Transformer Vibration Research Articles

Research on a Transformer Vibration Fault Diagnosis Method Based on Time-Shift Multiscale Increment Entropy and CatBoost.

A mechanical vibration fault diagnosis is a key means of ensuring the safe and stable operation of transformers. To achieve an accurate diagnosis of transformer vibration faults, this paper proposes a novel fault diagnosis method based on time-shift multiscale increment entropy (TSMIE) combined with CatBoost. Firstly, inspired by the concept of a time shift, TSMIE was proposed. TSMIE effectively solves the problem of the information loss caused by the coarse-graining process of traditional multiscale entropy. Secondly, the TSMIE of transformer vibration signals under different operating conditions was extracted as fault features. Finally, the features were sent into the CatBoost model for pattern recognition. Compared with different models, the simulation and experimental results showed that the proposed model had a higher diagnostic accuracy and stability, and this provides a new tool for transformer vibration fault diagnoses.

Distribution transformer mechanical faults diagnosis method incorporating cross-domain feature extraction and recognition of unknown-type faults

The complexity and uncertainty of vibration signals from distribution transformers pose significant challenges for diagnosing mechanical faults. To address this, this paper proposes a novel fault diagnosis model for distribution transformers, which combines a cross-domain fusion multi-scale convolutional autoencoder (CFMS-CAE) with an open-set domain adaptation classifier (OSDA-C). Specifically, in order to extract more comprehensive features, a convolutional autoencoder (CAE) model based on multi-output objectives is constructed to extract the time-frequency domain characteristics of transformer vibration signals. Multiple-scale convolutional layers are incorporated into the convolutional autoencoder to enable multi-range feature extraction. Additionally, parameter optimization is achieved using the crayfish optimization algorithm (COA). Subsequently, an open-set domain adaptation module is integrated into the convolutional neural network classifier to establish boundaries for each category and facilitate the identification of transformer fault categories, including unknown-type faults. The experimental results demonstrate that the proposed method is effective for fault identification in both dry-type and oil-immersed transformers, with average accuracy reaching 99.35% and 99.62%, respectively. For unknown-type faults, the accuracy also achieved 100% and 97.5%, respectively.

Assessment of the Winding Mechanical Condition Based on Transformer Vibration during Transient Processes

Assessment of the Winding Mechanical Condition Based on Transformer Vibration during Transient Processes

Vibration characteristics of a single‐phase four‐column auto‐transformer core excited by geomagnetically induced currents

AbstractDuring geomagnetic storms, geomagnetically induced currents (GICs) occur in the power grid. GIC is a quasi‐direct current, which is represented by using a fixed DC current, the authors propose an approach that combines field‐circuit‐coupled and weakly coupled magneto‐mechanical models to investigate the magnetostriction characteristics of single‐phase, four‐column autotransformer cores excited by GICs. A non‐linear magnetostrictive constitutive model that considers the impact of stress and magnetisation intensity on ferromagnetic materials is introduced. The model reveals that the strain in the core initially increases as the magnetisation intensity increases; however, when the core begins to saturate, the magnetostrictive strain weakens as the magnetisation intensity increases. Moreover, the results of multiphysics finite‐element modelling are experimentally verified using a direct current bias platform. The results show that when excited by GICs, there is an enhancement in the vibration of the iron core, and the harmonic content of the vibration acceleration increases considerably, with the odd harmonics most significantly increasing in amplitude. The conclusion drawn is that GICs can exacerbate transformer vibration and significantly increase the content of harmonics, especially odd harmonics.

Electromagnetic Vibration and Noise Analysis of Linear Phase-Shifting Transformer

The advantages of adjustable angle phase-shifting and great expansibility make the linear phase-shifting transformer a novel type of power conversion device with a wide range of potential applications. However, during the procedure, there is a lot of noise. For the purposes of transformer design and vibration and noise reduction, it is crucial to investigate its electromagnetic vibration and noise. In this paper, the radial electromagnetic force wave considering the influence of the end effect as the source of the noise of the linear phase-shifting transformer was deduced and calculated. Based on this, the spectrum and space–time properties of the radial electromagnetic force waves were simulated and verified. Additionally, a finite element model was created using the Ansys Workbench 2022R1 platform to study the electromagnetic vibration and noise of the linear phase-shifting transformer. A joint simulation of the electromagnetic, structural, and sound fields was then performed. First, the transformer’s natural frequency was determined by modal analysis. After that, the transformer’s structure and the results of the transient electromagnetic field computation were combined and a harmonic response analysis was conducted to determine the vibration acceleration spectrum. Finally, in order to solve the sound pressure field, the transformer’s boundary vibration acceleration was coupled to the air domain. Furthermore, an analysis was conducted to determine the noise distribution surrounding the linear phase-shifting transformer. The joint simulation findings demonstrate that the linear phase-shifting transformer’s resonance, which produces larger electromagnetic vibration and noise, is indeed caused by the radial electromagnetic force. Simultaneously, the impact of the LPST core’s fixed components on the electromagnetic vibration and noise of the core was examined.

