Estimation of Contamination Level of Overhead Insulators Based on Surface Leakage Current Employing Detrended Fluctuation Analysis

Deb, Suhas; Das, Santanu; Pradhan, A. K.; Banik, Apu; Chatterjee, Biswendu; Dalai, Sovan

doi:10.1109/tie.2019.2934008

Cited by 25 publications

(13 citation statements)

References 22 publications

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“…Here, the surface of the samples has been contaminated artificially through the 'solid layer method' as prescribed in IEC 60507 [2,7,13]. This method is well accepted and has been reported in many published literature [7,14]. So this method has been adopted in this work for slurry deposition on the MOSA surface.…”

Section: Housing Type Polymericmentioning

confidence: 99%

“…In this equation,

σ_{20}

is the conductivity of the solution in (μS/m) at 20°C,

σ_{θ}

is the conductivity at θ° C, and t is the constant whose value depends on the temperature [14]. Based on the conductivity at 20°C (

σ_{20}

), the salinity of coated layer can be measured through the equation given as

S_{m} = {(5.7 σ_{20})}^{1.03}

Based on the salinity, ESDD value can be determined as

E S D D = S_{m} \times \frac{V_{m}}{A_{m}}

…”

Section: Experimental Testsmentioning

confidence: 99%

See 1 more Smart Citation

Deep learning–based surface contamination severity prediction of metal oxide surge arrester in power system

Das

Dalai

Chatterjee

2021

IET Science Measure & Tech

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This paper presents an advanced technique based on cross-Stockwell transform (XST) and sparse autoencoder to predict the surface contamination severity of metal oxide surge arrester (MOSA) employing leakage current signal. Generally, MOSAs in power system network are exposed to different environmental conditions where its condition may degrade due to accumulation of pollutants, which may cause premature failure of it. Hence, system reliability can get affected. Therefore, monitoring the surface condition of MOSA is very important. In this proposed technique, MOSA leakage current signals of different surface contamination severity have been cross-correlated with leakage current recorded at the clean surface in joint time-frequency plane through XST, which is an extended version of ST. Thereafter, sparse autoencoder, a deep learning framework, has been applied to extract potential deep feature from leakage current-converted XST matrices. The extracted deep features have been classified through different classifiers. It has been observed that the proposed technique yields satisfactory accuracy regarding the estimation of surface contamination severity of MOSA. Therefore, the proposed method can be implemented to monitor the surface condition of MOSA, and it may be applied for topologically similar problems.

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Section: Housing Type Polymericmentioning

confidence: 99%

“…In this equation,

σ_{20}

is the conductivity of the solution in (μS/m) at 20°C,

σ_{θ}

is the conductivity at θ° C, and t is the constant whose value depends on the temperature [14]. Based on the conductivity at 20°C (

σ_{20}

), the salinity of coated layer can be measured through the equation given as

S_{m} = {(5.7 σ_{20})}^{1.03}

Based on the salinity, ESDD value can be determined as

E S D D = S_{m} \times \frac{V_{m}}{A_{m}}

…”

Section: Experimental Testsmentioning

confidence: 99%

Deep learning–based surface contamination severity prediction of metal oxide surge arrester in power system

Das

Dalai

Chatterjee

2021

IET Science Measure & Tech

Self Cite

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“…Numerous authors have presented different techniques to obtain the pollution level on insulators from the analysis of some parameters of the leakage current [9] [10]. Therefore, the leakage current may be considered one of the most important pollution level indicators used in the literature [11].…”

Section: Introductionmentioning

confidence: 99%

A New Current Transducer for On-Line Monitoring of Leakage Current on HV Insulator Strings

et al. 2022

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This paper presents the design, implementation and testing of a transducer to measure and monitor in real time leakage currents on high-voltage insulators. The new proposed transducer has the particularity of not affecting the insulation capacity of the insulator strings and can be installed without the need to de-energize the power system. The transducer is made up of a toroidal current transformer with a high permeability magnetic core, and an amplifier circuit, the output voltage of which is acquired, processed and transmitted by a dedicated device installed in the lower part of the HV tower. The leakage current is reconstructed from the transducer output voltage by applying the FFT technique and its own inverse transfer function. The transducer makes it possible to obtain the leakage current with its characteristic harmonics and the technique presented here reduces the electromagnetic noise present in the power systems. The proposed transducer was tested on an insulator inside an artificial fog chamber, built according to the IEC 60507 standard. Finally, the transducer was tested on an insulator string in a 220 kV substation. The results obtained show that the designed transducer correctly measures the leakage current on HV insulator strings and can be used for the on-line monitoring of pollution levels of insulator strings.

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“…Due to long‐term exposure to environmental pollutants (especially in heavily polluted areas and coastal regions), the surface of overhead insulator gets gradually contaminated. Under moist condition, the soluble component (salt, alkali) in the contamination layer is partially dissolved, which leads to the formation of conductive layer [1]. The formation of conductive layers leads to an increase in leakage current flow along the surface of the insulator.…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural network and Bi‐directional long short memory hybrid deep network aided infrared image classification framework for non‐contact monitoring of overhead insulators

Das

Deb

Chatterjee

et al. 2022

IET Signal Processing

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In this paper, a novel vision-based automated framework for estimation of pollution severity of outdoor insulators is proposed. The correct estimation of pollution severity is important to prevent the premature flashover of insulators. Existing methods to determine the degree of contamination of insulator surface requires direct contact with the insulator, which is practically problematic. Considering the aforesaid fact, in this article, a novel infrared thermal (IRT) image-based automated framework is proposed for noncontact monitoring the surface condition of outdoor insulators. A large number of IRT images corresponding to different contamination levels were captured from several porcelain disc insulators using a thermal camera. The captured IRT images were initially segmented using mask region-based convolutional neural network to remove the effect of background. Then, a hybrid deep learning network consisting of convolutional neural network and Bi-directional long short memory (CNN-BiLSTM) is designed for automated classification of IRT images. It has been observed that the proposed network has achieved an accuracy of 98.86%, specificity of 99.72%, precision of 98.86% and F-1 score of 98.86%, respectively. A comparative study with other deep learning models indicated that the proposed CNN-BiLSTM network delivered better performance. Hence, the proposed framework can be used in real-life for non-contact condition monitoring of insulators.

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Estimation of Contamination Level of Overhead Insulators Based on Surface Leakage Current Employing Detrended Fluctuation Analysis

Cited by 25 publications

References 22 publications

Deep learning–based surface contamination severity prediction of metal oxide surge arrester in power system

Deep learning–based surface contamination severity prediction of metal oxide surge arrester in power system

A New Current Transducer for On-Line Monitoring of Leakage Current on HV Insulator Strings

Convolutional neural network and Bi‐directional long short memory hybrid deep network aided infrared image classification framework for non‐contact monitoring of overhead insulators

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