Condition diagnosis of transformer oil-paper insulation using dielectric response fingerprint characteristics

Gao, Jun; Yang, Lijun; Wang, Youyuan; Liu, Xiao; Lv, Yandong; Zheng, Hanbo

doi:10.1109/tdei.2015.005608

Cited by 47 publications

(33 citation statements)

References 31 publications

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“…Comparing dielectric responses with those recorded from standardized samples, also known as "fingerprints", to present qualitative knowledge of the insulation state [13][14][15]; • Modeling the OIP system to match the responses to thereby understand the dependence of model parameters on insulation conditions [11,[16][17][18][19][20]; • Deriving numerical characteristics from the traces and fitting with intuitional indicators, e.g., moisture percentage or the degree of polymerization (DP) to develop analytical formulas between them [10,[21][22][23].…”

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confidence: 99%

A Parameterization Approach for the Dielectric Response Model of Oil Paper Insulation Using FDS Measurements

Feng

Yang

et al. 2018

Energies

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Abstract:To facilitate better interpretation of dielectric response measurements-thereby directing numerical evidence for condition assessments of oil-paper-insulated equipment in high-voltage alternating current (HVAC) transmission systems-a novel approach is presented to estimate the parameters in the extended Debye model (EDM) using wideband frequency domain spectroscopy (FDS). A syncretic algorithm that integrates a genetic algorithm (GA) and the Levenberg-Marquardt (L-M) algorithm is introduced in the present study to parameterize EDM using the FDS measurements of a real-life 126 kV oil-impregnated paper (OIP) bushing under different controlled temperatures. As for the uncertainty of the EDM structure due to variable branch quantity, Akaike's information criterion (AIC) is employed to determine the model orders. For verification, comparative analysis of FDS reconstruction and results of FDS transformation to polarization-depolarization current (PDC)/return voltage measurement (RVM) are presented. The comparison demonstrates good agreement between the measured and reconstructed spectroscopies of complex capacitance and tan δ over the full tested frequency band (10 −4 Hz-10 3 Hz) with goodness of fit over 0.99. Deviations between the tested and modelled PDC/RVM from FDS are then discussed. Compared with the previous studies to parameterize the model using time domain dielectric responses, the proposed method solves the problematic matching between EDM and FDS especially in a wide frequency band, and therefore assures a basis for quantitative insulation condition assessment of OIP-insulated apparatus in energy systems.

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A Parameterization Approach for the Dielectric Response Model of Oil Paper Insulation Using FDS Measurements

Feng

Yang

et al. 2018

Energies

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“…The possibility of the suggested approach has been certified by oil-paper insulation samples at various aging states and with distinct moisture levels. In 2016, Gao et al [5] have interpreted the oil-paper insulation state of the power transformer with the help of dielectric response approach. PDC, and FDS, were the measurements of dielectric response, accomplished on pressboard samples at peculiar temperatures.…”

Section: A Related Workmentioning

confidence: 99%

“…Nevertheless, there are some objections like the extent of aging of oil-paper insulation couldn't be calculated. Dielectric Response Method [5] is a thin band measurement method, which makes it easy to filter noise especially for higher frequencies, and Grey Correlation Model [5] can handle both incomplete data and uncertain problems accurately. But, it is highly sensitive to the water and aging state of insulation.…”

Section: B Reviewmentioning

confidence: 99%

Aging Assessment of Power Transformer Insulation Oil using Hybrid Meta-Heuristic Trained Artificial Neural Network

Singh*,

Singh

2019

IJEAT

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In today's economic state, power transformer remains as the most expensive equipment in electrical system, in which insulation oil has been taken a significant role for performing a prominent operation. Since the insulation oil happens to degrade soon due to aging, high temperature and chemical reactions such as the oxidation, the periodic checking of oil followed by its replacement is necessary to stop the unexpected failure of the transformer. Moreover, it will be very advantageous if it happens to implement an automated model for predicting the age of transformer oil from time to time. The main intent of this paper is to develop an age assessment framework of transformer insulation oil using intelligent approaches. Here, diverse parameters associated with the transformer such as Breakdown Voltage (BDV), moisture, resistivity, tan delta, interfacial tension, and flash point is given as input for predicting the age of the insulation oil. These data have been already collected using 20 working power transformers operated at various substations in Punjab, India. In the proposed model, the collected parameters are subjected to a well-performing machine learning algorithm termed as Artificial Neural Network (ANN) in order to predict the age of the insulation oil. As a main contribution, the existing training algorithm in ANN so called as Levenberg–Marquardt (LM) is replaced by a hybrid metaheuritics algorithm. The newly developed hybrid algorithm merges the idea of Crow Search Algorithm (CSA), and Particle Swarm Optimization (PSO), and the new algorithm is termed as Particle Swarm-based Crow Search Algorithm (PS-CSA). The new training algorithm optimizes the weight of ANN using the hybrid CS-PSO updating procedure, in such a way that the difference between the predicted and actual outcome is minimum. Hence, this age prediction of transformer insulation oil will be beneficial for the environs to avoid the drastic condition.

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“…On the contrary, the dielectric response technique, which serves as one of the most predominant techniques in the insulation condition diagnosis field, is also classified into the time-domain response and the frequency-domain response. Compared to the frequency-domain response (FDR), the time-domain response (TDR) has been suggested to have several defects including poor anti-interference and high time-consumption; the FDR is, therefore, believed to be more interesting for researchers [8,[14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Moisture and Aging Conditions of Oil-Immersed Cellulose Insulation Based on Fingerprints Database of Dielectric Modulus

Zhang

Fan

et al. 2020

Polymers

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Frequency-domain spectroscopy (FDS) is demonstrated to be affected by electrode polarization and conductance behavior in the low-frequency ranges, which causes the unreliable prediction results of transformer cellulose insulation. In order to solve this issue, a fingerprint database based on the dielectric modulus is reported to predict the degree of polymerization (DP) and moisture content of cellulose insulation. In the current work, the relevant fingerprints that characterize the insulation conditions are obtained by studying the dielectric modulus curves of cellulose insulation with various insulation conditions, as well as the DC conductivity of transformer oil. Then, the dielectric modulus fingerprint database is established in the lab, and the accuracy of the reported fingerprint database is later verified. As a potential tool, the dielectric modulus fingerprint database is tested by several samples, and the results demonstrate that the accuracy of this method is more than 80%. In that respect, an interesting discovery of this paper is that the dielectric modulus fingerprint database may be a helpful tool for conditions prediction of the transformer cellulose insulation system.

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Condition diagnosis of transformer oil-paper insulation using dielectric response fingerprint characteristics

Cited by 47 publications

References 31 publications

A Parameterization Approach for the Dielectric Response Model of Oil Paper Insulation Using FDS Measurements

A Parameterization Approach for the Dielectric Response Model of Oil Paper Insulation Using FDS Measurements

Aging Assessment of Power Transformer Insulation Oil using Hybrid Meta-Heuristic Trained Artificial Neural Network

Prediction of Moisture and Aging Conditions of Oil-Immersed Cellulose Insulation Based on Fingerprints Database of Dielectric Modulus

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