Iet Science Measurement & Technology最新文献

Varistor coupling is recommended by International Telecommunication Union in combination wave test waveforms of surge protective devices (SPD), although the varistor coupling method and recommendation parameters for the combination wave test are not well understood. The waveform influencing characteristics introduced by varistor coupling are studied in detail in this paper. Low-voltage varistors, with maximum operating voltages ranging from 130 to 750 V, are adopted here in the OrCAD/PSpice environment to analyze the influence of varistor coupling on the output characteristic of the combination wave generator (CWG) in test levels of 0 to 20 kV. Simulation results show that, owing to varistor coupling, the reverse oscillation phenomena will arise in the open-circuit voltages (OCVs). Also owing to varistor coupling, compared with those generated directly by a CWG, the peak value, the front time, and the tail time for both OCV and short-circuit current will decrease, whereas the virtual impedance will increase. Moreover, the lower the test level, or the higher the maximum operating voltage of the varistor, the more serious this effect. Experiments have also been conducted here, and our simulation results are found to match the experimental ones well. It seems that, compared with the conventional capacitor coupling, the range of the voltage of power supply networks and the levels of combination wave test by varistor coupling have to be limited to a great extent. Varistor coupling for combination wave test of SPD should be only used in special cases and special test levels where there are no strict waveform limitations.

By correcting the photon area factor (PAF) to suit the structure of the spacer and redefining the effective ionization region of the process of negative corona onset, a physical model for the calculation of COG of AC spacers is established. The validity and practicability of the established physical model are validated by the corona onset test results of spacers in both low-altitude and high-altitude areas. The numerical results pertaining to PAF and effective ionization region are consistent with the theoretical analysis. With the established physical model as a basis, the COG of the spacer is calculated under different geometrical structure parameters and ambient conditions. The results imply that changing the curvature of the spacer edge has a more significant impact on the COG of the AC spacer than changing the curvature of the spacer end and thickness H. The decrease in the length of the ionization region and the rapid attenuation of spatial electric field intensity of the spacer with smaller geometric parameters lead to an increase in the COG.

Faults in rolling bearings are usually observed through pulses in the vibration signals. However, due to the influence of complex background noise and interference from other machine components present in measurement equipment, vibration signals are typically non-stationary and often contaminated by noise. Therefore, in order to effectively extract the random variation and non-linear dynamic variation characteristics of vibration signals, a new method of rolling bearing fault diagnosis based on generalized multiscale mean permutation entropy (GMMPE) and grey wolf optimized least squares support vector machine (GWO-LSSVM) is put forward in this paper. Based on the multiscale permutation entropy (MPE), the multiscale equalization is firstly used to replace the coarse grained process, and the value of mean is extended to variance to avoid the dynamic mutation of the original signal. Finally, the parameters of LSSVM are optimized by the grey wolf optimization algorithm to achieve accurate identification of fault modes. The results of simulation and experiment show that applying the proposed GMMPE to rolling bearing fault feature extraction is feasible and superior, and the method based on GMMPE and GWO-LSSVM has better noise robustness, which can effectively achieve rolling bearing fault diagnosis.

In this paper, an improved two-step method is presented for the sensitivity analysis of vector network analyzer (VNA) S-parameter measurements due to the non-ideal line-reflect-match (LRM) calibration standards. This improved method is based on the indirect uncertainty propagation mechanism, which is especially suitable for the S-parameter measurements applying the self-calibration technique. To further simplify the formula derivation, the deviation matrices [δA] and [δB] are newly defined to represent the uncertainties of the T-matrices of error boxes. With this definition, formulas for the deviations of device under test (DUT) S-parameters can be concluded as functions of [δA] and [δB] in a concise form. Eventually, by solving only three linear combinations of the elements from [δA] and [δB], the sensitivity coefficients of DUT S-parameters due to non-ideal LRM can be conveniently deduced in an analytical form. Finally, experiments are performed to verify the proposed method.

