A Review of Frequency Response Analysis Methods for Power Transformer Diagnostics

Alsuhaibani, Saleh H.; Khan, Yasin; Béroual, Abderrahmane; Malik, N. H.

doi:10.3390/en9110879

Cited by 74 publications

(53 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…After that, the data of real part and imaginary part of the complex relative permittivity of transformer insulation are determined according to geometrical capacitance and the complex capacitance. According to the measurement results of on-site oil sample at test temperature T, the complex relative permittivity of insulation oil can be obtained by the direct current conductivity in terms of Equation (8). Then, the correlation of complex relative permittivity spectroscopy among the whole transformer insulation, insulation oil and insulation paper can be derived by Equation (7).…”

Section: A Power Transformer In Servicementioning

confidence: 99%

“…Unfortunately, it is a fact that the electrical and mechanical properties of the transformer oil-paper insulation system become aging gradually due to the combination stresses of the mechanical vibration, thermal, electrical, oxygen, water and other factors, in the long-term service process [4][5][6][7][8]. Historically, the applications of oil sample analysis (OSA) in terms of equilibrium relationships between insulating oil and cellulose insulation, dissolved gas analysis (DGA), dielectric loss factor (DLF) and insulation resistance (IR) have been commonly used for performing non-destructive condition monitoring of transformer insulation [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2017

View full text Add to dashboard Cite

Abstract: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.

show abstract

Section: A Power Transformer In Servicementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Energies 2017, 10,2022winding, there is a difference between RD windings and the healthy windings. However, the effect of RD is much more significant than that of the difference between the windings.…”

Section: Methodsmentioning

confidence: 99%

“…For the indicator of mean square error, both the training set and testing set are constructed according to Expression (10). In this expression, s, n, r, m, c and q have the same meaning as those in Expression (9).…”

Section: Construction Of Training Set and Testing Setmentioning

confidence: 99%

Identification of Power Transformer Winding Mechanical Fault Types Based on Online IFRA by Support Vector Machine

et al. 2017

View full text Add to dashboard Cite

Abstract:A transformer is the most valuable and expensive property for power utility, thus ensuring its reliable operation is a major task for both operators and researchers. Online impulse frequency response analysis has proven to be a promising technique for detecting transformer internal winding mechanical deformation faults when a power transformer is in service. However, as so far, there is still no reliable standard code for frequency response signature interpretation and quantification. This paper tries to utilize a machine learning method, namely the support vector machine, to identify and classify the winding mechanical fault types, based on online impulse frequency response analysis. Actual transformer fault data from a specially manufactured model transformer are collected and analyzed. Two feature vectors are proposed and the diagnostic results are predicted. The diagnostic results indicate the satisfied classifying accuracy by the proposed method.

show abstract

“…Commonly used winding-fault diagnosis methods require outage of the transformer before testing [9]. There are several common methods including low-voltage impulse testing (LVI) [10], short-circuit impedance measurement method (SCI) [11], frequency response analysis method (FRA) [12,13], partial discharge method (PD) [14] and dissolved gas-in-oil analysis method (DGA) [15]. Using DGA, the relative concentration of various gases in the transformer oil is analyzed to detect transformer winding fault.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Impulse Wave and Sweep Frequency Response Analysis Methods for Diagnosis of Transformer Winding Faults

Yang

Yong

2017

Energies

View full text Add to dashboard Cite

Abstract:Monitoring of winding faults is the most important item used to determine the maintenance status of a transformer. Commonly used methods for winding-fault diagnosis require the transformer to exit operation before testing and an external exciting signal, whether the transformer is malfunctioning or not. However, if an overvoltage signal can be regarded as a broadband excitation source for fault diagnosis, then the interference caused by signal injection can be eliminated without the need for additional pulse or impulse signals. In this paper, a tapped transformer is designed and test platforms are built to compare winding diagnoses using the impulse wave and sweep frequency response analysis methods by recording voltage responses on both the high-and low-voltage sides and calculating the respective transfer functions. Based on comparison of statistical indicators, it is found that the sensitivities of both methods are similar for detecting conditions of winding-ground and winding-interlayer short circuits. It is concluded that it is feasible to use a transient overvoltage monitoring system for winding-fault diagnosis.

show abstract

A Review of Frequency Response Analysis Methods for Power Transformer Diagnostics

Cited by 74 publications

References 18 publications

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

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

Identification of Power Transformer Winding Mechanical Fault Types Based on Online IFRA by Support Vector Machine

Comparison of Impulse Wave and Sweep Frequency Response Analysis Methods for Diagnosis of Transformer Winding Faults

Contact Info

Product

Resources

About