Transformer On-line Monitoring Research Articles

Adsorption Mechanisms of TM3 (TM = Mo, Ru, Au)-Decorated Tin Sulfide Monolayers for the Decomposition of Gas Components under Fault Conditions in Oil-Immersed Transformers.

Oil-immersed transformers play a pivotal role owing to their environmentally friendly characteristics, compact footprint, and cost-effectiveness. Ensuring the online monitoring of oil-immersed transformers is a fundamental measure to ensure the secure and stable operation of modern power systems. In this paper, metal particle cluster-doped SnS is firstly used in the adsorption and sensing of decomposition components (CO, C2H2) under fault conditions in oil-immersed transformers. The study comprehensively analyzed band structure, differential charge density, density of states, and molecular orbital theory to unveil the adsorption and sensing mechanisms of target gases. The findings suggest that the modification of metal particle clusters can enhance the surface electronic properties of single-layer SnS. In the regions of metal particle clusters and the gas-surface reaction area, electronic activity is significantly heightened, primarily attributed to the contribution of d-orbital electrons of the metal cluster structures. The modified SnS exhibits adsorption capacity in the following order: Ru3-SnS > Mo3-SnS > Au3-SnS. Additionally, the modified material demonstrates increased competitiveness for C2H2, with adsorption types falling under physical chemistry adsorption. Different metal elements exert diverse effects on the electronic distribution of the entire system, providing a theoretical foundation for the preparation of corresponding sensors. The findings in this work offer numerical insights for the further preparation and development of SnS nanosensors, concurrently shedding light on the online monitoring of faults in oil-immersed transformers.

A transformer acoustic signal analysis method based on matrix pencil and hybrid deep neural network

Acoustic signal analysis is an important component of transformer online monitoring. Currently, traditional methods have problems such as low spectral resolution, imbalanced sample distribution, and unsatisfactory classification performance. This article first introduces the matrix pencil algorithm for time-frequency spectrum analysis of acoustic signals, and then uses the SMOTE algorithm to expand the imbalanced samples. Then, an ACmix hybrid deep neural network model is constructed to classify 11 types of transformer operation and environmental acoustic signals. Finally, detailed experiments were conducted on the method proposed in this paper, and the experimental results showed that the matrix pencil algorithm has high time-frequency resolution and good noise resistance performance. The SMOTE sample expansion method can significantly improve the recognition accuracy by more than 2%. Overall accuracy of the proposed method in acoustic signal classification tasks reaches 91.81%.

Instruments for On-line Monitoring of Transformers

The main objective of the paper is to describe the experience with newly developed monitoring system for transformers with oil-paper insulation system. From the view of operation reliability, it is necessary for operators to provide a monitoring of transformer’s failure free operation. The application of monitoring system enables to warn timely of the rising failure and also decrease of degradation of single subsystems of machine. When failure is detected, it is important to support this detection by other measurements that would lead to exact determination of the failure. SFRA analysis (Sweep Frequency Response Analysis) is such additional method in our monitoring.

Research and Design of Power Transformer Online Monitoring System

Research and Design of Power Transformer Online Monitoring System

Identification and Calibration Method of Deviation of Main Transformer Online Monitoring Date Groups

Dissolved gas in transformer oil (DGA) online monitoring data can reflect equipment insulation performance, which provides an important basis for transformer condition assessment. The accuracy of online monitoring data directly affects the correctness of transformer condition assessment, and the health condition of the online monitoring data device also affects the effectiveness and reasonableness of online monitoring data. With the accumulation of operation time, the DGA online monitoring device faces many problems, resulting in the overall deviation of this indicator data from the actual value, and the transformer online monitoring device cannot play its monitoring effect. To solve these problems, this paper firstly linearizes the data of the DGA online monitoring device by segmentation, extracts the characteristics of the line segment curve, constructs a segmented correlation mining model, and mines the correlation of different indicators of DGA. Then, according to the change of correlation strength and weakness between online monitoring data in time sequence, the data deviation is tracked. Finally, a multi‐indicator back propagation (BP) neural network algorithm optimized by the genetic simulated annealing algorithm is constructed to calibrate the deviated data. The analysis shows that the method can identify the population deviation of online monitoring data and calibrate the deviated data effectively. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Research on online monitoring of grid current transformer based on transformers and BiGRU

