Iet Science Measurement & Technology最新文献

To solve the problem of low accuracy of the previous electricity theft detection methods, the authors propose a multi-domain feature (MDF) fusion electricity theft detection method based on improved tensor fusion (ITF). Firstly, the original electricity consumption series is transformed by gram angle field (GAF) to obtain the time-domain matrix. The original electricity consumption series is converted into frequency-domain by Maximal Overlap Discrete Wavelet Transform (MODWT) to obtain the frequency-domain matrix. Then, the convolutional neural networks (CNN) are used to extract features of the time-domain matrix and frequency-domain matrix, respectively. Next, in order to fuse single-domain feature information and MDF interaction information while reducing redundant information, the authors propose an ITF method to obtain a multi-domain fusion tensor. Finally, the multi-domain fusion tensor is input into the electricity theft inference module to judge whether the user implements electricity theft behaviour. The authors simulate six electricity theft types and evaluate the method's performance separately for each electricity theft type. The results show that the proposed method outperforms other methods.

It is difficult to separate Luer lock adapters during the external ventricular drainage (EVD) since they are often connected by a threaded connection, and the infusion needle put into the patient's body gets pulled out when extubation occurs unexpectedly. The paper described a new magnetic connection assembly made up of ring magnetic permanents and a Hall sensor that could be used to replace Luer lock adapters for quicker disconnection and reconnection of the drainage catheter. For the design and analysis of the device, the finite element method magnetics (FEMM) was used to estimate the size of magnetic permanents and the best detection site of the Hall sensor for detecting whether the magnetic permanents were closely attracted and/or pulled apart. Then an experiment was conducted to confirm our findings. Both simulation and experiment revealed that a pair of hollow cylindrical magnetic permanents, each with height 10 mm, outer diameter 8 mm, and inner diameter 4 mm, could achieve the same force as the Luer lock adapters held, and that the best Hall sensor detection position was in the middle of the permanent magnets, with a radial distance of about 2 mm.

To accurately diagnose the XLPE power cable insulation fault, this research proposed a novel hybrid algorithm combined with Convolutional Probabilistic Neural Network (CPNN) based on Discrete Wavelet Transform (DWT) and Symmetrized Dot Pattern (SDP) analysis. First, it built seven different power cable insulation defect models to measure partial discharge signals of power cable insulation faults. Then, a discrete wavelet transform was used for noise filtering. The time-domain partial discharge signal was directly converted into the point coordinate feature image of visual polar coordinates by SDP analyses. Finally, the feature image was trained and recognized by CPNN. After the important feature information of the feature-image was extracted by convolution layer and pooling layer operations, it is applied to the power cable insulation fault state diagnosis system based on the rapid learning and highly parallel computing of Probabilistic Neural Network (PNN). The experimental results proved that the method proposed in this research could accurately diagnose the power cable insulation fault type and the recognition accuracy is higher than 96%. The proposed method has a short detection time and high diagnostic accuracy. This proves that it can be applied to detect the power cable insulation fault type.

This paper reports on the partial discharge (PD) erosion characteristics of polypropylene (PP)/elastomer blends where ethylene-octene copolymer elastomer (EOC) and propylene-based elastomer are involved. The PD erosion is performed through a pair of needle-to-plane electrodes by applying 5 kV AC voltage for 2 h. A high-frequency current transformer, optical recorder, and ozone sensor are used to capture the information released during the PD erosion process. In addition, a 3D optical profilometer is employed to record the erosion profile, from which the erosion depth and the surface roughness can be extracted. Carrier trap distribution is derived from the isothermal surface potential decay measurement. The influences of elastomer type and content on the PD resistance are analyzed. The results show that the resistance is improved with the addition of elastomer, and the blend containing EOC of 20 wt% has the best PD resistance among all samples, which is 61.9% higher than that of the pure PP. It is proposed that the carrier trap characteristics, the property of elastomer, and the free volume in the material play important roles in the improvement of the PD resistance. This work provides a potential method to enhance the PD resistance of PP by blending elastomer with proper type and content.

Vehicles such as automobiles, ships, satellites, and aircraft have a limited amount of physical space to install antennas for communications and navigation systems. This is exacerbated by the use of modern materials, like carbon fibre, and that large areas of the vehicles structure cannot be used to mount antenna, due to aerodynamic or other requirements. Therefore, it is necessary to be able to quickly and accurately find the optimum locations to mount a number of antenna systems, in a restricted space, whilst considering a number of different and sometimes contradictory antenna performance parameters. Thus, defining the optimum antenna locations is a multi-objective problem (MOP) and lends itself to the use of multi-objective evolutionary algorithms (MOEA). This paper presents a MOEA methodology that can be used to accurately, quickly, and robustly define the antenna locations. It will also define an appropriate MOEA and the fitness functions for predicting the radio frequency (RF) interoperability/mutual coupling between antenna systems and antenna RF radiation pattern installed performance.

