Insulation Defect Detection of Electrical Equipment Based on Infrared and Ultraviolet Photoelectric Sensing Technology

Gao, Kai; Lyu, Lijun; Huang, Hua; Fu, Chenzhao; Chen, Fuchun; Jin, Lijun

doi:10.1109/iecon.2019.8927370

Cited by 6 publications

(6 citation statements)

References 4 publications

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“…At the same time, with the rapid development of system-onchip (SOC) and very large-scale integrated circuit (VLSI), wireless sensor chips are developing towards miniaturization and low power consumption, wireless transmission, and multiple intelligence. With the rapid development of wireless sensor networks in recent years, the demand for highefficiency power converters has become more and more urgent, and the market has become larger and larger [12].…”

Section: Related Workmentioning

confidence: 99%

Simulation of Steady‐State Temperature Rise of Electric Heating Field of Wireless Sensor Circuit Fault Current Trigger

Zhao

Zhuang

et al. 2021

Journal of Sensors

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This article analyzes the structure of the wireless sensor circuit, considering the balance of power consumption, integration, area, noise, etc., and adopts a radio frequency wireless sensor circuit with a low-IF structure. Through the analysis and comparison of traditional analog current trigger and digital current trigger structure, the feed-forward current trigger structure is selected, which is composed of received signal strength indicator (RSSI) and variable gain amplifier (VGA), which achieves low power consumption, fast stabilization time, and wide dynamic range design. The received signal strength indicator adopts the form of approximate logarithmic amplifier, five-stage double feedback loop structure, and realizes lower power consumption. In order to prevent the load current trigger from entering the speed saturation zone, a gain unit structure in which the superimposed current trigger is connected to the NMOS tube as the load is proposed. The test results show that the circuit has a good power consumption performance (1 mW) and at the same time 56.8 dB/m sensitivity. In this paper, through the analysis of the current trigger system and the analysis and comparison of the existing variable gain amplifiers, the variable gain amplifier structure composed of the folded wireless sensing unit and the index control unit is adopted. In order to reduce the power consumption of the circuit and increase the output swing, a structure in which the two-stage folding wireless sensor unit shares the controlled voltage-to-current part of the circuit is proposed. Aiming at the design requirements of the system, this article discussed in detail the architecture of the entire temperature measurement node and the design parameters of the chip and completed the overall architecture design of the chip. The simulation results of the steady-state temperature rise of the electric heating field show that the circuit has achieved an input dynamic adjustment range of more than 60 dB, the maximum power consumption is 1 mW, and the linearity error is less than 0.5 dB. The designed automatic gain control circuit is implemented in SMIC 0.18 cape CMOS process. The simulation results of the steady-state temperature rise of the electric heating field show that the circuit has a 56 dB input dynamic adjustment range within a linear error of 1.25 dB, and the time constant is 7.55 ms, and power consumption is 2.84 mW. Through the steady-state temperature rise simulation and test results of the electric heating field, the correctness of the design is verified and it meets the system requirements.

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Section: Related Workmentioning

confidence: 99%

Simulation of Steady‐State Temperature Rise of Electric Heating Field of Wireless Sensor Circuit Fault Current Trigger

Zhao

Zhuang

et al. 2021

Journal of Sensors

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“…A non-destructive ultrasonic phased array technology was utilized in [16] to detect defective silicone rubber composite insulator samples. In [17], an electrical equipment insulation defect detection system based on infrared and ultraviolet optoelectronics-sensing technology was developed. The insulation condition of the electrical equipment was evaluated comprehensively using the signals of infrared and ultraviolet optoelectronics sensors.…”

