Sweep Frequency Response Analysis Research Articles

Testing and diagnostic out of the service power transformer using a sweep frequency response analysis (SFRA) comparing with China standard

Testing and diagnostic out of the service power transformer using a sweep frequency response analysis (SFRA) comparing with China standard

Machine learning for power transformer SFRA based fault detection

This paper presents machine learning methods for health assessment of power transformer based on sweep frequency response analysis. The paper presents an overview of monitoring and diagnostics based on statistical Sweep Frequency Response Analysis (SFRA) based indicators that are used to evaluate the state of the power transformer. Experimental data obtained from power transformers with internal short-circuit faults is used as a database for applying machine learning. Machine learning is implemented to achieve more precise asset management and condition-based maintenance. Unsupervised machine learning was applied through the k-means cluster method for classifying and dividing the examined power transformer state into groups with similar state and probability of failure. Artificial neural network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) as part of supervised machine learning are created in order to detect fault severity in tested power transformers of different lifetime. The presented machine learning methods can be used to improve health assessment of power transformers.

Deformations in Power Transformers by Sweep Frequency Response Analysis and a Sample Field Study

Power transformers are expensive pieces of electrical equipment that are used to connect transmission and distribution networks. Power transformers are inspected and tested on a regular basis throughout production, shipment, installation, and operation. There are several ways for determining the mechanical integrity of power transformers. Power transformers are monitored using these diagnostic procedures, and any breakdowns and issues are avoided. Scanning Frequency Response Analysis (SFRA) is an efficient method for determining the condition of the transformer core and windings. To evaluate the state of the transformer winding, SFRA, winding direct current resistance, and winding capacitance tests were done in this study. In the case study, a comprehensive case analysis was done on the SFRA test findings for a 50/62.5 MVA power transformer, and the results were analyzed. The results reveal that the SFRA test finds structural winding problems in transformers with high accuracy.

Combination of Non-contact and Contact Measuring Methods for Analyzing Structural Conditions of Dry Transformers

Abstract The article describes the non-contact and contact analysis of 1-MVA dry power transformers with epoxy-resin insulation using an acoustic camera and frequency analyzer with automatic sweeping for low-middle frequency areas. Power transformers are most commonly used for construction component (core, windings, taps) analysis. The electrical, non-rotating machine generates electromagnetic and acoustic emissions that can be used to analyze dry transformers during their operation. Non-contact online diagnostic methods have many advantages over offline methods because it is not necessary to shut down the transformer, and also, the condition and behaviour of the machine are analyzed during its normal operation. The article presents the analysis and comparison of structural parts of the distribution dry transformers of the same type and power. The problem of insufficient or incorrect clamp-screw connection was identified using the SFRA (Sweep Frequency Response Analysis) method.

A New NILM System Based on the SFRA Technique and Machine Learning.

In traditional nonintrusive load monitoring (NILM) systems, the measurement device is installed upstream of an electrical system to acquire the total aggregate absorbed power and derive the powers absorbed by the individual electrical loads. Knowing the energy consumption related to each load makes the user aware and capable of identifying malfunctioning or less-efficient loads in order to reduce consumption through appropriate corrective actions. To meet the feedback needs of modern home, energy, and assisted environment management systems, the nonintrusive monitoring of the power status (ON or OFF) of a load is often required, regardless of the information associated with its consumption. This parameter is not easy to obtain from common NILM systems. This article proposes an inexpensive and easy-to-install monitoring system capable of providing information on the status of the various loads powered by an electrical system. The proposed technique involves the processing of the traces obtained by a measurement system based on Sweep Frequency Response Analysis (SFRA) through a Support Vector Machine (SVM) algorithm. The overall accuracy of the system in its final configuration is between 94% and 99%, depending on the amount of data used for training. Numerous tests have been conducted on many loads with different characteristics. The positive results obtained are illustrated and commented on.

An Analytical Approach to Locate Short Circuit Fault in a Cable Using Sweep Frequency Response Analysis

In this paper, analytical methods are proposed for the location of a short circuit fault in a power cable using sweep frequency response analysis (SFRA). The impedance spectroscopy is estimated using classical transmission line (TL) theory whereas the driving point impedance function (DPIF) of the cable is proposed to be constructed either by using experimental SFRA data or analytically from the knowledge of the frequency-dependent parameters, material properties, and dimensions of the cable. Finally, an analytical formula is proposed for the estimation of the location of a short-circuit fault using the location of poles and zeros of the impedance function before and after the fault. The formulae are validated using FEM software simulations as well as experimentally on a test cable.

