Assessment of Oil-Paper Insulation Aging Using Frequency Domain Spectroscopy and Moisture Equilibrium Curves

Yang, Lijun; Zou, Tiantian; Deng, Bangming; Zhang, Haibing; Yang, Mujie; Peng, Pan

doi:10.1109/access.2019.2906379

Cited by 57 publications

(34 citation statements)

References 23 publications

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“…In 2019, Yang et al [4] have suggested a new technique exploiting FDS and the water balance qualities of oil-paper insulation, to calculate the aging state of oil-paper insulation. To estimate the moisture content of the insulating paper, FDS curves were utilized, and oil samples were used to measure the moisture substance of the oil.…”

Section: A Related Workmentioning

confidence: 99%

“…Yet, it is having a challenge i.e., the areas are concentrated in dielectric, and in wave frequencies, the substrate loss might become inadequately high. FDS [4] calculates the contents of unknown data accurately using mathematical analysis algorithm, and provides consistent data. Nevertheless, there are some objections like the extent of aging of oil-paper insulation couldn't be calculated.…”

Section: B Reviewmentioning

confidence: 99%

“…2, and Eq. 3 (4) The error difference between the predicted op Ô and the actual op O output is given in Eq. 4, which should be minimized by optimizing the weight w A using proposed PS-CSA.…”

Section: B Artificial Neural Network-based Aging Assessmentmentioning

confidence: 99%

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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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Section: A Related Workmentioning

confidence: 99%

Section: B Reviewmentioning

confidence: 99%

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

Singh*,

Singh

2019

IJEAT

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“…Where α i and β i are called the Lagrange operator. If Equation (6) satisfies the conditions obtained by Lagrange vertical multiplication shown in Equation 7, Equation (5) can be converted to the dual form shown in Equation (8).…”

Section: The Introduction Of Svmmentioning

confidence: 99%

“…Power transformers perform a vital task of transforming electrical energy in power systems, and the condition evaluation of its internal oil/paper system has attracted wide attention [ 1 , 2 , 3 , 4 , 5 , 6 ]. In recent years, a prevailing method of moisture determination is the so-called frequency domain spectroscopy (FDS) [ 7 , 8 , 9 , 10 ] technique, which is also called dielectric frequency response (DFR) technique. The condition assessment of transformer oil-immersed polymer insulation based on FDS could be realized by the following steps: Firstly, extracting feature parameters that could reflect the insulation conditions of oil-immersed insulation from the measured FDS data.…”

Section: Introductionmentioning

confidence: 99%

Moisture Prediction of Transformer Oil-Immersed Polymer Insulation by Applying a Support Vector Machine Combined with a Genetic Algorithm

Zhang

Fan

et al. 2020

Polymers

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The support vector machine (SVM) combined with the genetic algorithm (GA) has been utilized for the fault diagnosis of transformers since its high accuracy. In addition to the fault diagnosis, the condition assessment of transformer oil-immersed insulation conveys the crucial engineering significance as well. However, the approaches for getting GA-SVM used to the moisture prediction of oil-immersed insulation have been rarely reported. In view of this issue, this paper pioneers the application of GA-SVM and frequency domain spectroscopy (FDS) to realize the moisture prediction of transformer oil-immersed insulation. In the present work, a method of constructing a GA-SVM multi-classifier for moisture diagnosis based on the fitting analysis model is firstly reported. Then, the feasibility and reliability of the reported method are proved by employing the laboratory and field test experiments. The experimental results indicate that the reported prediction model might be serviced as a potential tool for the moisture prediction of transformer oil-immersed polymer insulation.

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Study on XY Model and Its Equivalent Circuit of Oil‐Paper Insulation

Tu¹,

Wang

et al. 2022

IEEJ Transactions Elec Engng

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The XY model of oil‐paper insulation connects the dielectric properties of oil, paper and oil‐paper system. Its equivalent circuit is the basis of the application of dielectric response technology in the state diagnosis of transformer oil‐paper insulation. To better apply the XY model to the study of the dielectric properties of oil‐paper insulation, the dielectric properties of the coaxial oil‐paper insulation system built in the laboratory, XY model and its equivalent circuits are compared. Then an improved circuit of XY model which can reflect the properties of the oil‐paper insulation more truly is proposed. The results show that there is no difference between the XY model and the coaxial model, and both can well characterize the oil‐paper insulation system. The two equivalent circuits of XY model, “Series to Parallel” circuit and “Parallel to Series” circuit, ignore the current tangential component and the electric field distortion in oil‐paper insulation, respectively, resulting in deviations from the XY model. By introducing a new branch C*XY, the improved circuit can reflect the tangential current and electric field distortion in oil‐paper insulation, which can be completely equivalent to XY model. In addition, there is a characteristic frequency point in the oil‐paper insulation system, where the dielectric loss factor tan δ of oil, paper and oil‐paper system are the same. There is no difference between the XY model and its two equivalent circuits at this point. The characteristic frequency is determined by the dielectric properties of oil and paper, and not affected by the insulation structure. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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Assessment of Oil-Paper Insulation Aging Using Frequency Domain Spectroscopy and Moisture Equilibrium Curves

Cited by 57 publications

References 23 publications

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

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

Moisture Prediction of Transformer Oil-Immersed Polymer Insulation by Applying a Support Vector Machine Combined with a Genetic Algorithm

Study on XY Model and Its Equivalent Circuit of Oil‐Paper Insulation

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