Impulse Frequency Response Analysis Research Articles

Understanding IFRA for Detecting Synchronous Machine Winding Short Circuit Faults Based on Image Classification and Smooth Grad-CAM++

Fault diagnosis of the synchronous machine is essential for the safe operation of the modern power system. Recently, impulse frequency response analysis (IFRA) has been used to detect synchronous machine winding short circuit (SC) faults since the failure will significantly alter the IFRA signature of machines. However, there is no standard and reliable code for interpreting the IFRA signatures. It often refers to the frequency response analysis of power transformer standards, the mathematical-statistical indicator of relative factor is directly used to perform the judgment by the standards’ threshold, which might not be appropriate. Besides, the internal mechanism of frequency response analysis diagnosis is unclear. Therefore, this study proposes a technique based on image classification and smooth grade and gradient-based class activation maps (Smooth Grad-CAM++) to understand and interpret the IFRA method. It trains and analyzes the visualization results of the fault data set of a 5 kW synchronous machine’s winding. The experimental results show that the average accuracy of the image classification model based on Resnet18 reaches 99.63%. We performed an IFRA difference analysis according to the visualization results and gave some suggestions about IFRA. The training and detection process can be accelerated based on these suggestions. To illustrate the generalization of the suggestions, another 8 kW synchronous machine is used for the case study, and the experimental results show that these suggestions are still effective. The main contributions of this study are understanding the internal mechanism of IFRA diagnosis from another perspective and providing some conclusions different from previous research results on the IFRA method for the synchronous machine.

Improved Interpretation of Impulse Frequency Response Analysis for Synchronous Machine Using Life long Learning Based on iCaRL

Improved Interpretation of Impulse Frequency Response Analysis for Synchronous Machine Using Life long Learning Based on iCaRL

A condition monitoring and fault detection in the windings of power transformer using impulse frequency response analysis

A condition monitoring and fault detection in the windings of power transformer using impulse frequency response analysis

Evaluation of the Applicability of IFRA for Short Circuit Fault Detection of Stator Windings in Synchronous Machines

A machine is a device to realize the mutual conversion of electric energy and mechanical energy. This work mainly discusses the applicability of impulse frequency response analysis to detect the stator winding short circuit faults. The parameters of excitation waveform and signal sampling were carefully selected. A solid-state nanosecond pulsed power source prototype was developed for resonant excitation of windings. The proposed method was then verified on a 5 kVA, 380V, 2-pole, the salient synchronous machine through artificial fault experiments. Finally, the proposed method was compared with other traditional methods. The method has the advantages of fast measurement, rich high frequencies, high stability, and low cost. Since the principle of impulse frequency response analysis is also applicable to large synchronous machines, this method and device may also be applied to detect large synchronous machines winding short circuit faults. It can be used as an auxiliary and alternative method in the non-destructive fault detection of synchronous machines.

Fault anomaly detection of synchronous machine winding based on isolation forest and impulse frequency response analysis

Synchronous machine is one of the critical power generation parts in the power system. Its stable operation ensures people's normal economic activities. Winding is an essential component of a synchronous machine, and the winding fault is a common fault type. The reliable and efficient fault diagnosis of synchronous machine winding is of great significance to ensure the stability of the power system. Therefore, this paper proposes an anomaly detection method of synchronous machine winding fault based on isolation forest (IF) and impulse frequency response analysis (IFRA). Firstly, the basic principle of the anomaly detection method is introduced, and mathematical-statistical indicators of IFRA signatures used are then explained. Besides, the experimental verification is carried out on a 5 kW synchronous machine, and the performance of the anomaly detection method for winding fault is compared with other conventional methods. The experimental results show that the proposed method is feasible and effective, and the generalization ability of the data is strong. The comparative experimental results show that the proposed method is superior to the existing conventional supervised learning method. It has a shorter calculation time and higher accuracy, with stronger robustness, which is more suitable for the actual data structure.