The humidity role in the partial discharge (PD) inception mechanism is quite challenging, especially when considering the environmental temperature. Indeed, there is no general rule to explain the humidity effect on the PD phenomenon. In this paper, the PD activity in inter-turn insulation is experimentally investigated for different relative humidity (RH) conditions at three different ambient temperatures, that is, 30°C, 60°C, and 90°C. Partial discharge inception voltage (PDIV) is directly measured through a photomultiplier tube (PMT), whereas the tip-up tests are performed aiming at monitoring both dissipation factor (tanδ) and insulation capacitance (IC). These extra measurements (diagnostic dielectric markers) allow better assessing the insulation status. The adoption of the tip-up test enables the insulation properties measurement. Based on the tip-up tests’ findings, the interfacial polarization process starts at 75% RH under 60°C, while the high conductivity area is already formed at 75% RH when the ambient temperature is 90°C. The water film formation deduced from the tip-up test is then used to explain the trend of PDIV, and the validity is further proved by finite element analysis (FEA).

Touch voltages are more likely to appear on long-distance pipelines that lie in shared corridors with transmission lines. The touch voltage level varies with soil resistivity and can become hazardous while maintenance is being undertaken, necessitating risk assessment. Therefore, a novel approach is proposed that achieves better accuracy in the risk assessment of hazardous touch voltages appearing on pipelines than current approaches. The proposed approach incorporates soil resistivity measurements taken over a 1-year period, allowing for the derivation of soil models, touch voltage hazard profiles versus time and, thus, a probability reduction factor that can be integrated into a probabilistic risk calculation. Using a probability reduction factor is a key aspect to improving the accuracy of risk assessment, allowing pipeline asset operators to clearly identify whether or not, risk mitigation action is required. Further, the proposed approach allows appropriate risk mitigation actions to be identified. The efficacy and significance of the proposed approach are demonstrated through a case study on an area of land near a shared gas pipeline and transmission line corridor. The case study results show how appropriate safety levels can be achieved by adjusting the number of work day per year and temporary protection settings.

Partial discharge (PD) localization is critical to ensure the safe operation of power equipment. In this paper, an optimization scheme is developed for PD positioning of external insulation equipment. The optimization scheme is based on an eight-element cross sensor array to receive the ultrasonic signal generated by the PD, and uses the highly accurate 2-D multiple signal classification (2-D MUSIC) algorithm to locate the signal. Simulated discharge signal is adopted to test the optimization scheme, and the results show that the positioning error is less than 0.61° when the signal-to-noise ratio (SNR) is above −5 dB. In addition, three discharge models are tested at various locations. The average azimuth errors are 0.9°, 1.9°, and 1.55° for the needle-plate, cone-plate, and ball-plate discharge models, respectively, and the average pitch angle errors are 0.75°, 2.1°, and 1.4°, respectively. These show that the needle-plate model has the best positioning effect. Simulations and experiments are also carried out on the double discharge source scenario, and the error is within 2.2°. A PD location visualization equipment based on the proposed optimization scheme is developed and applied on-site. The equipment is capable of satisfying the requirements of operation and maintenance.

In this paper, a new technique based on the manipulation of stator current signature for induction machines fault diagnosis is introduced. The goal of the proposed method is to demodulate the characteristic frequencies from supply frequency and preserve the information of the supply frequency and its harmonics. The proposed method can be easily implemented and used in experimental systems due to its low computational complexity. The validity of the proposed method is proved through theoretical analysis and experimental results in steady-state and transient conditions. In this regard, the 270-W wound rotor induction machine (WRIM) is tested under different fault severities and load levels.

Traditional offline cable diagnosis methods need power outages during detection, affecting power supply reliability. Here, a hierarchical diagnosis method of cable aged segment based on transfer function is proposed. Firstly, the calculation model of cable transfer function with the aged segment is established; on this basis, the correlation between transfer function and cable aging is analysed. Then, a structure with combined sparse autoencoder and convolutional neural network is trained to estimate the aging location, and a hierarchical diagnosis model of distribution cable based on transfer function is proposed. The sensitivity and accuracy of aged segment detection are improved after hierarchical diagnosis. Finally, the simulation results show that the method proposed in this paper can effectively realize the online identification and location of the cable aged segment. The proposed method makes use of the advantage that the cable transfer function can be obtained online. Compared with the existing methods, this method does not need power outages in the diagnosis process, and the aged segment can be located without a lot of additional equipment.