A Review of Power Transformer Vibration and Noise Caused by Silicon Steel Magnetostriction

As an important hub equipment in the power system, the power transformer has increasingly higher requirements for its reliability and safety with the rapid development of the power industry. However, the vibrations and noise generated during its operation not only cause a series of issues such as loose windings, core wear, and fatigue of fasteners, but also lead to anxiety and unease among nearby residents, severely affecting their physical and mental health. Therefore, the generation mechanism and control measures of vibration and noise in power transformers have attracted extensive attention from scholars. The magnetostrictive effect of silicon steel sheets is one of the important factors in the causes of transformer vibration and noise. This paper focuses on the magnetostrictive effect of silicon steel sheets. Firstly, the micro mechanism of magnetostriction is introduced. Secondly, based on current research status, the vibration and noise mechanism caused by magnetostriction are summarized, and the influencing factors, measurement, modeling, and noise reduction methods based on magnetostriction are summarized. Finally, based on the current research status and future development trends, prospects are proposed, aiming to provide references for subsequent research on transformer vibration and noise.

Transformer Fault Diagnosis Method Based on SCA-VMD and Improved GoogLeNet

Aiming at the influence of the fundamental frequency and its harmonics in transformer vibration signals on fault signals, which cause a low fault identification rate and degradation of classification model performance, a new strategy is proposed for fault diagnosis using periodic map spectrum feature maps as input features. In this study, the optimal decomposition parameters were first found adaptively using the VMD improved by the positive cosine optimisation algorithm; then, the transformer vibration signal was modally decomposed, and the periodic map spectrum features were plotted according to the differences in the energy distribution of the different modal components at different frequencies. Finally, the GoogLeNet classification model with the improved attention mechanism assigned different weights to the feature maps in both spatial and channel dimensions to improve the classification accuracy and achieve transformer fault diagnosis. The experimental results verified the validity of the adopted feature map and the proposed model, and the accuracy was significantly improved to 99.04% compared with the traditional GoogLeNet, which is valuable for engineering applications.

Research on electromagnetic vibration energy harvester for cloud-edge-end collaborative architecture in power grid

With the deepening of the construction of the new type power system, the grid has become increasingly complex, and its safe and stable operation is facing more challenges. In order to improve the quality and efficiency of power grid management, State Grid Corporation continues to promote the digital transformation of the grid, proposing concepts such as cloud-edge-end collaborative architecture and power Internet of Things, for which comprehensive sensing of the grid is an important foundation. Power equipment is widely distributed and has a wide variety of types, and online monitoring of them involves the deployment and application of a large number of power sensors. However, there are various problems in implementing active power supplies for these sensors, which restrict their service life. In order to collect and utilize the vibration energy widely present in the grid to provide power for sensors, this paper proposes an electromagnetic vibration energy harvester and its design methodology based on a four-straight-beam structure, and carries out a trial production of prototype. The vibration pickup unit of the harvester is composed of polyimide cantilevers, a permanent magnet and a mass-adjusting spacer. The mass-adjusting spacer can control the vibration frequency of the vibration unit to match the target frequency. In this paper, a key novel method is proposed to increase the number of turns in a limited volume by stacking flexible coils, which can boost the output voltage of the energy harvester. A test system is built to conduct a performance test for the prototype harvester. According to the test results, the resonant frequency of the device is 100 Hz, the output peak-to-peak voltage at the resonant frequency is 2.56 V at the acceleration of 1 g, and the maximum output power is around 151.7 mu W. The proposed four-straight-beam electromagnetic vibration energy harvester in this paper has obvious advantages in output voltage and power compared with state-of-the-art harvesters. It can provide sufficient power for various sensors, support the construction of cloud-edge-end architecture and the deployment of a massive number of power sensors. In the last part of this article, a self-powered transformer vibration monitor is presented, demonstrating the practicality of the proposed vibration energy harvester.

