Existing methods for transformer fault diagnosis either train a classifier to fit the relationship between dissolved gas and fault type or find some similar cases with unknown samples by calculating the similarity metrics. Their accuracy is limited, since they are hard to learn from other algorithms to improve their own performance. To improve the accuracy of transformer fault diagnosis, a novel method for transformer fault diagnosis based on graph convolutional network (GCN) is proposed. The proposed method has the advantages of two kinds of existing methods. Specifically, the adjacency matrix of GCN is utilized to fully represent the similarity metrics between unknown samples and labeled samples. Furthermore, the graph convolutional layers with strong feature extraction ability are used as a classifier to find the complex nonlinear relationship between dissolved gas and fault type. The back propagation algorithm is used to complete the training process of GCN. The simulation results show that the performance of GCN is better than that of the existing methods such as convolutional neural network, multi-layer perceptron, support vector machine, extreme gradient boosting tree, k-nearest neighbors and Siamese network in different input features and data volumes, which can effectively meet the needs of diagnostic accuracy.

Fault diagnosis of power transformers using graph convolutional network

Dissolved gases analysis (DGA) provides widely recognized practice for oil-immersed power transformers, and it is mainly interpreted for fault diagnosis. In order to accurately estimate the health index state of power transformers and predict the incipient operation failure, a dynamic fault prediction technique based on hidden Markov model (HMM) of DGA is proposed in this paper. Gaussian mixture model, as a soft clustering method, is used to extract the static features of different health states from a DGA dataset of 65 in-service power transformers with 1600 days operation. Especially, a sub-health state is introduced to enrich the health index and aging stages of power transformers. The static features between health states and concentrations of dissolved gases are built, and the effectiveness of clustering is cross validated. Furthermore, taking time sequence into consideration, transition probability of power transformer between different health states based on the HMM model is calculated and analyzed. The effectiveness of dynamic early warning and incipient fault prediction in sub-health status of in-service power transformers has been proved. Moreover, the dynamic fault prediction is able to provide decision-making basis for practical condition-based operation and maintenances.

Dynamic Fault Prediction of Power Transformers Based on Hidden Markov Model of Dissolved Gases Analysis

Accumulation of interfacial space charge in oil-paper interface is a critical issue in insulation diagnostics of transformers. This interfacial charge mainly accumulates due to the conductivity difference of oil and paper. Accumulation of interfacial charge leads to localized field enhancement, which further leads to partial discharges and acceleration in the aging of insulation. Therefore, from the point of view of transformer insulation diagnostics, assessment of interfacial charge is very important. However, it is not easy to estimate interfacial space charge behavior from the transformer diagnostics methods currently in use. In case of Polarization-Depolarization Current (PDC) measurement, a well known method for transformer condition monitoring, the effect of interfacial charge is reflected in the non-linearity of current response during polarization and de-polarization. During de-polarization process, a part of the interfacial charge accumulated during polarization period is absorbed by the electrodes producing a current, which is difficult to separate using conventional linear dielectric theory. In this paper, an attempt has been made to separate this current component from de-polarization current through considering charge de-trapping mechanism. Terming this current component as de-trapping current, its relationship with other parameters of transformer insulation is discussed. The developed methodology has been applied on several practical transformers. It was observed that the time constant of de-trapping current is related to the paper conductivity, oil conductivity, dissipation factor and age of the insulation.

Assessment of interfacial charge accumulation in oil-paper interface in transformer insulation from polarization-depolarization current measurements

The life expectancy of a transformer is closely related to the condition of its oil-paper insulation. In the present study, oil impregnated pressboard samples with different aging states and water contents were prepared under controlled laboratory conditions to analyze the oil-paper insulation condition of the power transformer by the dielectric response method. Dielectric response measurements, i.e., polarization and depolarization currents (PDC) and frequency domain spectroscopy (FDS), were conducted on the pressboard samples at different temperatures. Results show that the dielectric responses of oil impregnated pressboards are highly sensitive to aging, water, and temperature, but the influence of the temperature on FDS can be effectively eliminated by using the master curve technique. Meanwhile, dielectric characteristic parameters, which are sensitive to aging state and water content, were extracted to form a fingerprint database to indicate the various states of the oil impregnated pressboard. A grey correlation diagnosis model was first introduced to assess the aging state and water content of oil-paper insulation. Finally, FDS measurements were performed on a field transformer to verify the effectiveness of the new evaluation technique proposed in this paper for the condition assessment of the transformer oil-paper insulation.

