Transformer Fault Detection Research Articles

Maximum Fourier spectrum cyclostationarity blind deconvolution and its application in structural health monitoring of power transformers

Abstract Power transformer is the most important equipment that affects whether the electric power system can be operated safely and normally, whose condition assessment problem has attracted considerable attention. Background noise frequently affects the effectiveness of nonintrusive techniques based on vibro-acoustic signals for structural health monitoring in power transformers. It is challenging to effectively eliminate background noise interference from time-frequency domain transformer defect detection techniques such as wavelet transformations, modal decomposition, etc. In this scenario, the Fourier spectrum cyclostationarity index (FSC) is designed based on the cyclostationarity index used for rotating machinery fault diagnosis to construct a maximum Fourier spectrum cyclostationarity blind deconvolution method (MFSCBD) for transformer fault detection in this paper. Firstly, the limitations of the traditional blind deconvolution (BD) in transformer fault detection are discussed in the mathematical principle. Then, a new BD framework based on Kepler optimization algorithm is proposed according to the principle of convex optimization to address the problems of difficulty in solving the complex blind objective function in the traditional differential BD framework and the ill-condition problem in the Rayleigh quotient BD framework. Subsequently, a synthetic nonstationary and nonlinear simulation signal is constructed for numerical verification, and a six-microphone array is designed to obtain the practical signals from the operating transformer to verify the performance of MFSCBD. Finally, the applications on the simulated and experimental signals of power transformers demonstrate that MFSCBD outperforms complete ensemble empirical mode decomposition with adaptive noise and successive variational mode decomposition to some extent for structural health monitoring.

CRDS‐based measurement of CO/CO2 concentration ratios for assessing transformer insulation aging

AbstractA gas analyzer has been developed using cavity ring‐down spectroscopy (CRDS) technology, designed to simultaneously measure the concentrations of CO2 and CO. These gases are critical indicators of transformer insulation degradation. The analyzer covers a CO2 concentration range from a few ppm to several thousand ppm and can measure CO/CO2 concentration ratios from 0.076 to 0.8. The selected absorption lines for CO and CO2 are centered at 6337.99 and 6338.59 cm−1, respectively. The two candidate lines are proximate to each other and nonoverlapping. The experimental results indicate that the system accurately fits the measured and calculated signals, enabling concentration measurements at the ppm level. This confirms its ability to measure the gas concentrations and ratios critical for assessing the condition of transformer insulation. This CRDS‐based system offers a reliable and sensitive method for early detection of potential transformer faults.

Optimizing Transformer Fault Detection Performance Through the Synergy of AI and Statistical Analysis for Multi-Fault Classification

Optimizing Transformer Fault Detection Performance Through the Synergy of AI and Statistical Analysis for Multi-Fault Classification

Research on blind source separation of operation sounds of metro power transformer through an Adaptive Threshold REPET algorithm

PurposeThe safe operation of the metro power transformer directly relates to the safety and efficiency of the entire metro system. Through voiceprint technology, the sounds emitted by the transformer can be monitored in real-time, thereby achieving real-time monitoring of the transformer’s operational status. However, the environment surrounding power transformers is filled with various interfering sounds that intertwine with both the normal operational voiceprints and faulty voiceprints of the transformer, severely impacting the accuracy and reliability of voiceprint identification. Therefore, effective preprocessing steps are required to identify and separate the sound signals of transformer operation, which is a prerequisite for subsequent analysis.Design/methodology/approachThis paper proposes an Adaptive Threshold Repeating Pattern Extraction Technique (REPET) algorithm to separate and denoise the transformer operation sound signals. By analyzing the Short-Time Fourier Transform (STFT) amplitude spectrum, the algorithm identifies and utilizes the repeating periodic structures within the signal to automatically adjust the threshold, effectively distinguishing and extracting stable background signals from transient foreground events. The REPET algorithm first calculates the autocorrelation matrix of the signal to determine the repeating period, then constructs a repeating segment model. Through comparison with the amplitude spectrum of the original signal, repeating patterns are extracted and a soft time-frequency mask is generated.FindingsAfter adaptive thresholding processing, the target signal is separated. Experiments conducted on mixed sounds to separate background sounds from foreground sounds using this algorithm and comparing the results with those obtained using the FastICA algorithm demonstrate that the Adaptive Threshold REPET method achieves good separation effects.Originality/valueA REPET method with adaptive threshold is proposed, which adopts the dynamic threshold adjustment mechanism, adaptively calculates the threshold for blind source separation and improves the adaptability and robustness of the algorithm to the statistical characteristics of the signal. It also lays the foundation for transformer fault detection based on acoustic fingerprinting.