Effect of Test Cable Termination on Frequency Response of Transformer Winding

Sweep Frequency Response Analysis (SFRA) method is the most powerful tool to predict the condition of transformer winding. The reliable measurement of frequency response is equally important as its interpretation. A few standards (IEEE std. C57.149-2012, IEC 60076, the Chinese Electrical Power Industry Standard ICS27.100.F24-2005) & much research work have been published, stating dos & don’ts while measuring the frequency response of transformer winding. In this paper, an attempt is made to introduce an additional factor affecting frequency response, while doing measurements. Here, the effect of test cable termination at transformer winding terminals, to prevent signal reflection, on frequency response is presented. A possible way to select suitable value/s of terminating resistance that can correct frequency response is also proposed.

Towards Unique Circuit Synthesis of Power Transformer Winding Using Gradient and Population Based Methods

The aim of the paper is to synthesize a nearly unique, physically realizable and mutually coupled ladder circuit representation of a two-winding transformer by identifying the most accurate and reliable optimization technique such that prior knowledge in selecting the initial guess and search space is avoided. To this end, magnitude and phase of the driving-point impedance function are captured by performing sweep frequency response analysis (SFRA). Then, two widely used population-based algorithms namely, particle swarm optimization (PSO) and artificial bee colony (ABC) and one gradient-based sequential quadratic programming (SQP) algorithm are implemented. Two case studies are considered namely, i) 15 MVA, 66/11.55 kV transformer and, ii) 111 kVA, 7.33/1.22 kV transformer. The performance of the aforementioned algorithms is compared using three evaluation parameters namely, repeatability, accuracy and reliability

Online SFRA for Reliability of Power Systems: Characterization of a Batch of Healthy and Damaged Induction Motors for Predictive Maintenance

Asynchronous motors represent a large percentage of motors used in the electrical industry. Suitable predictive maintenance techniques are strongly required when these motors are critical in their operations. Continuous non-invasive monitoring techniques can be investigated to avoid the disconnection of the motors under test and service interruption. This paper proposes an innovative predictive monitoring system based on the online sweep frequency response analysis (SFRA) technique. The testing system applies variable frequency sinusoidal signals to the motors and then acquires and processes the applied and response signals in the frequency domain. In the literature, SFRA has been applied to power transformers and electric motors switched off and disconnected from the main grid. The approach described in this work is innovative. Coupling circuits allow for the injection and acquisition of the signals, while grids feed the motors. A comparison between the transfer functions (TFs) of healthy motors and those with slight damage was performed with a batch of 1.5 kW, four-pole induction motors to investigate the technique's performance. The results show that the online SFRA could be of interest for monitoring induction motors' health conditions, especially for mission-critical and safety-critical applications. The overall cost of the whole testing system, including the coupling filters and cables, is less than EUR 400.

Synchronous Salient Poles Fault Localization by SFRA and Fault Diagram Method

Sweep Frequency Response Analysis (SFRA) is an extended and non-invasive technique used for transformer condition assessment. In the last years, its proven sensitivity and ease of testing have generated interest for rotating machine applications. Since SFRA measurements are curves plotted on Bode diagrams, the failures can be detected by comparison between a reference curve (performed at healthy conditions), and a curve obtained after an incident. However, the interpretation of results is a hard task because the usual way of interpreting the results still being by visual inspection, then, extensive knowledge and appropriate experience are required. To solve it, in previous publications, the authors developed a method called “Fault Diagram” to analyze SFRA results based on fitting the measurement with an equivalent three-variable circuit. This paper presents several improvements in the method based on changing the equivalent circuit, the optimization algorithm and the frequency range analyzed. Furthermore, the paper provides experimental results of the method's application in a 40 MVA synchronous machine pole. SFRA results presentation into a 3D plot provide information about the fault type (ground or inter-turn fault), location, number of faulty turns and fault resistance with more defined tendencies than in previous research.

