Iet Science Measurement & Technology最新文献

The 20 Hz low-frequency transmission technology is a new type of AC transmission technology based on fully controlled power electronic devices, which has gradually been applied in construction around the world. However, it is difficult to develop high-precision low-frequency standard transformers, resulting in a blank system for tracing the values of low-frequency transformers. Inductive voltage dividers have many advantages such as high accuracy and stability, so low-frequency voltage ratio standards are suitable for using inductive voltage dividers as the source of traceability. This article proposes the principle of two-stage excitation and develops a two-stage excitation low-frequency induction voltage divider. Based on the finite element method, a three-dimensional model was established to simulate and optimize the internal electromagnetic field and structure. Designed a closed shielded iron core structure, which has achieved good magnetic field shielding effect; In order to reduce the impact of capacitive leakage, the proportional winding adopts a coaxial cable 10 wire parallel winding method to achieve outer equipotential shielding. Based on the reference potential method, this device is calibrated for errors. The error measurement device uses a lock-in amplifier SR850. According to the error calibration results, the 10 stage transformation ratio error of the 1 kV two-stage excitation low-frequency induction voltage divider is better than 1 × 10⁻⁷.

Internal arc faults in cables are a significant cause of cable fires, with the cable temperature being time-dependent due to varying loading levels. Thus, it is necessary to study the characteristics of arc-induced cable ignition considering the influence of temperature, which have been rarely addressed in previous studies. In this paper, an experimental platform for alternating current arc generation is established. This platform records and analyses the arc development's morphological characteristics, the voltage and current waveform changes at both electrode ends, and the electrodes' temperature distribution. Moreover, two types of cable insulation materials, i.e. cross-linked polyethylene (XLPE) and polyvinyl chloride (PVC) with different thickness and temperature, are tested to study the arc-induced ignition characteristics of cables. By comparing ignition times and combustion intensities within the same time frame, the characteristics of arc-induced ignition are analysed. It can be concluded that the insulation temperature accelerates the arc-ignition process significantly. For XLPE, the ignition time under 70°C can be reduced around 11%. Thick XLPE (1.6 mm) can delay the ignition time up to 1.5 times compared with thin one (0.8 mm). PVC exhibits similar behaviour but demonstrates greater resistance to arc-induced ignition than XLPE.

Metal tip defect is a typical insulation defect in gas insulated switchgear (GIS). Simulation research on the defect under multi-physical field coupling was conducted in order to explore the realistic electric field distribution. The results of the simulation were confirmed by a partial discharge experiment. On the one hand, the influence of defect location and structural parameters on electric field distribution was investigated in the established defect model. On the other hand, the partial discharge experimental platform was built to obtain the initial voltage of partial discharge under this defect. The results show that the presence of the defects can increase the electric field distortion by several-fold or more in the surrounding electric field. The closer the defect on the outside of the conductor is to the basin-type insulator, the lower the electric field distortion is around the conductor. However, the opposite is true on the inside of the shell. The change in the top radius of the defect has the greatest impact on the electric field distribution. The experimental results verify the validity of the simulation model. This study augments the research on insulation defects in GIS, offering a valuable reference for the manufacturing and installation of GIS.

The co-frequency co-time full duplex (CCFD) system transmits and receives signals at the same frequency simultaneously, which can effectively improve the communication spectrum utilization of satellite and solve the problem of satellite frequency resource constraint. However, due to a large number of devices in a relatively small space, there is strong self-interference between transmitting and receiving antennas. Besides, the received signal intensity is not stable due to other factors such as multipath effect, electromagnetic interference, and so on. To solve this problem, this paper proposes a machine learning method for self-interference suppression of CCFD systems in digital domain. A signal sequence decomposition method based on the number of poles is used to solve the problem of insufficient suppression of nonlinear interference signal caused by signal strength instability. By using Hilbert transform, the input signal dimension of machine learning is reduced, which is to solve the high computational complexity. The short-term and long-term neural network architecture is adopted to predict and reconstruct the interference, and Bayesian optimization method is used to optimize the hyperparameters of the network, and introduces the acquisition function into the loss function, to improve the comprehensive training optimization of machine learning networks. The experimental results show that the proposed algorithm can effectively analyze and suppress the interference signal, and the interference suppression capability is improved by 10.7 dB compared with the traditional Long Short-Term Memory algorithm and 21.1 dB compared with the Least Mean Square algorithm. The algorithm presented in this paper is significant for the application of CCFD system on satellite.

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.