Transformers play a crucial role in ensuring the safety of power grids. It is of great value to diagnose faults using the large amount of power data generated by the grid. It is possible to detect internal latent faults in transformers in advance of their occurrence if the normal operating condition of the transformer is detected in a timely manner. To perform online fault diagnosis of grid current transformers, we combine the Transformer and BiGRU methods. There is a temporal component to the fault input sample sequences. By using Transformer’s multi-headed attention mechanism to extract deep features from fault input sample sequences, the temporal association between latent variables can be fully exploited. As a result of the extraction of features, BiGRU is used to generate fault category coding as an output. The experimental results indicate that using the proposed algorithm achieves better results than using a single model, which is useful for the study and application of fault diagnosis in power grids for current transformers.

Transformer online monitoring and intelligent diagnosis system design

Transformer is a pivotal equipment in the power system, and its safe and stable operation affects the entire power system. Therefore, the on-line monitoring and fault diagnosis of transformers are particularly important in practical applications. The traditional maintenance method is called regular maintenance. This maintenance method is to call a large number of inspection workers at regular intervals to carry out large-scale inspections, not only to collect various working data of the transformer, but also to observe its working status, and finally manually. Check whether there are hidden dangers in major machines, and judge whether the transformer can run safely and stably according to the data obtained by these large amounts of manpower and material resources. In order to improve the efficiency and accuracy of transformer fault diagnosis, and to carry out all-round real-time online monitoring and fault diagnosis of 220kV power transformers, considering the actual needs of electricity companies, this paper is based on the Qt development platform, C++, MySQL and other development environments. And developed a transformer online monitoring and intelligent diagnosis system, which mainly has the functions of providing basic information management, online monitoring, fault diagnosis, giving operation and maintenance suggestions, and providing users with visual comprehensive analysis. Tested with actual data, it is proved that the system can objectively reflect the operating state of the transformer and carry out real-time fault diagnosis. The system designed in this paper meets the needs of operators to understand the status of distribution transformers, and effectively solves the problem of untimely detection of distribution transformer faults. It can effectively improve the efficiency of detection, reduce the probability of major accidents, and reduce operation and maintenance costs., which saves financial and human resources, and is of great significance for improving economic efficiency and personal and equipment safety.

LFCC-based transformer sound feature extraction and analysis

As one of the important components of the power system, the safe and stable operation of transformers is particularly important. However, the traditional transformer fault diagnosis methods have certain disadvantages. Sound-based online monitoring of transformers is a simple and reliable noninvasive monitoring method that does not interfere with the normal operation of transformers and can reflect the working status and abnormal conditions of transformers well. This paper firstly introduces the relevant feature extraction techniques and designs a linear frequency filter bank based on the commonly used Meier cepstrum coefficients (MFCC), combined with the actual operation of transformers. After that, the characteristic quantities of some transformer sound signals containing common noise are analyzed, and the corresponding conclusions are drawn by combining with real data, and verified the validity of the methods used.