Vibration Scale Model of a Converter Transformer Based on the Finite Element and Similarity Principle and Its Preparation

A similarity criterion suitable for studying the vibration characteristics of converter transformers is proposed based on comprehensive consideration of geometric dimensions, electric field, magnetic field, force field, sound field, and coupling field interface interactions. By comparing the magnetic field, stress, displacement, sound field distribution, and vibration characteristics of the scale model of the converter transformer with the initial model, the reliability of the similarity criterion was determined. Based on the vibration similarity criterion of the converter transformer, a prototype of the proportional model was designed and manufactured, and vibration signals under no-load and load conditions were tested. These signals correspond to the vibration signals of the iron core and winding in the finite element model, respectively. Through comparative analysis, the reliability of the prototype and the vibration similarity model of the converter transformer has been proven, which can provide an accurate and effective laboratory research platform for in-depth research on the vibration and noise of the converter transformer and equipment protection.

Transformer Vibration Analysis Based on Double Branch Convolutional Neural Network

The power transformer is one of the important pieces of equipment in the power grid system, and its normal operation is related to the safety and reliability of the whole power system. There are many factors influencing transformer vibration in operation, and its characteristics are complex, so it is difficult to be directly used for transformer state analysis. This paper proposes a method for vibration signal analysis based on a continuous wavelet time-frequency graph. The segmented samples of transformer vibration signals are selected by the time-domain sample segmentation method, and the segmented time sequence samples are transformed by continuous wavelet transform to obtain a two-dimensional time-frequency graph. The time-frequency graph is input into the two-branch convolutional neural network, and the transformer state classification is given based on the features extracted from the network. The simulation analysis on transformer vibration data measured by multiple measuring points shows that the proposed method has an average recognition accuracy of 98.3%. The work in this paper can provide a reference for the vibration analysis of the transformer.

Transformer Vibration Signal Feature Extraction Based on Compressed Sensing and Wavelet Packet

Transformers are important equipment in the power system. Aiming at the collection, transmission, storage, and processing of massive high-dimensional vibration data in the process of transformer vibration online monitoring, this paper proposes a transformer vibration signal feature extraction method based on compressed sensing and wavelet packets. Firstly, the PartHadamard measurement matrix is used to compress the transformer vibration signal, and then the characteristics of the transformer vibration signal are extracted based on the wavelet packet decomposition and normalized wavelet information entropy. The proposed method completes feature extraction on the measured vibration data of the transformer, and three classification algorithms are applied to carry out simulation experiments. The results show that the proposed feature extraction method can effectively extract the vibration characteristics reflecting the operating state of the transformer while greatly reducing the dimension of vibration data, which provides a reference for the online monitoring of transformer vibration.

Transformer fault diagnosis based on probabilistic neural networks combined with vibration and noise characteristics

When the transformer is running, the vibration which is generated in the core and winding will spread outward through the medium of metal, oil, and air. The magnetic field of the core changes with the variation of the transformer excitation source and the state of the core, so the corresponding vibration and noise change. Therefore, the vibration and noise of the transformer contain a lot of information. If the information can be associated with the fault characteristics of the transformer, it is significant to evaluate the running state of the transformer through the vibration and noise signal, which improve the intelligence, safety, and stability of the transformer operation. Based on this, modeling and simulation of transformer multi-point grounding, DC bias, and short-circuit between silicon steel sheets fault are first carried out in this paper, and vibration and noise distribution of transformer under different faults are given. Second, a fault diagnosis method based on transformer vibration and noise characteristics is proposed. In the process of implementation, vibration and noise signals under multi-point grounding, DC bias, and short-circuit between silicon steel sheets are taken as the sample data, and the probabilistic neural network algorithm is used to effectively predict the transformer fault. Finally, the effectiveness of the proposed scheme is verified by identifying the simulation faults-the proposed fault diagnosis method based on PNN can be effectively applied to transformer.