Section: Introductionmentioning

confidence: 99%

Composite Insulator Defect Identification Method Based on Acoustic–Electric Feature Fusion and MMSAE Network

et al. 2023

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Aiming to solve the partial discharge problem caused by defects in composite insulators, most existing live detection methods are limited by the subjectivity of human judgment, the difficulty of effective quantification, and the use of a single detection method. Therefore, a composite insulator defect diagnosis model based on acoustic–electric feature fusion and a multi-scale perception multi-input of stacked auto-encoder (MMSAE) network is proposed in this paper. Initially, during the withstanding voltage experiment, the electromagnetic wave spectrometer and ultrasonic detector were used to collect and process the data of six types of composite insulator samples with artificial defects. The electromagnetic wave spectrum, ultrasonic power spectral density, and n-S map were then obtained. Then, the network architecture of MMSAE was built by integrating a stacked auto-encoder and multi-scale perception module; the feature extraction and fusion methods of the electromagnetic wave spectrum and ultrasonic signal were investigated. The proposed method was used to diagnose test samples, and the diagnostic results were compared to those obtained using a single input source and the artificial neural network (ANN) method. The results demonstrate that the detection accuracy of acoustic–electric feature fusion is greater than that of a single feature; the accuracy of the proposed method is 99.17%, which is significantly higher than the accuracy of the conventional ANN method. Finally, composite insulator defect diagnosis software based on PYQT5 and Keras was developed. Ten 500 kV aging composite insulators were used to validate the effectiveness of the proposed method and design software.

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“…These methods have great requirements for weather conditions and are timeconsuming. In recent years, researchers have introduced the ultrasonic wave method (Yao, Yu et al, 2019) and the ultraviolet pulse method (Gao, Lyu et al, 2019). Yao, Yu et al (2019) proposed an ultrasonic wave method for detection by receiving and processing ultrasonic signals between 20-40 kHz and 80-140 kHz because ultrasound at these frequencies is emitted from the insulator failure location.…”

Section: Introductionmentioning

confidence: 99%

“…Yao, Yu et al (2019) proposed an ultrasonic wave method for detection by receiving and processing ultrasonic signals between 20-40 kHz and 80-140 kHz because ultrasound at these frequencies is emitted from the insulator failure location. Gao, Lyu et al (2019) introduced the phase relationship between pulse waveform of an ultraviolet photoelectric sensor and pulse current waveform of partial discharge, the relationship between ultraviolet pulse frequency and apparent discharge quantity, and the attenuation characteristics of ultraviolet pulse frequency with the distance from the sensor to detection target, thus, the partial discharge of electrical equipment can be accurately detected. These two approaches are more accurate but expensive.…”

Section: Introductionmentioning

confidence: 99%

High Accuracy Real-Time Insulator String Defect Detection Method Based on Improved YOLOv5

et al. 2022

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Insulator string is a special insulation component which plays an important role in overhead transmission lines. However, working outdoors for a long time, insulators often have defects because of various environmental and weather conditions, which affect the normal operation of transmission lines and even cause huge economic losses. Therefore, insulator defect recognition is a crucial issue. Traditional insulator defect identification relies on manual work, which is time-consuming and inefficient. Therefore, the use of artificial intelligence to detect the defect location and recognize its class has become a key research field. By improving the classical YOLOv5 (you only look once) model, this article proposes a new method to enable high accuracy and real-time detection. Our method has three advantages: 1) Efficient-IoU (EIoU) replaces intersection over union (IoU) to calculate the loss of box regression, which overcomes that the detection is sensitive to various scale insulators in aerial images. 2) Since YOLOv5 itself detects some natural scenes in the real world, some anchors setting by default are not suitable for defect detection, this article introduces Assumption-free K-MC2 (AFK-MC2) algorithm into YOLOv5 to modify the K-means algorithm to improve accuracy and speed. 3) The cluster non-maximum suppression (Cluster-NMS) algorithm is introduced to avoid missing detection of insulators because of mutual occlusion in images and improve the computation speed at the same time. The experiments’ results show that this model can improve detection accuracy compared with YOLOv5 and realize real-time detection.

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Insulation Defect Detection of Electrical Equipment Based on Infrared and Ultraviolet Photoelectric Sensing Technology

Cited by 6 publications

References 4 publications

Simulation of Steady‐State Temperature Rise of Electric Heating Field of Wireless Sensor Circuit Fault Current Trigger

Simulation of Steady‐State Temperature Rise of Electric Heating Field of Wireless Sensor Circuit Fault Current Trigger

Composite Insulator Defect Identification Method Based on Acoustic–Electric Feature Fusion and MMSAE Network

High Accuracy Real-Time Insulator String Defect Detection Method Based on Improved YOLOv5

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