The converter transformer is an essential part of the DC transmission system. Compared with the traditional oil-impregnated AC transformer, the main insulation of the converter transformer bears more complex electric field aging stress during long-term operation. The influence of the proportion of AC components in the AC/DC composite electric field on insulation aging is still unclear. Therefore, a combined aging test platform of composite electric field and thermal was built in the laboratory, and accelerated aging tests of oil-paper insulation under different AC/DC ratios were carried out. Through the time-frequency domain dielectric response characteristics of oil-paper insulation, the quantitative relationship between the time-frequency domain dielectric response characteristic parameters and AC proportional coefficient in different aging stages was obtained. The results show that the influence of the AC component on the aging of the oil-impregnated pressboard is more prominent. The maximum relaxation polarization time and the maximum exponential coefficient of polarization–depolarization current (PDC) can effectively characterize the aging of oil-paper insulation. Meanwhile, to accurately assess the insulation state of the converter transformer, this paper established the equivalent dielectric relaxation model for the main insulation structure. A quantitative assessment method for moisture content and aging of oil-paper insulation based on time-frequency domain dielectric response was proposed. The influence of transformer oil conductivity, test temperature, and main insulation structure was eliminated. The effectiveness of this method was verified by comparative tests, the maximum error for DP is 20, and the maximum error for moisture content is 0.15%. The research results of this paper can provide theoretical support for on-site assessment of converter transformer insulation status.

Partial discharge (PD) signals have a large amount of data and a low energy proportion of pulse signals, resulting in difficult data transmission and poor reconstruction efficiency. To this end, a PD signal compression reconstruction method based on transfer sparse representation and dual residual ratio threshold (TSR-DRRT) is proposed. TSR-DRRT is centred on the sparse representation (SR) and accurate reconstruction of noisy signals. The intrinsic pulse of PD signals is extracted by signal decomposition, and jointly trained with different types of signals to establish the transfer SR dictionary. The compressed signal accurately retains the essential characteristics of the pulse information by improving the match between the dictionary atoms and the polymorphic PD pulses. To match the transfer SR dictionary, the inner and outer DRRT iteration termination conditions are set adaptively during the reconstruction process based on the correlation difference between the dictionary and signal frames. Independent control of PD pulse recognition and reconstruction accuracy is achieved, and its performance under noisy signals is improved. The results show that the method can achieve high ratio compression and efficient reconstruction of noisy signals. Different types of PD signals can also have high matching accuracy. This method can meet the demand for PD signals compression and transmission to the terminal for accurate reconstruction.

Air-insulated switchgear plays an important role in distribution network and partial discharge (PD) is one of the common faults in its operation. The method of gas component analysis, which is to detect chemical gas products, is a novel one to discover PD. In order to study the feasibility of the method, the monitoring experiment of characteristic gases, comprising carbon monoxide (CO), nitrogen dioxide (NO₂), and ozone (O₃), of metal protrusion PD is carried out. This paper conducts the comparative experiments on three main factors affecting PD: voltage, air humidity and degree of protrusion, as well as studies how they affect the formation of characteristic gases. According to the experimental result, higher applied voltage promotes the formation of characteristic gases in the same conditions. In addition, with the increase of humidity from 40% to 75%, the generation of gases is inhibited. Furthermore, when the needle–plate distance is 30 mm, the gas concentration has almost no change within 6 h, which is different from that of 10 mm. In conclusion, the difference in gases concentration reveals the influence of three main factors on the generation of characteristic gases and provides an analytical basis for PD detection by gas composition analysis.

Hong-shan, Z., Xiao-mei, G., Li-bo, M., Yan, W., Cheng-yan, S., Jie-ying, C.: A hierarchical diagnosis method of cable aged segment based on transfer function. IET Sci. Meas. Technol. 16, 512– 522 (2022). https://doi.org/10.1049/smt2.12125

In Section 0 Introduction, we missed an important reference ‘Wu Y , Zhang P , Lu G . Detection and location of aged cable segment in underground power distribution system using deep learning approach. IEEE Transactions on Industrial Informatics, 2021, 17(11):7379-7389.’ because of our mistakes. The code in this paper is developed based on the reference and we further make hierarchical diagnosis and consider the influence of cable joints to improve the sensitivity and accuracy of cable aging diagnosis.

We apologize for this error.

Here, performance of auto-encoder deep neural networks in detection and isolation of induction motor states (healthy, bearing outer race fault, stator winding short circuit and broken rotor bar) in the presence of unbalanced power supply and electro-pump dry running disturbances is investigated. Easily available three-phase electrical current signals are denoised using independent component analysis, and then the frequency-domain signal is used to train a neural network. A comparison is made between shallow and deep neural networks and also between the conventional structure of deep methods and the encoder–decoder structure in terms of training and test accuracy and robustness. In fact, the depth is increased and the effectiveness is investigated. At the end, it is shown that an encoder–decoder structure leads to the best result in terms of accuracy and robustness. The algorithms are examined experimentally, and the results show that the auto-encoder deep neural network can detect the aforementioned faults with a high reliability in the presence of disturbances.