Iet Science Measurement & Technology最新文献

In order to monitor the state of bushing online, an intelligent monitoring system for transformer bushing was developed. A four-in-one sensor integrating hydrogen sensing technology using palladium nickel alloy, pressure sensing technology, wide range temperature sensing, and micro water measurement technology was developed. A three-in-one integrated sensor based on micro current detection technology was developed to realize online monitoring of bushing dielectric loss, capacitance, and partial discharge. The test results show the hydrogen measurement range of sensor is 0 to 10,000 μL/L, and the measurement uncertainty is lower than 10% or 10 μL/L. The pressure measurement range is 0 to 1.0 MPa, and the uncertainty is lower than 0.3%. The temperature measurement range is −40°C to 85°C, and the uncertainty is lower than ± 1°C. The micro water measurement range is 0 to 1000 μL/L, and the measurement uncertainty is lower than ± 5% or 10 μL/L. The dielectric loss and capacitance error increased by one order of magnitude compared to current standards. The resolution of partial discharge is 5 pC. The performance of the device fully satisfies the requirements for online monitoring of transformer bushing. It has been installed in dozens of 330 and 750 kV substations, providing a reliable guarantee for safe operation of transformer bushing.

PV fault diagnosis remains difficult due to the non-linear characteristic of PV output, which makes PV output to be likely disturbed by the ambient environment. This study proposes a novel convolutional extension neural network (CENN) algorithm, which is a jointed architecture based on convolutional neural network (CNN) and extension neural network (ENN), takes advantage of CNN and ENN. The CENN is combined with the symmetrized dot pattern (SDP) analysis method to diagnose the common eight PV array faults. The SDP is used to transform the measured PV signals into the point coordinate feature image; then, the CENN is trained to identify the different PV faults. Experimental results show an obvious improvement in short detection times and high accuracy compared with traditional CNN and the histogram of oriented gradient (HOG) extraction method with support vector machine (SVM), K-nearest neighbours (KNN), and back propagation neural network (BPNN) classifiers, with 95.3%, 94%, 93.5%, and 93.3% accuracy, respectively. Using the proposed CENN, the accuracy can be raised to 97.3%. Additionally, the signals measured by various sensors are collected using programmable logic controller (PLC). The human–machine interface (HMI) and the proposed algorithm are developed using LabVIEW for graphical design. Finally, the information is transmitted to a tablet PC for performing real-time remote monitoring.

The de-embedding calibration method has been proposed to achieve high-precision calibration for a single port electric field or magnetic field probe, which can effectively eliminate the calibration ripple. However, the method's effectiveness for a four-port calibration system has not been verified yet. In this paper, a four-port de-embedding calibration method with a differential magnetic field probe is proposed, and its effectiveness is proved. Two symmetric grounded coplanar waveguide transmission lines are applied in the proposed method to solve the ABCD-matrix of the embedded part of the calibrator. The de-embedded S-parameter model of the four-port calibration system for differential magnetic field probe can be obtained. The calibration results indicate that the proposed method can also reduce the calibration ripple and compensate for the attenuation caused by the calibrator. Compared with the traditional calibration method using a microstrip line calibrator, the ripples of the proposed method can be reduced by 34%. The analysis results of the frequency interval of the ripple (FIR) in different methods show that the de-embedding method can reduce the FIRs (except around 1.2 GHz) caused by the reflection of the calibrator and retain the FIR (about 1.2 GHz) caused by the reflection of the probe itself.

A large number of monitoring sensors are introduced in the power grid. However, the traditional trust models commonly used for edge-side security management are weak in detecting large-scale malicious interactions and collusion attacks. For that, a lightweight and anti-collusion trust model combined with nodes’ dynamic relevance for the power Internet of Things (IoT) is proposed. Firstly, a global trust management system is constructed according to the working mechanism of sensors in the power grid. After that, trust feedback and contact frequency of the devices are combined to build an adaptive dynamic weight vector based on relevance volatility. Fluctuations in trust values are reduced and the trust difference between normal and malicious nodes is widened. An anti-collusion algorithm based on contact set awareness is also designed to effectively detect collusion attacks. The checksum local broadcast is established in the trust model to counteract the risk of intelligent terminal failure. The results show that the trust model achieves 100% accuracy of node discrimination when the maximum proportion of malicious nodes is 20% in a 50-node network scale. In addition, the calculation time of the overall model is 211 ms and the memory consumption is 161 kb, which is suitable for power IoT sensor networks.

This paper proposes a feature extraction method combining adaptive variational mode decomposition (AVMD) and singular value decomposition (SVD) for electric shock fault-type identification. The AVMD algorithm is utilized to adaptively decompose the electric shock signal into intrinsic mode components, each containing distinct frequency information. Subsequently, the correlation coefficient is employed to extract the intrinsic mode component with amplitudes greater than or equal to 0.1 ($�{\gamma }_k$ ≥ 0.1). Feature extraction is then performed using SVD on the $�{\gamma }_k$ ≥ 0.1 intrinsic mode component, based on its maximum singular value and singular entropy. This approach effectively overcomes the limitation of the traditional VMD that necessitates manual K value setting. Moreover, it achieves dimensionality reduction and feature extraction of the intrinsic mode components through SVD, resulting in enhanced computational efficiency and fault identification accuracy. Extensive simulations demonstrate the remarkable recognition rates of electric shock fault types in animals and plants using the proposed AVMD-SVD method, achieving a recognition rate as high as 99.25%. Comparative performance analysis further verifies the superiority of AVMD-SVD over similar empirical mode decomposition-SVD feature extraction techniques.