TRANSFORMER BUSHING – A PART OF MEASUREMENT SYSTEM

Power transformer is one of the most important and most expensive components in the electric power system (EPS) and requires, from its production and throughout lifetime, continuous monitoring and checks of the availability in the power system. All the measurements and tests on the transformer which manufacturers attempt, in order to determine the quality of their products, are carried out through the connection of different types of bushings, and these measurements and tests are conducted in accordance with requirements of applicable standards. Also, the owner of the transformer during the exploitation phase wishes and needs to know the status and availability of transformers for future work. The only available points of the transformer inside are the bushings. The technical practice from the early beginnings of the transformers are periodic off-line measurements. Following that practice and experience, the need arose for continuous supervision of transformer operation (on-line). In the 90’s the first simple systems for the transformer on-line monitoring appeared. Today, it is an established fact that the modern systems for on-line monitoring of transformers provide a complete insight in the transformer state including alarms in the case of critical states. It is realistic to expect in the future that these same systems, apart from providing diagnostics and giving alarms, will be authorized to switch off transformers in the case of necessity with high degree of confidence. An important role in on-line monitoring, as a part of measurement system, has a transformer bushing. For the purpose of both off-line and on-line measurements, manufacturers of the bushings are equipping them with the measuring tap. Unfortunately, despite intentions to standardize the components of the power system, this is not the case with the measuring tap, and the intention of this paper is to draw attention to these problems and to try to find a solution.

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

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.

Density Functional Theory Study of Oil-Immersed Transformers Characteristic Decomposition Products Adsorption on n ZrO2 (n = 1~2) Modified GaN Nanotubes

Oil-immersed transformers, with safe reliability and high utilization, plays an irreplaceable role in power transmission and transformation. Unavoidable insulation defects can occur that affect the safety of the power system and socio-economic, thus timely online monitoring becomes a focus of attention in the power sector. In this study, n ZrO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> (n = 1-2) modified GaN nanotubes are firstly proposed as a novel gas-sensing material for estimating the discharge faults in oil-immersed transformers through dissolved gas analysis. Density functional theory calculations are carried out to deeply explore the characteristic dissolved gases adsorption and sensing mechanisms. The density of states, charge density difference, band structures, and theoretical recovery time are analyzed to evaluate the feasibility and applicability of the gas sensor. The data shows that ZrO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> dopant provides active sites for gas adsorption. n ZrO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -GaNNTs (n = 1-2) has limited gas sensitivity to H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> and CH <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> but demonstrates considerable ability to capture C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> . Moreover, all adsorption has excellent gas-sensitive recovery properties, aiding in the rapid recycling of sensors. Meanwhile, the verification is carried out by experiments. Results in this work can be employed for practical production of n ZrO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> (n = 1-2) modified GaN nanotubes gas sensors, ultimately realizing online monitoring of oil-immersed transformer.

Pd4 cluster decorated SnO2 nanowire for detecting characteristic gases in oil-immersed transformers: A theoretical and experimental study

In this paper, Pd4 cluster decorated SnO2 (Pd4-SnO2) nanowire is considered as a novel gas sensor alternative for detecting the typical dissolved gases in transformer oil, including hydrogen (H2) and acetylene (C2H2). Specifically, the most stable geometric structures of gas molecules, Pd4-SnO2 and gases adsorbed by Pd4-SnO2 are systematically explored via the density functional theory (DFT) method. The geometric structure formation, density of states (DOS), deformation charge density (DCD) and molecular orbital theory analysis are used to reveal the gas adsorption and sensing mechanism. Simulation calculation results show that Pd4 cluster modification can significantly enhance the gas-sensitive response of intrinsic SnO2, Pd atoms which provide active sites for gas adsorption owing to their outstanding metallic and catalytic behaviors. It also offers excellent adsorption capacity for both gas molecules. Meanwhile, the Pd cluster decorated SnO2 sensing materials are successfully synthesized by electrospinning technology combined with magnetron sputtering process. The micromorphology structure of the synthesized samples are analyzed by using SEM and other microscopic techniques. The gas-sensing investigations indicate that this composite nanomaterial could be a potential candidate for gas-sensing applications. The testing data shows high consistency to the simulation conclusion and the results in this work can be applied to produce and promote Pd cluster decorated SnO2 gas sensors, ultimately realizing online monitoring of oil-immersed transformers.