Approach for Identification of Inter-Turn Fault Location in Transformer Windings Using Sweep Frequency Response Analysis

As inter-turn faults in transformer windings are the most severe, their delayed detection and localization may create extensive damage to the winding. Therefore, a new inter-turn fault localization methodology based on sweep frequency response analysis (SFRA) through calculation of Pre-fault Inter-Turn Fault Factor ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ITFF_pre</i> ) for all possible fault locations by estimating the values of series and shunt capacitance of the windings during healthy conditions is presented. For a faulty transformer, Post-fault Inter-Turn Fault Factor ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ITFF_post</i> ) is calculated by determining the value of equivalent capacitance using SFRA. The exact fault location in the faulty transformer is obtained by comparing the single calculated value of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ITFF_post</i> of the faulty transformer with the values stored in the lookup table. The efficacy of the suggested approach is verified by performing faults at various locations on the transformer model developed in the laboratory. Its authenticity is also verified on an existing 400 kVA, 11 kV/440 V distribution transformer installed in the real field. The results indicate that the suggested method is capable to identify inter-turn fault location with the minimum ladder network parameters. Finally, a relative assessment of the suggested approach with numerous other methods confirms the superiority in terms of inter-turn fault localization.

Effects of power transformer high-frequency equivalent circuit parameters non-uniformity on fault diagnosis using SFRA test

The electric power transformer is an essential component of an electrical power system because it regulates the voltage level to provide the best possible system operation. Thus, it is important to detect the transformer faults to maintain its reliability as high as possible. The sweep frequency response analysis (SFRA) test is one of the tests which, used to diagnose transformer faults. It determines the fault type by analyzing its response across a wide frequency range. The main advantage of the SFRA test is the detection of mechanical deformation faults since mechanical deformation faults cause the high-frequency equivalent circuit parameters to change which, could be detected easily using the SFRA test. Also, statistical features are defined for this SFRA response used in fault detection and classification. The transformer high-frequency equivalent circuit parameters are non-uniform distributed over discs due to normal leakages and aging. Thus, in the present paper, the non-uniform distribution of the transformer high-frequency equivalent circuit is studied in the SFRA test at different faults and various non-uniformity levels. Finally, when considering the non-uniform distribution of high-frequency equivalent circuit parameters, the results demonstrate that some features are greatly affected, and others are just minimally affected. Depending on how much each feature is affected by the uniformity of high-frequency equivalent circuit parameters, rules of fault diagnosis algorithms based on the SFRA test could be reweighted to improve their accuracy. Also, the obtained results could be compared to real measured data to estimate the actual non-uniformities of transformer parameters.

Objective Interpretation of SFRA, in the Light of CIGRE TB 812

The SFRA (Sweep Frequency Response Analysis) technique has become quite popular as a condition monitoring technique for transformers. The SFRA technique holds a vast repertoire of data in terms of the electromagnetic behaviour of the transformer. Much research has happened in comprehending this data but no effort has succeeded in determining a universal objective criterion for the interpretation of SFRA data. CIGRE study committee A2.53 has recently released Technical Brochure 812 which has been the result of nearly half a decade of international efforts in arriving at an objective criterion for the interpretation of SFRA. This paper discusses the state of the art in objective interpretation of SFRA data with special reference to TB 812. The paper also presents a case study in HPL where SFRA was compared to a conventional method.

머신러닝 기법을 이용한 변압기 주파수 응답 모델 파라미터 추정

Examining power transformer faults is crucial for maintaining the reliability of the power system. The most popular methods for detecting power transformer fault include thermal analysis, vibration analysis, partial discharge analysis, dissolved gas analysis(DGA), and sweep frequency response analysis(SFRA). Especially, the SFRA test is examined to detect transformer internal fault such as winding fault. Simulation-level frequency response analysis enables inspection of the power transformer before connecting to the grid. This paper proposes a parameter estimation method using machine learning for the power transformer frequency response equivalent model.

Fault detection in a power transformer based on reverberation time

The power transformer is one of the vital elements of electric power systems. The reliability of power transformer operation is directly related to its condition. Detection of winding deformations is possible by means of sweep frequency response analysis. Interpretation of SFRA results is usually performed by visualization of images generated using specialized devices and comparison of transfer functions obtained from successive tests. This diagnostic method is known as a fingerprint and its disadvantage is the subjectivity and relying on engineer’s experience. This paper presents an experimental approach for detection of internal transformer short-circuit faults. Existing mathematical indicators based on frequency response analysis are often unable to unambiguously indicate the degree of fault and their determination requires high-cost measurement and signal processing equipment. A new indicator based on the reverberation time, commonly employed in acoustics, is proposed instead. The proposed parameter is based on time-domain calculation and enables reliable detection of transformer inter-turn short circuits and accurate estimation of their degree. A significant advantage of the proposed approach is its low dependence on the frequency range of the measured signal, which allows employment of low-cost measuring and signal processing devices.