Transformer fault diagnosis based on DBO-BiLSTM algorithm and LIF technology

Abstract In response to the deficiencies of traditional power transformer fault detection techniques, such as low sensitivity and the inability for online monitoring, a novel transformer fault diagnosis model combining Laser-Induced Fluorescence (LIF) technology with deep learning is proposed. Initially, the spectral data of transformer insulation oil is acquired using LIF technology, yielding spectral data for various fault types. Subsequently, MinMaxScaler (MMS) and Standard Normalized Variate (SNV) methods are employed for denoising and preprocessing the spectral data. The preprocessed data is then subjected to dimensionality reduction using Linear Discriminant Analysis (LDA) and T-distributed Stochastic Neighbor Embedding (T-SNE) to ensure that the spectral data retains maximal feature information while minimizing its dimensionality. Following this, Long Short-Term Memory (LSTM), Bi-directional Long Short-Term Memory (BiLSTM), Dung Beetle Optimizer-Bi-directional Long Short Term Memory (DBO-BiLSTM), Convolutional Neural Network (CNN), and Support Vector Machine (SVM) models are constructed. The reduced-dimensional data is fed into each of the five models for training to facilitate transformer fault diagnosis. Through comparative analysis among the five models, the optimal model is selected. Experimental results indicate that the DBO-BiLSTM model is the most suitable for transformer fault diagnosis in this experiment, underscoring its significant implications for ensuring the safety of power systems.

Development of All Solid State Nanosecond Pulse Transformer Fault Detection Device Based on Marx Circuit

Abstract a nanosecond pulse generator with a fast leading edge for transformer fault diagnosis is developed. The device is composed of a full solid-state nanosecond pulse generator based on Marx circuit and with a tail cut switch and a field programmable logic gate array (FPGA) device. Among them, the nanosecond pulse source is mainly composed of DC power supply, control circuit and main circuit. The main circuit is a 9-level modular Marx circuit, using MOSFET as the main switch and tail cut switch; The control circuit generates a synchronous trigger pulse signal, which drives the MOSFET to work synchronously after being isolated through the optical fiber. The output nanosecond pulse voltage parameters are adjustable amplitude 0-8kv, pulse width 100˜1000ns, repetition frequency 1hz˜1000hz, rising edge 30ns and falling edge 40ns. The transformer fault diagnosis platform is built, and the transformer off-line and on-line fault diagnosis is carried out, which paves the way for further exploration of transformer fault diagnosis technology.

Research on anomaly detection in oil chromatography based on convolutional neural network and grey relational analysis algorithm

Abstract The power transformer is a crucial voltage conversion node and a core device in substations. Currently, dissolved gas analysis (DGA) in oil is a practical and validated method for assessing the operational status of transformers. In this study, addressing issues such as insufficient data mining from single dissolved gas measurements, we employed a combination of Convolutional Neural Network and Grey Relational Analysis algorithm to enhance the reliability of identifying abnormal dissolved gas data in transformer oil. This approach improves the sensitivity and accuracy of transformer fault detection. The study includes data processing and result analysis, indicating that the proposed algorithm effectively identifies abnormal dissolved gas data in transformer oil.