Effects of Converter Harmonic Voltages on Transformer Insulation Ageing and an Online Monitoring Method for Interlayer Insulation

Power electronic converter can produce massive voltage stresses on the connected transformer, with great effects on electrical ageing of the insulation. Such effects can be diverse upon different insulation components under different harmonic voltages, which have not been well clarified. This article aims at enhancing online monitoring of converter transformers from two aspects. First, electrical ageing evaluation models are developed for different types of transformer insulation. It is the first time to clarify the effects related to the component location and the ageing severity under differential-mode and common-mode (CM) voltages. Interlayer (IL) insulation faces a serious ageing threat and the line-lead component is aged with the severest degree. Second, CM voltages are used to build transfer function (TF) measurement for IL insulation monitoring. The key is to identify the optimal monitoring frequency with the highest sensitivity. Based on the analytical case introducing typical parameters, the TF performance is analyzed in different ageing conditions. Online experiments validate that the proposed method is effective and has good application prospect. The contributions lie in that the diverse ageing effects of converter harmonic voltages on different insulation components can be distinguished and online monitoring of IL insulation can be achieved effectively.

Deep Primary and Secondary Fusion Transformer Based on Internet of Things Technology

As one of the core equipment of power system, power transformer has great fault influence and complex fault causes. In order to ensure the safe and stable operation of power system, its operation state must be monitored and judged. With the increasing maturity of the Internet industry and the continuous development of sensor technology, the emergence of smart grid has contributed to the realization of intelligent transformer on-line monitoring, condition evaluation and fault diagnosis. This paper studies the deep primary and secondary fusion transformer based on the Internet of Things technology, summarizes the development status of intelligent transformer and its existing problems on the basis of relevant literature, and proposes the optimization technology of deep primary and secondary fusion transformer based on the Internet of Things technology to solve its existing problems, and conducts related experiments and fault detection research on the proposed technology. The experimental results show that this optimization technology has good feasibility, good self-state evaluation and fault diagnosis functions. The deep primary and secondary fusion based on the Internet of Things technology proposed in this study can increase the reliability monitoring of intelligent transformer operation, provide a strong technical guarantee for the normal operation of distribution network, and also provide important technical support for the next research of distribution network technology.

Anomaly Detection and Pattern Differentiation in Monitoring Data from Power Transformers

Aiming at the problem of abnormal data generated by a power transformer on-line monitoring system due to the influences of transformer operation state change, external environmental interference, communication interruption, and other factors, a method of anomaly recognition and differentiation for monitoring data was proposed. Firstly, the empirical wavelet transform (EWT) and the autoregressive integrated moving average (ARIMA) model were used for time series modelling of monitoring data to obtain the residual sequence reflecting the anomaly monitoring data value, and then the isolation forest algorithm was used to identify the abnormal information, and the monitoring sequence was segmented according to the recognition results. Secondly, the segmented sequence was symbolised by the improved multi-dimensional SAX vector representation method, and the assessment of the anomaly pattern was made by calculating the similarity score of the adjacent symbol vectors, and the monitoring sequence correlation was further used to verify the assessment. Finally, the case study result shows that the proposed method can reliably recognise abnormal data and accurately distinguish between invalid and valid anomaly patterns.

Optimistic locating method of transformer voiceprint based on beam and delay algorithm

In view of the on-line inspection mode of transformer voiceprint information during operation, an optimistic location method is proposed to assist the acoustic print diagnosis for fault identification, location and further evaluation work. Firstly, according to the directional location algorithm of antenna array (Beamforming), traditional directional location is extended to general spatial location. Then on the basis of TDOA(Time Difference Of Arrival) algorithm of time domain signal characteristic analysis, integrated weighted processing and data centralization, on the one hand, reduce the edge value, on the other hand, correct the trend of bad data, and improve the existing cross-correlation operation. The simulation results show that the optimized beam-based and weighted TDOA algorithm has excellent performance in 2D localization of transformer voiceprint. These research results can provide a basis for transformer online monitoring and voice print diagnosis.