Experimental results of electromechanical structure properties of noise and vibration on power transformers after lightning impulse test

Transformer damage due to lightning impulse wave may result in serious loss of profits, thereby compromising the reliability of power systems. Currently, there is limited studies on the effect of lightning impulse on the noise and vibration of power transformers. In line with this, this study proposed a single-phase transformer with a voltage specification of 345 kV (high voltage)/115 kV (low voltage) and a capacity of 200 MVA. The soft magnetic 27PH100 material with a low magnetostriction variation was selected as the core material for the design of a transformer with a significantly low noise and vibration that could be maintained even after repeated lightning impulse applications. First, the parameters of the core and coil with k-factor design of the transformer, which can withstand high electrical signals, such as lightning impulses and insulation damage, was established and tested. The deformation and modal shape of the core and winding were simulated by finite element analysis. Furthermore, sweep frequency response analysis was used to observe the structure of the core and coil without any deformation. Diagnosis gas analysis was adopted to analyze the oil in the tank of the transformer to avoid dielectric loss due to insulation damage. Temperature rise test was also used to verify the stability of the transformer performance under no-load and full-load conditions. Finally, the results of the continuous lightning impulse tests revealed that the transformer operated under a stable performance, while generating significantly low noise (<65 dB) and vibration (<10 μm), which are remarkably lower than the IEEE standard of 85 dB.

High-Frequency Modeling of a Three-Winding Power Transformer Using Sweep Frequency Response Analysis

A power transformer is an essential device for stable and reliable power transfer to customers. Therefore, accurate modeling of transformers is required for simulation-based analysis with the model. The paper proposes an efficient and straightforward parameter estimation of power transformers based on sweep frequency response analysis (SFRA) test data. The method first develops a transformer model consisting of repetitive RLC sections and mutual inductances and then aligns the simulated SFRA curve with the measured one by adjusting parameters. Note that this adjustment is based on individual parameter impacts on the SFRA curve. After aligning the two curves, the final transformer model can be obtained. In this paper, actual single-phase, three-winding transformer model parameters were estimated based on field SFRA data, showing that SFRA curves simulated from the estimated model are consistent with the measured data.

SFRA Test Method Application for Turbo Generator Rotor Winding

The development of technology in the present makes the testing method for analysis to know the defect to insulation winding in rotating machines is varied. There are many specific testing tools to determine the damage. The authors of this paper conduct a preliminary study using sweep frequency response analysis (SFRA) to detect and find damage in the turbo generator rotor winding. SFRA is a test equipment performed on transformers based on the working principle of RLC which are Resistive, Inductive, and Capacitive. The importance of the damage identification process is needed to maintain reliable rotor generator and avoid catastrophic defect to equipment and losses to power plant. In one case study, the rotor generator experienced high vibration indication when operating in high reactive power and the SFRA test results on the rotor winding shows a mismatch between two curves. In another case, a mismatch of SFRA result showed winding deformation, such as coil elongation, without high vibration indication in rotor generator. After inspection, repair, measurement and testing, the SFRA method proves that it can show indications of defect without major dismantling.

Failure Analysis of Power Transformer Based on Transformer Turn Ratio Test and SFRA

A power transformer is one of the most key components of energy transmission and distribution in an electric power system. Inter-turn disturbances or short circuit faults may occur because of insulation degradation between one or more successive turns of the windings. If the fault is not detected early, it may spread to the nearest winding turn and causes permanent damage to the winding. Therefore, it is necessary to detect inter-turn faults to prevent catastrophic failure of the transformer. In this study, failure analysis of a transformer that approaching the catastrophic stage is conducted using sweep frequency response analysis (SFRA) and transformer turns ratio (TTR) testing methods. Correlation between SFRA & TTR tests is analyzed. TTR test before rewinding is > 0.5%, while after rewinding is < 0.5%. 400 VAC voltage is applied, and the output voltage is measured. SFRA and TTR results show that there is a linear correlation for all phases. It can be concluded in this study that the TTR test can be used to detect transformer failure.

Normograms preparation and interpretation for 400/ 800 kV class of converter transformers/reactors with and without oil using sweep frequency response analysis ( SFRA )

Normograms preparation and interpretation for 400/ <scp>800 kV</scp> class of converter transformers/reactors with and without oil using sweep frequency response analysis ( <scp>SFRA</scp> )