude and phase error of the AC voltage is simulated; an adaptive differential-sampling control algorithm is proposed to control the relative amplitude and phase between the AC voltage and PJVS voltage, improving the error of the PJVS-based ADC. Furthermore, the authors obtained the exponential decay algorithm error curve that varies with phase error and the M-shaped variation curve with sampling error by simulation and a PJVS-based ADC with different sampling DVM, which provides possibilities for optimizing the measurement error of the PJVS-based ADC.

Transcutaneous microwave dielectric spectroscopy holds promise as a modality for non-invasive subcutaneous biomarker monitoring, such as blood glucose levels. Experimental studies have demonstrated a strong correlation between blood glucose concentration and blood permittivity using microwave signals. However, the sample volume of interest, blood vessels, is located underneath skin layers where the microwave signal interaction with other surrounding biomaterials is accumulated. Here, a computational study of the proposed method to mitigate the contribution of unwanted tissue and thereby increase the specificity of subcutaneous spectroscopy, using differential probing analysis (DPA), is presented. By exploiting the relationship between the diameter of an open-ended coaxial probe (OCP) and the size of its protruding electric field, two OCPs with different sizes can sense different volumes beneath the skin. The proposed method computationally subtracts the effect of unwanted tissue, estimated with the smaller probe, from the effective contribution of both blood and unwanted tissue with the larger one. The method is validated by using simulated models and data from the full wave software CST Studio Suite across frequencies ranging from 1 to 10 GHz, achieving errors comparable to state-of-the-art techniques.

In the era of Industry 4.0, there is a growing emphasis on the digitization of electrical networks. Over recent decades, the integration of interconnected digital technologies, including sensors and communication systems, within electrical substations has emerged as a significant driver. Consequently, there is an increasing need for precise online monitoring of critical assets such as power transformers to enhance grid reliability. This study utilizes an optical-based Fiber Bragg Grating (FBG) sensor to capture vibration signals from a custom-designed single-phase transformer model, specifically developed for experimental purposes. This model offers a unique advantage with its ability to interchangeably simulate healthy and distorted winding sections without causing damage. Using a high current source, the laboratory model was subjected to three different current levels across six distinct configurations to monitor winding displacements. The results from this investigation highlight the FBG sensor's capability to accurately distinguish between healthy and distorted winding sections. Furthermore, this feasibility study represents a significant step forward in the online mechanical assessment of transformer windings, moving away from traditional methods that require transformers to be taken out of service for inspection. This innovative approach shows considerable potential for implementing effective real-time monitoring of winding deformation in power transformers.

This paper presents a fast hysteresis loss analysis method based on neural network, which predicts the hysteresis loss from the waveform of the magnetic flux density in a magnetic core. The hysteresis loss data used to train the neural network is computed using the play model which can accurately account for hysteresis characteristics such as minor loops and DC bias. The proposed method can perform loss analysis 2,000 times faster than conventional methods. The proposed method is highly effective when harmonic components are superimposed on the input waveforms and when loss analysis is performed for a large number of different waveforms.