Eigenvalue Analysis for Investigation of Tilting of Transformer Winding Conductors Under Axial Short-Circuit Forces
TL;DR: In this article, a state-space approach has been used to determine the eigenvalues of various winding configurations used in practice by assuming a small value of tilt angle up to 10°.
Abstract: The tilting phenomenon may lead to a catastrophic failure, such as an interturn fault in transformers, under the action of axial short-circuit electromagnetic forces. In this paper, first, a state-space approach has been used to determine the eigenvalues of various winding configurations used in practice by assuming a small value of tilt angle up to 10°. These eigenvalues decide the critical tilting forces and the natural frequencies of windings. The four cases involving disk, layer, helical windings (all these with strip conductor), and layer winding with continuously transposed cable conductor have been analyzed. Further, initial critical tilt angles are determined for various magnitudes of the axial force in a typical 5-MVA transformer.
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TL;DR: The presented approach is effective and time-saving in terms of fault diagnosis for transformer winding and core and shows satisfactory performance in learning robust and discriminative features from measured signals.
Abstract: This paper introduces a novel fault diagnosis approach for transformer based on self-powered radio-frequency identification (RFID) sensor and deep learning technique. The exploited RFID sensor tag with functionalities of signal collection, data storage, and wireless transmission employs surrounding electromagnetic field as power source. A customized power management circuit, including ac–dc converter, supercapacitor, and its corresponding charging circuit, is presented to guarantee constant dc power for the sensor tag. The measured vibration signal contains miscellaneous noises and is characterized as nonlinearity and nonstationarity, so it is difficult to extract robust and useful features by using traditional feature extraction approaches. As one of the deep learning techniques, stacked denoising autoencoder (SDA) shows satisfactory performance in learning robust features from complex signal. Hence, in this paper, SDA approach is employed to learn robust and discriminative features from measured signals. The experimental results show that the presented power supply can generate 2.5-V dc voltage, which is the rated operating voltage for the rest of the sensor tag. The developed sensor tag can achieve a reliable communication distance of 17.3 m in the test environment. Furthermore, the SDA approach shows satisfactory performance in learning robust and discriminative features. Experimental results indicate that the presented approach is effective and time-saving in terms of fault diagnosis for transformer winding and core.
28 citations
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TL;DR: In this article, a hyperboloid method is proposed for the 3D positioning of transformer winding radial deformation location using the ultrawideband radar measurement results and is based on locus of objects in the space.
Abstract: Online monitoring of the transformer winding using electromagnetic waves has recently been proposed for the detection of mechanical defects of transformer winding. In this paper, a new method, which uses the ultrawideband radar measurement results and is based on locus of objects in the space, is proposed for the 3-D positioning of transformer winding radial deformation location. The proposed experimental setup for this method named hyperboloid method is modeled using Computer Simulation Technology software. In this paper, Vivaldi antennas suitable for measurements in environments with multipath, are used and the analysis is performed in the time domain. The simulation results show that the exact 3-D location of the radial deformation can be detected with a good accuracy.
18 citations
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TL;DR: A machine learning-based methods for developing PHM models from sensor data to perform fault diagnostic for transformer systems in a smart grid and demonstrates that the developed meta heuristic algorithm for optimizing the parameters of the neural network is effective and useful.
Abstract: An emerging prognostic and health management (PHM) technology has recently attracted a great deal of attention from academies, industries, and governments. The need for higher equipment availability and lower maintenance cost is driving the development and integration of prognostic and health management systems. PHM models depend on the smart sensors and data generated from sensors. This paper proposed a machine learning-based methods for developing PHM models from sensor data to perform fault diagnostic for transformer systems in a smart grid. In particular, we apply the Cuckoo Search (CS) algorithm to optimize the Back-propagation (BP) neural network in order to build high performance fault diagnostics models. The models were developed using sensor data called dissolved gas data in oil of the power transformer. We validated the models using real sensor data collected from power transformers in China. The results demonstrate that the developed meta heuristic algorithm for optimizing the parameters of the neural network is effective and useful; and machine learning-based models significantly improved the performance and accuracy of fault diagnosis/detection for power transformer PHM.