Fault Detection and Normal Operating Condition in Power Transformers via Pattern Recognition Artificial Neural Network

Aging, degradation, or damage to internal insulation materials often contribute to transformer failures. Furthermore, combustible gases can be produced when these insulation materials experience thermal or electrical stresses. This paper presents an artificial neural network for pattern recognition (PRN) to classify the operating conditions of power transformers (normal, thermal faults, and electrical faults) depending on the combustible gases present in them. Two network configurations were presented, one with five and the other with ten neurons in the hidden layer. The main advantage of applying this model through artificial neural networks is its ability to capture the nonlinear characteristics of the samples under study, thus avoiding the need for iterative procedures. The effectiveness and applicability of the proposed methodology were evaluated on 815 real data samples. Based on the results, the PRN performed well in both training and validation (for samples that were not part of the training), with a mean squared error (MSE) close to expected (0.001). The network was able to classify the samples with a 98% accuracy rate of the 815 samples presented and with 100% accuracy in validation, showing that the methodology developed is capable of acting as a tool for diagnosing the operability of power transformers.

Liquid-solid interface diffusion behaviors of furfural in the insulation systems of natural ester-cellulose paper and mineral oil-cellulose paper

Furfural as an ageing product of cellulose paper can be detected by the furfural content in the oil. With the construction of green power grid, more and more power equipment adopts environmentally friendly insulating fluids. The investigation of the diffusion behaviors of furfural at different oil-paper interfaces can provide a basis for fault detection in power transformers. The liquid-solid interface diffusion behaviors of furfural in natural ester-cellulose paper and mineral oil-cellulose paper insulating systems are investigated by molecular dynamics simulation in this paper. Results show that the range of center-of-mass movement distances of furfural in mineral oil-cellulose paper is about 5 times that of natural ester-cellulose paper. The main reason for this is that the long-chain macromolecules composing the natural ester undergo curling in the extended flow. As polar molecules, natural ester can attract furfural, which is also a polar molecule. And the electrostatic potential mapping interval of furfural is about 4.4 times that of mineral oil, but 1.1 times that of natural ester, with which it overlaps more. These hinder the diffusion of furfural molecules in the natural ester-cellulose paper liquid-solid interface. This research provides theoretical support for the diffusion of furfural in the insulation systems of natural ester-cellulose paper and mineral oil-cellulose paper. It also provides useful guidance for fault detection in transformers using natural ester insulating fluids.

Comprehensive Study on Power Transformer Fault Detection and Diagnosis Post-Cianjur Earthquake: A Case Investigation

Comprehensive Study on Power Transformer Fault Detection and Diagnosis Post-Cianjur Earthquake: A Case Investigation

Deep Learning-Based Algorithm for Internal Fault Detection of Power Transformers during Inrush Current at Distribution Substations

The reliability and stability of differential protection in power transformers could be threatened by several types of inferences, including magnetizing inrush currents, current transformer saturation, and overexcitation from external faults. The robustness of deep learning applications employed for power system protection in recent years has offered solutions to deal with several disturbances. This paper presents a method for detecting internal faults in power transformers occurring simultaneously with inrush currents. It involves utilizing a data window (DW) and stacked denoising autoencoders. Unlike the conventional method, the proposed scheme requires no thresholds to discriminate internal faults and inrush currents. The performance of the algorithm was verified using fault data from a typical Korean 154 kV distribution substation. Inrush current variation and internal faults were simulated and generated in PSCAD/EMTDC, considering various parameters that affect an inrush current. The results indicate that the proposed scheme can detect the appearance of internal faults occurring simultaneously with an inrush current. Moreover, it shows promising results compared to the prevailing methods, ensuring the superiority of the proposed method. From sample N–3, the proposed DNN demonstrates accurate discrimination between internal faults and inrush currents, achieving accuracy, sensitivity, and precision values of 100%.