Transformer vibration and noise monitoring system using internet of things

AbstractDuring continuous operation, transformer problems can occur due to various reasons. In reality, the operating parameters of the transformer have been collected and monitored through the supervisory control and data acquisition (SCADA) systems. However, these systems face many challenges when applied to no‐human substations. Currently, noise signals have been used to detect transformer errors. Abnormal noise recognition and vibration monitoring can recognize the transformer's potential defects and errors. In this study, the authors built an internet of things (IoT) system that allows remote control centres to monitor the condition of transformers through noise and vibration at non‐human substations. The proposed model was equipped with a wireless sensor network node consisting of vibration sensors, audio collectors, Arduino modules, and Lora modules. The authors set up two schemes for the IoT network: one sensor node for a 220‐kV transformer and three sensor nodes for all three phases of the 500‐kV transformer. The data obtained from the sensor node were sent to LoRa Gateway and displayed on the computer through LabVIEW. The study also enabled monitoring of parameters through IoT devices such as Desktops, Laptops, Smartphones from LabVIEW NXG Web VI platform, ThingSpeak, and Amazon S3 storage cloud. In addition, a Model Predictive Control (MPC) algorithm was applied to predict the deterioration of transformer health to maintain the system stability and, hence, prolong the transformer life and operability.

Simulation on Vibration Characteristics of Oil Tank of Flexible DC Transformer with Different Foundation

The transformer vibration and noise under the action of voltage and current is a serious problem to the equipment itself and the environment. In this paper, a newly designed and manufactured flexible DC transformer is considered. To reduce vibration and noise of the transformer through proper installation on the foundation, the vibration characteristics of the oil tank with different foundation designs are analysed. The numerical simulation model of the oil tank with two kinds of foundation is established based on the FEA. The convergence of the numerical model is verified through coarse and fine meshes. The vibration characteristics, including the natural frequencies, the modal shapes, and the harmonic responses, are revealed based on the simulation. The present results can provide a foundation of the vibration noise analysis and vibration reduction optimization of the transformer.

Transformer Fault Warning Based on Spectral Clustering and Decision Tree

The insufficient amount of sample data and the uneven distribution of the collected data across faults are key factors limiting the application of machine learning in power transformer fault warning, as demonstrated by the poor adaptability of the established data-driven models under actual operating conditions. In this paper, an unsupervised and supervised learning method is designed for power transformer fault early warning based on electrical quantities and vibration signals. The method is based on the Fourier levels of transformer vibration signals under different electrical conditions measured in the field, and the vibration features are clustered according to their intrinsic properties by means of a spectral clustering algorithm. A decision tree model of the vibration characteristics under each cluster is then constructed to calculate early warning values for the transformer vibration spectrum under different electrical conditions, enabling the assessment of transformer production variability. The above process, which is based on field measurement data and data mining analysis methods, is cheaper than the existing transformer fault warning techniques at home and abroad and makes better use of information and training models.

Numerical analysis and experimental research of vibration and noise characteristics of oil-immersed power transformers

Oil-immersed power transformers play a prominent role in power grid system, and its vibration and noise influence the operation of the power grid. In this paper, an electromagnetic–mechanical-acoustic coupled three-dimensional finite element method of oil-immersed transformer is established. On this basis, according to a noise and vibration experiment of product-level oil-immersed power transformer, it not only proves the effectiveness of the calculation model, but also explores the influences of the radiating grilles and input voltage on the vibration and noise characteristics of the oil immersion transformer quantitatively. Research shows that the main frequency components of transformer vibration and noise focus on 100 Hz and its higher and integer multiple frequencies. The errors of total vibration acceleration level and overall sound pressure levels between the simulation results and test results do not exceed 3 dB, which verify the effectiveness of the numerical method.

Investigation on Low Frequency Bandgap of Coupled Double Beam with Quasi-Zero Stiffness for Power Transformer Vibration Control