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

Conventional dielectric response measurement techniques, for instance, recovery voltage measurement (RVM), frequency domain spectroscopy (FDS) and polarization–depolarization current (PDC) are effective nondestructive insulation monitoring techniques for oil-impregnated power transformers. Previous studies have focused mainly on some single type of dielectric measurement method. However, the condition of oil paper insulation in transformer is affected by many factors, so it is difficult to predict the insulation status by means of a single method. In this paper, the insulation condition assessment is performed by grey relational analysis (GRA) technique after carefully investigating different dielectric response measurement data. The insulation condition sensitive parameters of samples with unknown insulation status are extracted from different dielectric response measurement data and then these are used to contrast with the standard insulation state vector models established in controlled laboratory conditions by using GRA technique for predicting insulation condition. The performance of the proposed approach is tested using both the laboratory samples and a power transformer to demonstrate that it can provide reliable and effective insulation diagnosis.

Grey Relational Analysis for Insulation Condition Assessment of Power Transformers Based Upon Conventional Dielectric Response Measurement

Search for a reliable and efficient insulation diagnostic tool has always been the interest of power utilities. Today a large number of methods are available that can be used for insulation condition monitoring. These methods include both traditional and newer techniques. However due to complex aging process of oil paper insulation under the influence of different types of stresses, insulation condition assessment is generally performed by experts after carefully evaluating different measurement data. Furthermore, measurement data are influenced by various factors (like conductive aging byproducts, furanic compounds, paper and oil-moisture) in addition to measurement error (if any). This makes prediction of insulation condition based on single type of measurement rather difficult. This paper presents an Expert System designed to perform insulation diagnosis. The Expert System considers measurement data obtained using both traditional and newer techniques in order to come to a definitive conclusion. The Expert System extracts insulation condition sensitive information from data obtained using different techniques and then uses these to devise an optimized insulation model. This optimized model is used to predict paper-moisture content and other insulation condition sensitive parameters. Since these values are predicted using optimized model, they are not dependent on a single type of measurement and hence are less likely to be affected by error of any specific measurement. The performance of the developed Expert System is first tested on a laboratory sample and then on several real life power transformers belonging to NTPC Ltd.

An expert system approach for transformer insulation diagnosis combining conventional diagnostic tests and PDC, RVM data

Moisture content in an oil-paper insulation system of power transformer accelerates the aging process of cellulose that degrades insulation properties. As a result, dielectric response measurement data is also affected by moisture present in insulation. In time domain these measurements include Return Voltage Measurement and Polarization-Depolarization Current measurement. In addition it is a known fact that power transformer insulation consists of complex structures which is difficult to model in laboratory. Therefore, results obtained from samples prepared under controlled environment may not be applicable to real life power transformer. The present paper discusses a new approach to combine the results obtained from these two methods namely, Return Voltage Measurement and Polarization-Depolarization Current measurement for several power transformers, to find the relation between oil and paper moisture content that is relevant to insitu power transformers.

A new approach for determination of moisture in paper insulation of in-situ power transformers by combining polarization-depolarization current and return voltage measurement results

Large number of power transformers that are operating today have already outlived their designed life. Such equipments, which are now prone to failures, are still in operation in order to maximize asset utilization. Extending the life of such equipments by policies based on proper condition assessment can mean substantial saving for the utilities. Modern techniques based on dielectric response measurement and analysis provides valuable information regarding the condition of the HV insulation. However, commercial equipments available for recording dielectric response are extremely costly. The paper reports the design of a low-cost instrument developed at High Tension Laboratory of Jadavpur University for measurement of return voltage of high voltage oil-paper insulation. In addition a method is also discussed that facilitate user to carry out on-site validation of RV measurements recorded using the above instrument.

A novel methodology for on-site validation of RV measurement data

The present paper shows that time domain data measured from different in-situ generator transformers can be successfully transformed to frequency domain. It is also shown that the transformed frequency domain data can be used to determine insulation condition. For the present work, Polarization-Depolarization Current is measured using a low-cost instrument developed at High Tension Laboratory of Jadavpur University. The transformation of data from time to frequency domain is tested using data recorded from several generator transformers belonging to NTPC Ltd. The paper reports the results obtained from some of these transformers. The value of paper moisture predicted using the measured time domain data and the transformed frequency domain data are also observed to be in good agreement with each other. Presence of a small relaxation peak in the plot of e″ r (ω) at low frequencies, below the frequency of minimum e″ r (ω), is an indication of high paper moisture content in oil-paper insulation. Such peaks are clearly visible in the transformed frequency domain data of the transformers which have high moisture content.

Condition assessment of in-situ generator transformers by frequency domain analysis using time domain data

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

S. Sarkar

Author Tools

Chat about Author

Papers

An expert system approach for transformer insulation diagnosis combining conventional diagnostic tests and PDC, RVM data

A new approach for determination of moisture in paper insulation of in-situ power transformers by combining polarization-depolarization current and return voltage measurement results

A novel methodology for on-site validation of RV measurement data

Condition assessment of in-situ generator transformers by frequency domain analysis using time domain data