15 citations
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TL;DR: A novel hybrid fault diagnosis method for power transformer employing solar-powered radio-frequency identification sensor for transformer vibration signal acquisition and deep belief network (DBN) for feature extraction achieves remarkable results in feature extraction for the hybrid fault signal and achieves high diagnosis accuracy.
Abstract: This paper introduces a novel hybrid fault diagnosis method for power transformer. This method employs solar-powered radio-frequency identification (RFID) sensor for transformer vibration signal acquisition and deep belief network (DBN) for feature extraction. The customized RFID sensor employs solar panel as a power source, and a supercapacitor is adopted to be the stand-by power when the solar panel cannot work. A charging circuit is exploited to guarantee constant DC output voltage. The collected hybrid faults signal is characterized as nonlinear and nonstationary; moreover, it contains abundant noises and harmonic components, which makes it difficult to acquire succinct and robust features from the raw signals. Hence, the DBN is adopted to extract features from the collected vibration signal. In order to obtain optimum feature extraction performance, the quantum particle swarm optimization algorithm (QPSO) is employed to determine the hidden layer structure and learning rate of the DBN model. The experiments indicate that the proposed RFID sensor is able to realize reliable data acquisition and transmission. Besides, the optimized DBN achieves remarkable results in feature extraction for the hybrid fault signal and achieves high diagnosis accuracy.
12 citations
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TL;DR: In this article , a strong coupled magnetic-structural model is proposed by using the analysis method, where the independent variables are the axial position of the disks, and the accuracy of the strong coupling model is verified by the iterative method.
Abstract: When an external short circuit occurs, the axial electromagnetic force increases dozens of times. Under the effect of the axial short-circuit electromagnetic force, the transformer windings vibrate violently. The spatial distribution of the disks is constantly changing during vibration, which can change the temporal and spatial distributions of the leakage magnetic field and the axial electromagnetic force. This process is called the strong coupling phenomenon of the structural field and the leakage magnetic field. In this article, a strong coupled magnetic-structural model is proposed by using the analysis method. The strong coupling equations, where the independent variables are the axial position of the disks, are obtained. The vibration process with strong coupling phenomenon considered can be obtained. The accuracy of the strong coupling model is verified by the iterative method. By comparing the results calculated by the strong coupling model with those calculated by the weak coupling model, the influence of the strong coupling phenomenon on the vibration process is obtained. Due to the influence of the strong coupling phenomenon, the winding vibration intensity increases and the maximum displacement of the disks can be doubled, which implied that the strong coupling phenomenon cannot be ignored when investigating the short-circuit strength of power transformers.
9 citations
References
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01 Jan 2004
TL;DR: In this article, a reference illustrates the interaction and operation of transformer and system components and spans more than two decades of technological advancement to provide an updated perspective on the increasing demands and requirements of the modern transformer industry.
Abstract: This reference illustrates the interaction and operation of transformer and system components and spans more than two decades of technological advancement to provide an updated perspective on the increasing demands and requirements of the modern transformer industry. Guiding engineers through everyday design challenges and difficulties such as stray loss estimation and control, prediction of winding hot spots, and calculation of various stress levels and performance figures, the book propagates the use of advanced computational tools for the optimization and quality enhancement of power system transformers and encompasses every key aspect of transformer function, design, and engineering.
396 citations
"Eigenvalue Analysis for Investigati..." refers background in this paper
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TL;DR: In this article, Rabins' method for measuring the self-inductance of and mutual inductance between two-winding transformers was used to calculate the B-field forces.