In-Depth Simulation of Low-Voltage AC Arc-Fault and Saturated Transformer Fault Detection System

In-Depth Simulation of Low-Voltage AC Arc-Fault and Saturated Transformer Fault Detection System

Research on power equipment troubleshooting based on improved AlexNet neural network

Power equipment is an important component of the whole power system, so that it is obvious that it is required to develop a correct method for accurate analysis of the infrared image features of the equipment in the field of detection and recognition. This study proposes a troubleshooting strategy for the power equipment based on the improved AlexNet neural network. Multi-scale images based on the Pan model are used to determine the equipment features, and to determine the shortcomings of AlexNet neural network, such as slower recognition speed and easy overfitting. After knowing these shortcomings, it would become possible to improve the specific recognition model performance by adding a pooling layer, modifying the activation function, replacing the LRN with BN layer, and optimizing the parameters of the improved WOA algorithm, and other measures. In the simulation experiments, this paper's algorithm was compared with AlexNet, YOLO v3, and Faster R-CNN algorithms in the lightning arrester fault detection, circuit breaker fault detection, mutual transformer fault detection, and insulator fault detection improved by an average of 5.47 %, 4.69 %, and 3.42 %, which showed that the algorithm had a better recognition effect.

Design of Power Transformer Fault Detection of SCADA Alarm Using Fault Tree Analysis, Smooth Holtz–Winters, and L-BFGS for Smart Utility Control Centers

Design of Power Transformer Fault Detection of SCADA Alarm Using Fault Tree Analysis, Smooth Holtz–Winters, and L-BFGS for Smart Utility Control Centers

Transformer fault diagnosis based on MPA-RF algorithm and LIF technology

Power transformers are essential components in power systems used to regulate voltage, transmit electrical energy, provide isolation, and match loads. They contribute to efficient and reliable electricity transmission and distribution. However, traditional methods for diagnosing transformer faults are time-consuming, not suitable for online monitoring, and greatly affected by environmental conditions. In this experiment, we propose the use of laser-induced fluorescence (LIF) technology for transformer fault detection. LIF technology is a method for analyzing and detecting specific molecules or atoms in samples. It combines laser technology with fluorescence measurements, making it a powerful analytical tool. It achieves high sensitivity and selectivity in analyzing molecules and atoms by exciting and detecting fluorescence in the sample. This makes it an important technology in scientific research and practical applications. Furthermore, LIF technology has not been previously applied to power transformer fault diagnosis. Therefore, this experiment introduces a transformer fault diagnosis model based on the marine predators algorithm (MPA) optimized random forest (RF) algorithm and LIF spectroscopy technology. Four different oil samples were selected for experimentation: crude oil, thermally faulty oil, partially moist oil, and electrically faulty oil. First, LIF technology for collect spectral images and data from the different fault oil samples. The obtained spectral data was preprocessed using two methods, multivariate scatter correction (MSC) and standardization method (SNV). Then, principal component analysis (PCA) and kernel principal component analysis (KPCA) for reducing the dimensionality of the preprocessed spectral data. Finally, the RF model, MPA-RF model, and PSO-RF model were established; and the reduced data was input into the model for training. Through comparisons of the predictions on the test set, evaluation metrics of the algorithm (including fitting coefficient, MSE, RMSE, and RMSE), and iteration convergence curves, the best transformer fault diagnosis model was identified. The results show that the MSC-KPCA-MPA-RF model has the best matching resule, with a fitting coefficient of 0.9963 and a mean square error of 0.0047. The SNV-PCA-MPA-RF model has the worst fitting effect, with a fitting coefficient of 0.9840 and a mean square error of 0.0199. Through comparisons of the convergence of different models, the MSC-KPCA-MPA-RF model has the best convergence and is the most applicable model for transformer fault diagnosis in this experiment. This model has significant implications for ensuring the safety of the power system.

