Monitoring of winding faults is of great significance for the assessment of the transformer maintenance status. In this study, the time series analysis is combined with frequency response analysis (FRA) for better interpretation and analysis of FRA results. Here, healthy baseline residual data are modeled using the time series analysis for faults detection of the transformer winding. Different statistical software, such as R 4.0.2 and ITSM2000, are used to analyze measured data. To detect the transformer’s winding faults, various analyses, such as the autocorrelation and partial autocorrelation functions plot, the Box–Pierce test, the Ljung–Box test, the McLeod–Li test, the turning points test, the Wallis and Moore phase–frequency test, and the Jarque–Bera test, and the order of minimum corrected Akaike’s information criteria are used. The simulation results show that the suggested method is able to classify and discriminate mechanical and electrical faults of the transformer’s winding with good accuracy.

Transformer Winding Faults Detection Based on Time Series Analysis

Fault detection and diagnosis in power transformers: a comprehensive review and classification of publications and methods

Moisture accumulates with the growing aging progress of oil-paper insulation and further shortens the remaining life of the transformer The frequency-domain spectroscopy (FDS) technique can be used to realize the moisture estimation However, the moisture estimation results would be unreliable once the aging effect on FDS was ignored Given this issue, an alternative model including the aging effect is thus proposed using FDS and intelligent algorithm In this work, the feature parameters of FDS data are used to build the databases for characterizing the aging degree and moisture Then, the moisture estimation models are developed using the weighted K-nearest neighbor (K-NN) algorithm The accuracy and applicability of the proposed models are finally discussed in laboratory and field conditions In that respect, the findings reveal that the reported model is available for moisture estimation of transformer oil-paper insulation under various aging degrees and test temperatures

FDS Measurement-Based Moisture Estimation Model for Transformer Oil-Paper Insulation Including the Aging Effect

The influence of nonuniform aging has not been considered in the prediction of the frequency domain spectroscopy (FDS) of the transformer oil-paper insulation (OPI) when using the finite element method (FEM). Given this issue, the modified simulation model was constructed based on the XY model and FEM. In the current work, the dielectric response property of the pressboards with different aging conditions in the modified model was characterized by its dielectric response parameters measured in the lab. Then the modified simulation model constructed in a 3-D system would be applied to study the effect of both nonuniform aging and the axial geometry on the FDS of OPI. Finally, the feasibility of the proposed models was verified by comparing the measured FDS and the predicted FDS. This article is expected to provide a model for predicting the FDS of the transformer OPI including the effect of nonuniform aging.

A Modified Simulation Model for Predicting the FDS of Transformer Oil-Paper Insulation Under Nonuniform Aging

Detection of probability of occurrence, type and severity of faults in transformer using frequency response analysis based numerical indices

In this article, a new testing method for the diagnosis of winding faults based on the resonant frequency of the transformer is proposed. The system that consists of the high-voltage dc power and high-voltage insulated gate bipolar transistor (IGBT) could charge the transformer periodically. The response measured from the bushing of the winding is the oscillating wave that can be used for evaluating the status. Carrying out the new test on a 220-kV three-winding, three-phase transformer in the field, the tests of insulation and the winding have been completed in one test. The calculated frequency verifies the feasibility and applicability of the method in theory and the voltage does not affect the resonant frequency in the test. The three-winding, three-phase model transformer was built to simulate the various parameters of the power supply, which proves that the features of the oscillating waves are brought about by the properties of the transformer. Also, the new testing method provides a new opportunity to do parameter identification of axial displacement and short-circuit (SC) fault. The abovementioned research shows that the proposed method can conduct the test of winding and insulation in one test for a short time, which proves a reference in the field.

A New Testing Method for the Diagnosis of Winding Faults in Transformer

Autotransformer (AT) is the most core power supply equipment, and overvoltage and short circuit (SC) fault may lead to winding deformation, which will have a negative impact on its insulation and even affect the operation of a train. The frequency response analysis (FRA) is widely used for detecting winding faults in a transformer. However, the direct measure of FRA for each split winding fails because the split windings are adopted to satisfy the impedance requirement of a high-speed railway, where the windings are connected inside the tank. A novel fault interpretation method based on image features and binary tree support vector machine (SVM) is proposed, which can get the condition of three windings in one measurement. Winding faults caused by different windings are simulated, including SC defect, axial deformation, and series capacitance variation, and the FRA curves are measured under various faults. Then, the features of the gray-level gradient co-occurrence matrix and the gray-level difference statistics are got from the polar plot of FRA. Finally, the image features are used as the inputs to the binary tree SVM for fault type and faulty winding classification. The results show that the proposed method has high accuracy for identifying fault type and faulty winding in AT.

Detection of Winding Faults Using Image Features and Binary Tree Support Vector Machine for Autotransformer

The windings of a traction transformer are always be threatened by the fluctuation of the load. Frequency response analysis (FRA) is regarded as the most effective method to diagnose the winding deformation. Appropriate FRA modelling method may help to predict typical winding faults for large transformers. The existing model usually simplify dozens of winding disks into several disks, it is applicable when analysing small displacement. However, when the disks between windings are seriously misplaced, the conventional model may not be suitable. This study presents an improved lumped parameter circuit model considering intersection capacitances and mutual inductance between windings. The feasibility of the proposed model is verified based on a 10 kV test transformer. Finally, the model is applied for a 220 kV traction transformer, the variation of parameters and features are analysed for axial displacement of different windings. The results show that the axial displacement may lead to increase of resonance frequency and decrease of amplitude. The low-frequency band (1–30 kHz) may be regarded as visible features for diagnosing axial displacement of H winding and the middle frequency band (50–200 kHz) may be a characteristic band for diagnosing axial displacement of T winding.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-epa.2019.0362

FRA modelling for diagnosing axial displacement of windings in traction transformers

Frequency response analysis (FRA) is a widely used approach for detecting winding faults in a transformer. Appropriate and quantitative FRA features will help to improve the accuracy of fault diagnosis. In this study, a novel FRA interpretation including new image features is proposed based on the image processing technique. First, winding faults of different windings are simulated in a test autotransformer and the FRA curves are measured under various faults. Then frequency region division method and image processing technique are first applied to the measured FRA curves. The area ratio and centroid deviation in different frequency regions are calculated through a novel algorithm. Finally, the image features are used as the inputs to support vector machine model. Additionally, three different parametric optimisation algorithm are compared during the training process. The results show that the particle swarm optimisation and image feature exhibit best performance for identifying winding faults.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-epa.2019.0933

Detection of transformer winding faults using FRA and image features

The invention discloses an eddy current loss evaluation method for a wound core traction transformer Under the condition that winding initial section width of a wound core, an outer section radius and silicon steel sheet thickness are given, continuously and gradually changed section width of the wound core is homogenized in a winding process; a traditional anisotropic equivalent conductivity parameter is corrected; and an electromagnetic field is solved to obtain a new eddy current loss calculation formula The method has the beneficial effects that the problem that an electromagnetic fieldsolution boundary condition is difficult to determine due to the continuously and gradually changed section width of a wound core is solved through application of a homogenized modeling thought, so the eddy current loss of the wound core traction transformer is effectively predicted

Wu Zhenyu

Papers

A New Testing Method for the Diagnosis of Winding Faults in Transformer

Detection of Winding Faults Using Image Features and Binary Tree Support Vector Machine for Autotransformer

FRA modelling for diagnosing axial displacement of windings in traction transformers

Detection of transformer winding faults using FRA and image features

Eddy current loss evaluation method for wound core traction transformer