This study addresses the challenge of motion control in autonomousvehicles through the introduction of a novel profile generation design. Specifically,autonomous vehicles often contend with uncertain factors and physicallimitations within their operational environments, such as abrupt changes inacceleration or intricate parametric motion profiles. To tackle this problem, afiltering technique for motion profile generation is proposed, leveraging ahardware programming language as its foundation. The investigation begins byanalyzing the specific structure of the mobile platform, which includes twoactive side wheels, two passive rear wheels, a high load capacity, and adifferential drive mode. Building upon this theoretical basis, the proposedfiltering technique is introduced to attain smooth motion profiles and optimizetiming. Furthermore, the study suggests the use of FPGA (Field ProgrammableGate Array) acceleration to expedite these computations for swift motionprocessing. To validate the efficacy of the proposed method, both the mobilevehicle and the load are simulated as a one-axis linear ball-screw system withan aluminum ruler. The experimental results unequivocally demonstrate theeffectiveness, feasibility, and applicability of the proposed approach across avariety of industrial solutions.

Low-frequency noise, generated inherently by the number or mobility fluctuation of carriers, is a crucial concern for the design of analog and digital circuits. Unified modelling based on experimental validation of near-DC noise in amplifiers is a long-standing open problem. This article develops a model for low-frequency noise by deriving new bounds for carrier capturing and releasing. According to the proposed model, a measurement system is suggested that operates in a wide frequency range and even at very low frequencies. The system is noise-tolerant, since the amplifier is selected based on acceptable noise levels. Among the advantages are the independence from specialized structural noise models for each component and the low cost of the measurement system. The evaluation results show that the proposed method leads to a promising improvement in the low-frequency noise measuring and is superior to conventional models in the normalized root mean square error indicator. Findings reveal that the proposed measurement method can estimate the flicker noise around the DC frequency, and the proposed model agrees reasonably with the proposed measurement circuit.

In order to improve the electrical tree resistance of epoxy resin, composites were prepared using dihydroxydiphenylsilane and a novel silicone modifier modified epoxy resin and micron silica, and the composites were subjected to accelerated thermal aging test and electrical tree test and recorded the growth process of electrical trees to further obtain the characteristic parameters of electrical trees in the experimental samples. The results show that the silicone-modified epoxy resin/silica composite has better thermal stability and electrical tree resistance than the unmodified epoxy resin/silica composite and pure epoxy resin. Analysis of the scanning electron microscope (SEM) and breakdown field strength results show that the introduction of the modifier enhanced interfacial properties between the epoxy resin and silica. At the end of the electrical tree test, the length of the electrical tree in the silicone-modified epoxy resin/silica composite was at a minimum 25.31% of the length of the electrical tree in the unmodified epoxy resin/silica composite with the same silica filling ratio, and 9.19% of the length of the electrical tree in the pure epoxy, while the electrical trees in the silicone-modified epoxy resin/silica composite have lower expansion factors, as well as higher fractal dimensions, compared to those in the resin without added silica.

The diffusional process involving elastic collisions between charge carriers and neutrals has long been the predominant candidate for post-injection momentum loss in the far wider drift region outside confined DC corona discharges in gases. A supplementary research paradigm is here put forward for the interest of a greater understanding of corona-assisted unipolar ion flows in atmospheric air. In this respect, the auto-pulsing mode of glow-type coronas in air is modelled as a source of momentum whose conservation is substantially preserved across the external transfer region. The tangible prospect of charge-bearing flows in the form of a filamentary convection, with negligible drifting component, in buffer gas is put forward in relation to spontaneous symmetry-breaking instability. This setting bears some resemblance to the self-organised collective behaviour of self-propelled (active) particles. The value of this scheme gains strength precisely in relation to the strong inhomogeneity of the electric field surrounding electrodes prone to go into corona. On such a basis, it is believed that the drift of corona-generated ions deserves to be reconsidered in view of the arguments set out in the present modelling.

The restrike of vacuum circuit breaker during the breaking process causes overvoltage and intensifies the insulation deterioration of high-voltage equipment. To solve this problem, the vacuum breaker aging test method and the restrike characteristics are researched. A withstand voltage test platform of 40.5-kV vacuum circuit breaker was established to obtain the effects of the contact spacing and loop and inrush currents of vacuum circuit breaker on the number of restrike, the wavefront steepness of the breakdown voltage, and the breakdown duration. The number of restrike during the breaking of vacuum circuit breaker decreased and the duration of the repeated-breakdown arc increased with the increased contact spacing of vacuum circuit breaker. As the loop current of the test system increased, the number of restrike during the breaking of vacuum circuit breaker, the wavefront steepness of the breakdown voltage, and the duration of the restrike arc increased. As the inrush current amplitude increased during the closing of vacuum circuit breaker, the number of restrike and the duration of the repeated-breakdown arc during the breaking of vacuum circuit breaker increased. The results provide a theoretical basis and data support for the preventive test of 40.5-kV vacuum circuit breaker and the optimization of the withstand voltage test.