Abstract: Introduction Historical Background Uses in Power Systems Core-Form and Shell-Form Transformers Stacked and Wound Core Construction Transformer Cooling Winding Types Insulation Structures Structural Elements Modern Trends Magnetism and Related Core Issues Basic Magnetism Hysteresis Magnetic Circuits Inrush Current Distinguishing Inrush from Fault Current Optimal Core Stacking Circuit Model of a Two-Winding Transformer with Core Circuit Model of the Core Two-Winding Transformer Circuit Model with Core Approximate Two-Winding Transformer Circuit Model without Core Vector Diagram of a Loaded Transformer with Core Per-Unit System Voltage Regulation Reactance and Leakage Reactance Calculations General Method for Determining Inductances and Mutual Inductances Two-Winding Leakage Reactance Formula Ideal Two-, Three-, and Multiwinding Transformers Leakage Reactance for Two-Winding Transformers Based on Circuit Parameters Leakage Reactances for Three-Winding Transformers Phasors, Three-Phase Connections, and Symmetrical Components Phasors Wye and Delta Three-Phase Connections Zig-Zag Connection Scott Connection Symmetrical Components Fault Current Analysis Fault Current Analysis on Three-Phase Systems Fault Currents for Transformers with Two Terminals per Phase Fault Currents for Transformers with Three Terminals per Phase Asymmetry Factor Phase-Shifting and Zig-Zag Transformers Basic Principles Squashed Delta Phase-Shifting Transformer Standard Delta Phase-Shifting Transformer Two-Core Phase-Shifting Transformer Regulation Effects Fault Current Analysis Zig-Zag Transformer Multi-terminal Three-Phase Transformer Model Theory Transformers with Winding Connections within a Phase Multiphase Transformers Generalizing the Model Regulation and Terminal Impedances Multiterminal Transformer Model for Balanced and Unbalanced Load Conditions Rabins' Method for Calculating Leakage Fields, Leakage Inductances, and Forces in Transformers Theory Rabins' Formula for Leakage Reactance Application of Rabins' Method to Calculate the Self-Inductance of and Mutual Inductance between Coil Sections Determining the B-Field Determination of Winding Forces Numerical Considerations Mechanical Design Force Calculations Stress Analysis Radial Buckling Strength Stress Distribution in a Composite Wire-Paper Winding Section Additional Mechanical Considerations Electric Field Calculations Simple Geometries Electric Field Calculations Using Conformal Mapping Finite Element Electric Field Calculations Capacitance Calculations Distributive Capacitance along a Winding or Disk Stein's Disk Capacitance Formula General Disk Capacitance Formula Coil Grounded at One End with Grounded Cylinders on Either Side Static Ring on One Side of Disk Terminal Disk without a Static Ring Capacitance Matrix Two Static Rings Static Ring Between the First Two Disks Winding Disk Capacitances with Wound-in Shields Multistart Winding Capacitance Voltage Breakdown and High-Voltage Design Principles of Voltage Breakdown Geometric Dependence of Transformer-Oil Breakdown Insulation Coordination Continuum Model of Winding Used to Obtain the Impulse-Voltage Distribution Lumped-Parameter Model for Transient Voltage Distribution Losses No-Load or Core Losses Load Losses Tank and Shield Losses Due to Nearby Busbars Tank Losses Associated with the Bushings Thermal Design Thermal Model of a Disk Coil with Directed Oil Flow Thermal Model for Coils without Directed Oil Flow Radiator Thermal Model Tank Cooling Oil Mixing in the Tank Time Dependence Pumped Flow Comparison with Test Results Determining m and n Exponents Loss of Life Calculation Cable and Lead Temperature Calculation Tank Wall Temperature Calculation Tieplate Temperature Core Steel Temperature Calculation Load Tap Changers General Description of Load Tap Changer Types of Regulation Principles of Operation Connection Schemes General Maintenance Miscellaneous Topics Setting the Impulse Test Generator to Achieve Close to Ideal Waveshapes Impulse or Lightning Strike on a Transformer through a Length of Cable Air Core Inductance Electrical Contacts References Index
147 citations
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