Diagnostic of transformer winding deformation fault types using continuous wavelet transform of pulse response

Impulse Frequency Response Analysis (IFRA) provides an alternative technique for detection of mechanical type faults in power transformers among researchers. However, the conventional frequency response curve obtained from the time domain transient response only contains the frequency domain information of signal, and the time domain information is neglected. Furthermore, the traditional approach of using the fast Fourier transform (FFT) in processing the non-stationary signal could result in lack of useful information. This paper proposes a novel winding deformation fault diagnostic technique which is modified from IFRA, the detected transient signals of pulse response are processed based on the continuous wavelet transform (CWT) algorithm, and the wavelet time-frequency diagram are plotted and analyzed. The simulation of a simple transformer high frequency model and experiment analysis show that the various types of winding deformation can be distinguished by the proposed technique. The data processing result by the wavelet transform conforms with the variation pattern of frequency response to different winding fault types. Besides, the verification result of experiment indicates that the wavelet time-frequency diagram obtained by the proposed technique shows better performance than the frequency response curves obtained by the conventional FFT on processing the transient signals.

Improved Method to Obtain the Online Impulse Frequency Response Signature of a Power Transformer by Multi Scale Complex CWT

Online impulse frequency response analysis (IFRA) has proven to be a promising method to detect and diagnose the transformer winding mechanical faults when the transformer is in service. However, the existing fast Fourier transform (FFT) is actually not suitable for processing the transient signals in online IFRA. The field test result also shows that the IFRA signature obtained by FFT is easily distorted by noise. An improved method to obtain the online IFRA signature based on multi-scale complex continuous wavelet transform is proposed. The electrical model simulation and online experiment indicate the superiority of the wavelet transform compared with FFT. This paper provides guidance on the actual application of the online IFRA method.

Identification of Power Transformer Winding Mechanical Fault Types Based on Online IFRA by Support Vector Machine

A transformer is the most valuable and expensive property for power utility, thus ensuring its reliable operation is a major task for both operators and researchers. Online impulse frequency response analysis has proven to be a promising technique for detecting transformer internal winding mechanical deformation faults when a power transformer is in service. However, as so far, there is still no reliable standard code for frequency response signature interpretation and quantification. This paper tries to utilize a machine learning method, namely the support vector machine, to identify and classify the winding mechanical fault types, based on online impulse frequency response analysis. Actual transformer fault data from a specially manufactured model transformer are collected and analyzed. Two feature vectors are proposed and the diagnostic results are predicted. The diagnostic results indicate the satisfied classifying accuracy by the proposed method.

Study on factors of repetitive impulse influencing online IFRA test

Online impulse frequency response analysis (IFRA) is an advanced method for detecting winding faults in transformers. In this method, square wave impulse is commonly used as the excitation signal. In theory, the frequency response curve of a transformer is not related to the excitation signal. However, it is discovered that the test results can be influenced by the input impulse. The influence may cause misinterpretation of the frequency response curve. This study has concentrated on three factors — the pulse width, repetition frequency, and in particular, injection moment. Analyses on these factors are presented. Then, simulation and experimental studies are carried out to verify the influence of these factors.

Impact analysis of the capacitive coupling sensor on bushing external insulation

Online impulse frequency response analysis (IFRA) method is proposed and developed as a new technique to detect the winding mechanical deformation of a running transformer. The preliminary application of IFRA has been realised by using the bushing capacitive coupling sensor (CCS). However, whether the installation of the sensor affects the normal operation of the transformer is still unknown. To study above problem, this study first establishes a simulation model for transformer bushing based on the finite-element method and discusses the influence of sensor installation on the electric field distribution of capacitive bushing. The simulation proves that the installation of the CCS has minimal effect on the safe operation of the bushing. Second, the bushing withstand voltage tests are conducted in a lab, which proves that the installation of CCS meet the requirement for safe operation of capacitive bushing. Finally, the CCSs are installed on running power facilities, and the long and safe running experience indicates that the bushing and related facilities are unaffected by CCSs. The simulation analysis, withstand voltage test, and field application verify that the installation of CCS would not threaten the safe operation of high-voltage bushing, laying the foundation for engineering application of IFRA.

Impact of capacitive coupling circuit on online impulse frequency response of a power transformer

Detecting the early signs of mechanical failures of power transformer winding is necessary and is possible with online monitoring techniques. Online impulse frequency response analysis (IFRA) is a promising diagnostic method when a transformer is in service. This paper examines the unrevealed problem existing in the method, namely, the impact of bushing capacitive coupling circuit on online impulse frequency response. An equivalent electrical model of capacitive coupling circuit and transformer winding is established. The frequency response of the capacitive coupling circuit is obtained to study its influence on online impulse frequency response. The parameter variations of capacitive coupling circuit caused by coupling capacitance variation and bushing dielectric breakdown are simulated to investigate their influence on online impulse frequency response signatures. A few experiments are eventually performed to verify the theoretical analysis and simulation results. This paper contributes to the application of online IFRA.

