Detection Method of Partial Discharge on Transformer and Gas-Insulated Switchgear: A Review

Faizol, Zulbirri; Zubir, Farid; Saman, Norhafezaidi Mat; Ahmad, Mohd Hafizi; Rahim, Mohamad Kamal A.; Ayop, Osman; Jusoh, Muzammil; Majid, Huda A.; Yusoff, Zubaida

doi:10.3390/app13179605

Cited by 11 publications

(3 citation statements)

References 138 publications

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“…Plasmas will be generated when PDs occur [12], which is accompanied by light, and optical-based sensors have been shown to be very sensitive in discharge plasma measurements [13,14]. Among many PD sensors, fluorescent optical fiber is highly praised for its high resistance to electromagnetic interference, high sensitivity and ease to be built-in [15][16][17]. Since Schwarz proposed that fluorescent fiber sensors have higher sensitivity than conventional quartz fibers [18], scholars have developed many advanced fiber optic-based PD detection systems, and subsequently proposed fault diagnosis algorithms [19,20].…”

Section: Introductionmentioning

confidence: 99%

Optical signals measurement of discharge plasma in air switch cabinet using a fluorescent optical fiber as the sensor

Cheng,

Hao,

Zhu

et al. 2024

Phys. Scr.

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Air switch cabinets are widely employed in the power industry, there is, however, no reliable methods for detecting partial discharges (PDs). Because fiber optic-based sensors are easy to install and have anti-electromagnetic interference capabilities, they are well suited for PDs detection in switch cabinets with their many components and metal shell structure. In this work, corona discharge in an air switch cabinet was detected using fluorescent fiber, and the characteristics of PDs were analyzed. Furthermore, experimental investigations were conducted to explore the impact of gas pressure and fiber length on the PDs detection. The results exhibit a correlation between the aforementioned factors and the PDs’ total amplitude, maximum amplitude, number of pulses and detection limit of PD quantity. The results can provide references for the application of fluorescent optical fiber on the PD detection in air switch cabinet.

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Section: Introductionmentioning

confidence: 99%

Optical signals measurement of discharge plasma in air switch cabinet using a fluorescent optical fiber as the sensor

Cheng,

Hao,

Zhu

et al. 2024

Phys. Scr.

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“…Among the most prevalent characteristic decomposition components of SF 6 gas, the concentration of SO 2 serves as a pivotal indicator for assessing GIS insulation conditions. The detection of these characteristic SF 6 decomposition components in GIS is typically achieved through a variety of methods, including gas chromatography [4,5], infrared absorption spectrometry [6][7][8], and photoacoustic spectrometry [9].Gas chromatography offers high accuracy in gas detection but is plagued by extended detection periods. Photoacoustic spectrometry boasts high detection sensitivity, albeit susceptible to interference from environmental noise.…”

Section: Introductionmentioning

confidence: 99%

Research on an Ultraviolet Spectral Denoising Algorithm Based on the Improved SVD Method

Qin,

Wang,

Wang

2023

Applied Sciences

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The utilization of ultraviolet (UV) absorption spectroscopy for monitoring the concentration of specific decomposition gas components in gas-insulated switchgear (GIS) can provide a means to assess its insulation status. Nevertheless, UV optical modules currently deployed in the field are susceptible to external interferences like ambient noise and equipment vibrations. Real-time spectral data acquisition often suffers from significant noise contamination, directly impinging on subsequent feature extraction and detection accuracy. This paper presents an optimized singular value decomposition (SVD) noise reduction method for mitigating noisy spectral signals. First, each singular value within the noisy signal is transformed into a component signal. Next, the highest frequency value in the signal serves as an indicator to characterize the signal. Finally, the primary frequency values are arranged based on the decreasing singular values of the original noisy signal. The singular value corresponding to the first primary frequency value surpassing a preset threshold is selected as the effective order for denoising. Random noise with varying intensities was intentionally introduced to the UV spectral signal of sulfur dioxide (SO2), followed by noise reduction procedures. It is shown that the improved SVD noise reduction algorithm proposed in this paper enhances the signal-to-noise ratio (SNR) by 18.02% and 16.86%, and reduces the root-mean-square error (RMSE) by 15.13% and 14.92%, respectively, compared with the singular value difference spectrum (SVDS) denoising method and wavelet transform denoising method under the condition of low SNR. Furthermore, there exists a linear relationship between the concentration of SO2 samples and the eigenvalues of the UV spectra, demonstrating a higher linear goodness with a coefficient of 0.99735. The denoising method proposed in this paper does not require the manual setting of various types of parameters, and has a better ability to deal with the noise of UV spectral signals in engineering sites with complex environments.

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“…Currently, cable PD-detection methods can be mainly categorized into electrical and non-electrical detection methods [14]. Electrical detection methods primarily encompass the ultrasonic method [15], the pulsed current method [16], and the UHF method [17,18].…”

Section: Introductionmentioning

confidence: 99%

Partial Discharge Pattern-Recognition Method Based on Embedded Artificial Intelligence

Yan,

Bai,

Zhang

et al. 2023

Applied Sciences

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This paper proposes a method for detecting and recognizing partial discharges in high-voltage (HV) equipment. The aim is to address issues commonly found in traditional systems, including complex operations, high computational demands, significant power consumption, and elevated costs. Various types of discharges were investigated in an HV laboratory environment. Discharge data were collected using a high-frequency current sensor and a microcontroller. Subsequently, this data underwent processing and transformation into feature sets using the phase-resolved partial discharge analysis technique. These features were then converted into grayscale map samples in PNG format. To achieve partial discharge classification, a convolutional neural network (CNN) was trained on these samples. After successful training, the network model was adapted for deployment on a microcontroller, facilitated by the STM32Cube.AI ecosystem, enabling real-time partial discharge recognition. The study also examined storage requirements across different CNN layers and their impact on recognition efficacy. To assess the algorithm’s robustness, recognition accuracy was tested under varying discharge voltages, insulation media thicknesses, and noise levels. The test results demonstrated that the algorithm could be effectively implemented on a microcontroller, achieving a recognition accuracy exceeding 98%.

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Detection Method of Partial Discharge on Transformer and Gas-Insulated Switchgear: A Review

Cited by 11 publications

References 138 publications

Optical signals measurement of discharge plasma in air switch cabinet using a fluorescent optical fiber as the sensor

Optical signals measurement of discharge plasma in air switch cabinet using a fluorescent optical fiber as the sensor

Research on an Ultraviolet Spectral Denoising Algorithm Based on the Improved SVD Method

Partial Discharge Pattern-Recognition Method Based on Embedded Artificial Intelligence

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