Online Monitoring Data Processing Method of Transformer Oil Chromatogram Based on Association Rules

AbstractThe oil chromatography online monitoring system can monitor the insulation status of power transformers continuously and real‐time. The accuracy of its detection data directly affects the stable operation of the transformers. Aiming at the problem of abnormal data in the current transformer online monitoring data set, this paper proposes an online oil chromatographic data processing method based on association rules and support vector regression (SVR) analysis. First, using piecewise linearization to convert numerical variables into Boolean variables, and then exploring gas indicators with strong correlations in oil chromatogram. Then, using the improved box plot method to detect the abnormal value points in it, and distinguish the data of different abnormal patterns based on the association rules. Finally, the advanced particle swarm optimization support vector regression (APSO‐SVR) algorithm is used to repair the data to improve the credibility of the data set. Using actual transformer oil chromatographic monitoring data for testing, the results show that the proposed data processing method can effectively improve the accuracy of oil chromatographic online monitoring data. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Co, Rh decorated GaNNTs for online monitoring of characteristic decomposition products in oil-immersed transformer

Oil-immersed transformer is one of the most widely used electrical equipment in modern power systems due to its high utilization, safe reliability, and high space utilization. Real-time operation status and insulation fault monitoring of oil-immersed transformer have attracted widespread attention in recent years. In this study, Co, Rh decorated GaN nanotubes is proposed as a novel gas-sensing material for diagnosing the discharge faults in oil-immersed transformer by on-line detecting the characteristic decomposition products. The gas-sensing properties and adsorption mechanism of H2, C2H2 and CH4 on Co, Rh decorated GaNNTs are deeply analyzed from the atomic size. Pristine GaNNTs has circumscribed response to the characteristic decomposition products. While Co, Rh modification can significantly enhance the adsorption capacity of GaNNTs surface, which acts as active sites for gas adsorption due to its virtue of eminent electropositive and catalytic oxidation. Both Co and Rh decorated GaNNTs has strong selectivity to C2H2 and H2 molecules, except CH4 molecule, as only a weak physical adsorption acts on CH4. All calculation data in this work can be employed for practical production of Co, Rh decorated GaNNTs gas sensors, ultimately realizing online monitoring of oil-immersed transformer.

Transformer Operating State Monitoring System Based on Wireless Sensor Networks

Transformer operating state prediction is an important module of the transformer operating state maintenance system. Since the analysis of dissolved gas in oil is the prerequisite for realizing the analysis of transformer operating status, the key to predicting the transformer operating status is to predict the content of dissolved gas in oil. Before optimizing the parameters <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> of the improved grey prediction model GM(1,1,B), this article determines that the average relative error of the model fitting is the objective function. It is stipulated that the search space for the optimal solutions of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> are both [0~1], and the hybrid algorithm of genetic algorithm and particle swarm optimization is used to optimize the model parameters <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> . By analyzing the characteristics of different types of abnormal values in transformer online monitoring data such as oil chromatogram and oil temperature, a fast analysis and detection method of online monitoring data stream based on multivariate time series and correlation analysis is proposed. For multi-dimensional monitoring data, from the perspective of data association and time series analysis, a sliding time window is used to record the occurrence time and type of abnormal points, establish a judgment model for candidate abnormal data sets, and use clustering algorithms to analyze candidate abnormalities. The data collection performs comprehensive abnormality judgment of multi-dimensional data. Experiments show that this method can detect abnormal operating states in online monitoring data streams in real time, and has high application value.

Online Monitoring Technology of Power Transformer based on Vibration Analysis

Abstract This paper presents a method for the study of the influence of stability of a power transformer on the power system based on the vibration principle. Traditionally, the EMD and EEMD algorithms are employed to test the box vibration signal data of the power transformer under three working conditions. The proposed method utilizes a partial EMD screening along with MPEEMD method for the online monitoring of power transformer. A complete online monitoring system is designed by using the STM32 processor and LabVIEW system. The proposed system is compared with EMD and EEMD algorithms in terms of the number of IMFs obtained by decomposition, maximum correlation coefficient, and mean square error. The inherent mode correlation, when compared with the mean square error of the reconstructed signal, shows that the reconstruction error of MPEEMD algorithm is 4.762×10−15 which is better than the traditional EMD algorithm. It is observed from the results that the proposed method outperforms both EMD and EEMD algorithms.