Noninvasive method for online detection of internal winding faults of 750 kV EHV shunt reactors

Seven hundred fifty kilovolt extra-high voltage (EHV) shunt reactors are important power equipment that stabilize the normal operation of an entire 750 kV power grid. Internal winding faults of a reactor could be a difficult issue when the reactor is in service, and result in sudden equipment failure and significant economic losses. To prevent sudden catastrophic failure of equipment, online detection should be implemented to evaluate the state of the reactor properties when requested. In this paper, a noninvasive method based on online impulse frequency analysis was proposed and developed to detect internal winding faults of a 750 kV EHV shunt reactor. Initially, the safe implementation of the online detecting device was verified before field installation. Unlike in previous work, the online detecting device was modified and improved according to the specific situation of shunt reactors. Original fingerprints of 750 kV EHV shunt reactors were obtained and analyzed after site installation and normal operation of the detecting device. A fault simulation test was implemented to validate the method. This paper provides guidance on actual implementation of online impulse frequency response analysis on power equipment.

Experiences with non-intrusive monitoring of distribution transformers based on the on-line frequency response

The following article presents the results obtained in experiences that use the Impulse Frequency Response Analysis (IFRA) method with a transformer in service. The IFRA method has been implemented in order to transform the transient signals to the frequency domain using Discrete Fourier Transform (DFT). However, it can be considered that the DFT is not the most suitable tool for this type of analysis, since, by definition, this tool is useful for processing stationary signals. Taking that into consideration, the analysis of transient signals could be hypothetically improved by using continuous wavelet transform (CWT), given their variable time/frequency resolution. The analysis of transient signals in Wavelet domain has improved the repeatability of the frequency response curves, as it has been observed in experimental results. The proposed on-line IFRA method, based on Wavelet transform, was validated under load and noload conditions on a 150 kVA three-phase transformer 13200/225 Volts, in the Campus of the Universidad del Valle, Cali, Colombia.

Novel technique to improve the fault detection sensitivity in transformer maintenance test

AbstractEarly detection of incipient faults in transformer windings is important, so that required corrective measures can be taken to help prevent interruption during operation. Low voltage impulse (LVI) and sweep frequency response analysis (SFRA) tests have been widely adopted within the industry to determine a transformer winding's deformation. However, these tests have drawbacks, including limited frequency ranges for the LVI test and time‐consuming measurements for the SFRA test. To obtain better signature analysis in the transformer maintenance test, especially detection of minor faults, this paper suggests a new input signal using a pulse sequence (PS) in the transfer function (TF) analysis. The results of the PS test are compared against the LVI and SFRA tests to complete the assessments, which are derived from experimental works on the 25 kVA distribution transformer. It is concluded that the PS method improves fault detection sensitivity significantly. Copyright © 2009 John Wiley & Sons, Ltd.

Monitoreo no-invasivo de transformadores empleando la respuesta en frecuencia obtenida a partir de señales transitorias controladas

El siguiente artículo presenta un nuevo procedimiento para emplear señales transitorias controladas con el cual sepuede obtener la respuesta en frecuencia (FRA) del transformador en servicio. El procedimiento emplea el métodoIFRA (análisis de la respuesta en frecuencia ante un impulso) complementado con la Transformada Wavelet. Elmétodo IFRA ha sido implementado en trabajos previos, pasando señales temporales al dominio de la frecuenciausando la Transformada de Fourier, sin embargo, Fourier no se considera la herramienta más eficaz para este tipo deanálisis dado que fue desarrollada para el procesamiento de señales estacionarias. Teniendo en cuenta lo anterior, eneste trabajo se empleó la Transformada Wavelet dado su variabilidad en resolución en el dominio tiempo-frecuencia,con lo cual se mejoró la repetitividad de las curvas de respuesta en frecuencia tal como se observa en los resultadosexperimentales.El análisis desarrollado fue aplicado a un transformador de distribución monofásico de 7600/240V, 3 kVA, mostrandola viabilidad del método propuesto, el cual podría también emplearse para la obtención de la curva de repuesta enfrecuencia en transformadores de potencia en servicio.