Adsorption properties of noble-metal (Ag, Rh, or Au)-doped CeO2(1 1 0) to CO: A DFT + U study

Dissolved gas analysis (DGA) is an effective method for fault detection of oil-immersed transformers, and CO is one of the characteristic gases in insulation oil. Therefore, based on density functional theory, the adsorption properties of three noble metals (Ag, Rh, or Au)-doped CeO2 (110) to CO were studied in this paper. The adsorption energy, charge transfer, density of states (DOS), and deformation charge density (DCD) were calculated to analyze the adsorption behavior of CO on the material surface. The frontier molecular orbital, energy band structure, and desorption time were also calculated to evaluate the potential of the three doping systems as CO sensors. The results show that the adsorption properties of Ag-CeO2, Rh-CeO2, and Au-CeO2 to CO are significantly higher than that of pristine CeO2, with adsorption energies are −54.61, −85.78, −205.57 kJ/mol, respectively. However, Rh-CeO2 exhibits superior sensitivity and satisfactory recovery time towards CO compared with Ag-CeO2 and Au-CeO2. Rh-CeO2 also shows excellent selectivity to CO, which is a promising candidate material for CO sensors. The research conducted in this work is intended to contribute to dissolved gas analysis (DGA).

Development of Double Channel Transformer Voiceprint Detection System Based on MEMS Digital Silicon Microphone

Aiming for on-line monitoring of typical mechanical faults of transformer, a two-channel MEMS digital silicon wheat voice print detection system is developed, which consists of two channel MEMS digital silicon wheat array, the host of sound acquisition system and the upper computer software with the function of displaying real-time waveform and spectrum analysis. The device can operate reliably in complex environments by adjusting the array spacing, improving the power quality, and adjusting the network protocol. Further, different test environments were built to test the resolution performance of the equipment to the center frequency of the sound source at different distances and angles of the sound source. The results show that the system can realize the resolution of the center frequency of different sound source distance and Angle, and still has a good center frequency resolution performance in the case of environmental noise. At the same time, the system has strong stability, environmental adaptability, easy installation and non-contact detection characteristics, can be used for real-time detection and diagnosis of transformer faults.

Early fault diagnosis of transformer winding based on leakage magnetic field and DSAN learning method

Aiming at the problem of lack of training samples and low accuracy in transformer early winding fault diagnosis, this paper proposes a transformer early faults diagnosis method based on transfer learning and leakage magnetic field characteristic quantity. The method uses the leakage magnetic field waveform on the measuring point of the simulated transformer winding to draw the Lissajous figure to calculate the characteristic quantity. The characteristic quantity of the simulation model is used to train the convolutional neural network (CNN) faults classification model. The CNN fault classification model is transferred to the actual transformer fault detection through the improved deep subdomain adaptive network (DSAN), so as to realize the fault diagnosis of the actual transformer by the classification model trained by the simulation data. The test examples of the actual transformer early fault experimental platform and the leakage magnetic field measurement platform are established, and the feasibility of the transfer learning method based on the leakage magnetic field feature quantity proposed in this paper is verified.

Analysis of Interturn Faults on Transformer Based on Electromagnetic-Mechanical Coupling

A running transformer frequently experiences interturn faults; they are typically difficult to detect in their early stages but eventually progress to interturn short circuits, which cause damage to the transformer. Therefore, finding out the fault mechanism of the full interturn fault process can provide a theoretical basis for transformer fault detection. In this paper, an electromagnetic-solid mechanics coupled finite element model consistent with an actual oil-immersed three-phase transformer is established. The transient process of winding from interturn discharge to interturn short circuit is simulated to study the electromagnetic characteristics as well as the mechanical characteristics during transformer failure. The model parameters of the transformer are simulated to obtain the fault current, electromagnetic parameters and other performance parameters to study the characteristics of the magnetic field and coil force when interturn faults occur. Finally, the vibration of the transformer casing is used to detect as well as diagnose the transformer fault situation, providing a theoretical basis for the study of transformer detection and diagnosis capability improvement measures.

The Fault Detection of Power Transformers Based on Kernel Principal Component Analysis

The Fault Detection of Power Transformers Based on Kernel Principal Component Analysis