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

Frequency response analysis (FRA) suffers from the interpretation of results despite its potential ability to detect faults related to the power transformer windings. This article presents a technique for interpreting frequency responses, which is based on image processing and a deep learning method called graph convolutional neural network (CNN). The proposed procedure transfers frequency responses into 2-D images through a visualization technique. The resulting images are aggregated into a dataset to be used as the CNN input. The proposed technique is applied on frequency responses of two different winding models with short circuit (SC) faults. The SC faults with different intensities are applied on different sections of a simulated ladder model winding and a 20 kV winding of a 1.6 MVA distribution transformer. After determining the frequency response for each faulty case and applying the visualization technique, the precise locating of the SC faults is performed by the CNN. Then, the results are analyzed by performance evaluation metrics. At this stage, the high performance of the CNN in the use of 2-D images instead of the conventional method is observed. Finally, by testing the high impedance SC faults in different sections of the simulated winding model and applying the suggested method step by step, early detection of the SC fault is also performed in this article. It should be noted that the suggested technique, in addition to its accuracy and high detection speed, can be considered as an important step in automatic interpretation of frequency responses for online monitoring of transformers. 

Turn-to-Turn Short Circuit Fault Localization in Transformer Winding via Image Processing and Deep Learning Method

Transformers are playing an increasingly significant part in energy conversion, transmission, and distribution, which link various resources, including conventional, renewable, and sustainable energy, from generation to consumption. Power transformers and their components are vulnerable to various operational factors during their entire life cycle, which may lead to catastrophic failures, irreversible revenue losses, and power outages. Hence, it is crucial to investigate transformer condition assessment to grasp the operating state accurately to reduce the failures and operating costs and enhance the reliability performance. In this context, comprehensive data mining and analysis based on intelligent algorithms are of great significance for promoting the comprehensiveness, efficiency, and accuracy of condition assessment. In this article, in an attempt to provide and reveal the current status and evolution of intelligent algorithms for transformer condition assessment and provide a better understanding of research perspectives, a unified framework of intelligent algorithms for transformer condition assessment and a survey of new findings in this rapidly-advancing field are presented. First, the failure statistics analysis is outlined, and the developing mechanism of the transformer internal latent fault is investigated. Then, in combination with intelligent demands of the tasks in each stage of transformer condition assessment under big data, we analyze the data source in-depth and redefine the concept and architecture of transformer condition assessment. Furthermore, the typical methods widely used in transformer condition assessment are mainly divided into rule, information fusion, and artificial intelligence. The new findings for intelligent algorithms are also elaborated, including differentiated evaluation, uncertainty methods, and big data analysis. Finally, future research directions are discussed.

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Review on Evolution of Intelligent Algorithms for Transformer Condition Assessment

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

A new testing method (oscillating wave method) has been proposed as a solution to the problem of short maintenance time in the traction power supply system, which could complete tests of winding and insulation via one connection and improve the maintenance efficiency of the transformer. Through the method being proved highly sensitive to winding faults, the variation under different winding states, however, remains unclear. This article aims to analyze oscillating wave signatures in the occurrence of axial displacement (AD). First, the modeling method will be proposed that can simulate oscillating wave signatures in different winding states. Then, the time-domain feature and the new proposed frequency-domain feature are applied to analyze the fault extent and location of AD fault. The results show that this model accurately simulates oscillating wave signatures. Moreover, the time-domain feature [Euclidean distance (ED)] is closely related to fault extent. The combined use of the time-domain feature (ED) and the frequency-domain feature (W-Energy entropy in Z-Score) is effective in the identification of fault location. These features, therefore, can provide the reference for winding state evaluation.

Feature Analysis of Oscillating Wave Signal for Axial Displacement in Autotransformer

To further improve the efficiency and reliability of transformer winding state detection, this paper proposes a method for detecting transformer winding oscillating wave faults based on correlation coefficients and discrete wavelet transform (DWT), and a 10 kV transformer winding fault simulation test platform is built for experimental analysis. Through filtering and discrete wavelet transformation of the measured data, the discrete wavelet detail signal which can be used to evaluate the characteristics of the oscillatory wave is obtained. Finally, the experimental analysis of the oscillatory wave under the normal and fault conditions of the winding is carried out. The results show that the self-excited oscillatory wave characteristics obtained by discrete wavelet transform have good repeatability and can reflect the difference between windings. When the axial displacement and parallel capacitance fault occur, the characteristics change obviously, which can provide a reference for transformer winding fault detection.

The Fault Detection Method of Transformer Winding Oscillatory Wave Based on Discrete Wavelet

The invention discloses a transformer winding axial displacement and frequency response correlation research platform and a test method thereof. The research platform is mainly composed of an axial displacement fault simulation device and a transformer model. The test method comprises steps: simulating different axial displacement faults of a transformer model through a fault simulation device, and obtaining a frequency response curve and a frequency response function under different axial displacement faults through a frequency response tester; and determining the fault degree through the axial displacement fault diagnosis parameter H. According to the invention, frequency response changes of the winding under different axial displacement faults can be effectively simulated, characteristic parameters are extracted for research, and the method can be used for research and provides reference for engineering practice.

Feiming Jiang

Papers

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

Feature Analysis of Oscillating Wave Signal for Axial Displacement in Autotransformer

The Fault Detection Method of Transformer Winding Oscillatory Wave Based on Discrete Wavelet

Transformer winding axial displacement and frequency response correlation research platform and test method thereof