To suppress low frequency vibration and noise generated by power transformer in residential area and achieve environment protection standards, a double-beam metamaterial is proposed, which is fabricated through periodically coupling silicon steel sheet and aluminum beams with Belleville quasi-zero stiffness spring (BQZSS). The double-beam metamaterial can be assembled with iron core of transformers as a whole to obtain the design of a low-noise transformer. Performing static analysis, the mechanical model of BQZSS is established and the relationship between restoring force, stiffness, and displacement can be obtained. Then, the mechanical properties of BQZSS are verified with the finite element method. On this basis, the governing equations of the unit cell of the double-beam metamaterial are derived using Euler–Bernoulli beam theory. According to Bloch theorem and boundary conditions, the dispersion relation of the double-beam metamaterial is deduced, and behaviors of flexural wave propagation in beams are investigated. The effects of structure parameters on bandgaps and dispersion properties are studied, and the low frequency vibration bandgap mechanism is revealed. Finally, the frequency response function (FRF) of the double-beam metamaterial with a finite length is calculated in ANSYS to verify the bandgap characteristics given by a theoretical model. The results show that the double-beam metamaterial can yield multiple low frequency bandgaps at 100 Hz, 200 Hz, 300 Hz, and 500 Hz for suppression of flexural vibration components of transformers, which benefits from Bragg scattering (BS) and the blend of BS and LR mechanisms. The opening and closing of the low frequency bandgaps and the attenuation constants within bandgaps can be tuned by choosing parameters of beams. These findings suggest that the coupling between beams can lead to novel dispersion properties, which provides a new control approach for low frequency vibration and noise in power transformer.

Frequency Modulation Approach for High Power Density 100 Hz Piezoelectric Vibration Energy Harvester

Piezoelectric vibration energy harvester (PVEH) is a promising device for sustainable power supply of wireless sensor nodes (WSNs). PVEH is resonant and generates power under constant frequency vibration excitation of mechanical equipment. However, it cannot output high power through off-resonance if it has frequency offset in manufacturing, assembly and use. To address this issue, this paper designs and optimizes a PVEH to harvest power specifically from grid transformer vibration at 100 Hz with high power density of 5.28 μWmm-3g-2. Some resonant frequency modulation methods of PVEH are discussed by theoretical analysis and experiment, such as load impedance, additional mass, glue filling, axial and transverse magnetic force frequency modulation. Finally, efficient energy harvesting of 6.1 V output in 0.0226 g acceleration is tested in grid transformer reactor field application. This research has practical value for the design and optimization process of tunable PVEH for a specific vibration source.

Fault Diagnosis of Transformer Winding Based on VMD-SVM

Considering the nonlinear and non-stationary characteristics of the transformer vibration acceleration signal obtained from the surface of the transformer tank, the variational mode decomposition (VMD) theory is introduced. Simulation analysis shows that the VMD decomposition has obvious advantages over EMD when the needle frequency is similar to the signal. It effectively avoids two types of modal aliasing and over-decomposition, and accurately reflects the characteristics of the source signal. Aiming at the problem that the two core parameters of the support vector machine are difficult to determine, the Pareto particle swarm method is used to perform multi-objective parallel optimization of the two core parameters of the support vector machine to obtain the optimal parameters. The VMD-SVM fault diagnosis model is tested using the transformer instance fault data, and compared with the other two methods. The instance test results show that the VMD-SVM proposed in this paper has the highest diagnostic accuracy and realizes the latent fault of the power transformer winding. accurate diagnosis.

Vibration Measurement and Numerical Modeling Analysis of Transformer Windings and Iron Cores Based on Voltage and Current Harmonics

The operating condition and structural state of the converter transformer are closely related to vibration. Abundant harmonics aggravate the vibration of windings and iron cores, resulting in frequent mechanical structural failures, which seriously affect the stable operation of the power system. Traditional research mainly focuses on the vibration of AC transformers without harmonics and there is no in-depth discussion of the vibration mechanism and the numerical calculation model of windings and iron cores under harmonics. In addition, the influence of harmonics, winding connection method and other factors on the vibration characteristics are not clear. Therefore, this paper analyzes the voltage and current harmonic components and contents, establishes a harmonic-vibration numerical model and compares the vibration time-frequency characteristics with or without harmonics and different valve side winding connections through vibration measurement experiments. Finally, a combined simulation analysis reveals the contribution of the windings and core to the tank. The results show that the tank vibration amplitude and dominant frequency will increase under harmonica and the valve side current will affect the dominant frequency. Among these results, when there are harmonics, the amplitude increases by three times, the vibration dominant frequency changes from 100 Hz to 400 Hz and the frequency spectrum widens to 2000 Hz. In particular, the contribution of the winding vibration under the harmonic current will exceed the iron core. The research results reveal the influence of converter transformer harmonics on vibration, which can provide a theoretical basis for numerical calculation of vibration and monitoring of operating conditions and guide the design of structural vibration reduction to reduce mechanical failures caused by vibration.