Power Transformers In Service Research Articles

Determining the Remaining Functional Life of Power Transformers Using Multiple Methods of Diagnosing the Operating Condition Based on SVM Classification Algorithms

Starting from the current need for the safety of energy systems, in which power transformers play a key role, the study of the health of power transformers in service is a difficult and complex task, since the assessment consists of identifying indicators that can provide accurate data on the extent of degradation of transformer components and subcomponents, in order to establish a model for predicting the remaining life of transformers. Therefore, this paper proposes a model for assessing the remaining service life by diagnosing the condition of the transformer based on the health index (HI) obtained from a multi-parameter analysis. To determine the condition of power transformers, a number of methods are presented based on the combination of the combined Duval pentagon (PDC) method and ethylene concentration (C2H4) to determine the fault condition, the combination of the degree of polymerisation (DP) and moisture to determine the condition of the cellulose insulation and the use of the oil quality index (OQIN) to determine the condition of the oil. For each of the classification methods presented, applications based on machine learning (ML), in particular support vector machine (SVM), have been implemented for automatic classification using the Matlab development environment. The global algorithmic approach presented in this paper subscribes to the idea of event-based maintenance. Two case studies are also presented to validate SVM-based classification methods and algorithms.

Generic approach to calculation of short circuit currents in power transformers

Power transformer in service is exposed to various voltage and current stresses. The ability to withstand short circuit is an essential requirement for power transformers. There are different types of short circuit: single - phase to earth, double - phase with or without simultaneous earth fault and three - phase short circuit. These various short circuit conditions result in different stress conditions for the windings. Mesh analysis and symmetrical components are the two methods most commonly used for determining the magnitude of short circuit currents. In this paper, both methods will be presented with results compared on a real-case transformer. Also, a generic scheme using the symmetrical components approach is designed in order to standardize the short-circuit currents calculation for all power transformer types and to reduce the time required for obtaining results.

The Vibroacoustic Characteristics Analysis of Transformer Core Faults Based on Multi-Physical Field Coupling

Power transformers play an important role in the safe and reliable operation of the whole power grid. Once a fault occurs, it will endanger the normal operation of the transformer, and even result in power grid accidents. Accurate and practical methods of transformer fault monitoring and type identification have attracted extensive attention in the field of electrical engineering. However, it is difficult to obtain a large number of measurement data for different fault types on a large power transformer. The vibroacoustic characteristics of transformer faults have significant asymmetry. For the power transformer in service, it is complex and uneconomic to obtain the vibroacoustic signals under different fault conditions. To handle this problem, this paper proposes simulation methods of several common transformer core faults, based on multi-physical field coupling, and then analyzes the vibroacoustic signals generated by the operating transformer. Finally, it verifies the results of acoustic and vibration signals under several faults, through physical experiments. The results show that the transformer fault simulation method is reasonable and accurate. Furthermore, a change in the transformer core state will cause a change in the transformer vibroacoustic characteristics, and different types of core faults can be distinguished by the analysis of vibroacoustic characteristics.

A fault diagnostic method for oil-immersed transformer based on multiple probabilistic output algorithms and improved DS evidence theory

Power transformer is one of the most vital equipment in the electric power systems, which serves as the connection of power networks at different voltage levels. However, the power transformers in service are subjected to various stresses like thermal stress and electrical stress, which may lead to several faults such as arching, partial discharge and overheating. In order to detect these incipient faults and ensure the normal operation of power transformers, the diagnostic methods based on Dissolved Gas Analysis (DGA) have been developed. However, the DGA methods suffer from low diagnostic accuracy. On the other hand, with the development of Artificial Intelligence (AI), a lot of intelligent algorithms have been applied in this area. However, most traditional AI algorithms are hard classification, which may cause the wrong diagnosis of faults. To overcome those disadvantages, a method based on the improved DS evidence theory and multiple probabilistic output algorithms is proposed in this paper to improve the performance of fault diagnosis of oil-immersed transformers. Firstly, the performance of the soft and hard classification algorithms is compared and analyzed. After that, three diagnostic models based on the Multiclass Relevance Vector Machine (MRVM), Multiclass Support Vector Machine (MSVM) and Back Propagation Neural Network (BPNN) are established. In order to improve the accuracy of these models, Particle Swarm Optimization (PSO) is used to optimize their hyper-parameters. Finally, the combination of these three probabilistic output models is achieved based on the improved Dempster-Shafer (DS) evidence theory, which can help to obtain a more accurate fault diagnosis result by fusing multiple algorithm results. The case study results show that the proposed method achieves a probabilistic output for the fault diagnosis of oil-immersed transformers and overcomes the deficiency of traditional DGA methods which are difficult to get accurate diagnosis results and can’t summarize the fault development rule inductively.

Multigas Analysis by Cavity-Enhanced Raman Spectroscopy for Power Transformer Diagnosis.

We demonstrate the utility of cavity-enhanced Raman spectroscopy (CERS) as a unique multigas analysis tool for power transformer diagnosis. For this purpose, improvements have been added to our recently introduced CERS apparatus. Based on optical feedback frequency-locking, laser radiation is coupled into a high-finesse optical cavity, thus resulting in huge intracavity laser power. With 20 s exposure time, ppm-level gas sensing at 1 bar total pressure is achieved, including carbon dioxide (CO2), carbon monoxide (CO), hydrogen (H2), methane (CH4), ethane (C2H6), ethylene (C2H4), acetylene (C2H2), nitrogen (N2), and oxygen (O2). By using the internal standard gas (sulfur hexafluoride, SF6), the quantification of multigas with high accuracy is also realized, which is confirmed by the measurement of calibration gases. For fault diagnosis, transformer oil is sampled from a 110 kV power transformer in service. Dissolved gases are extracted and analyzed by the CERS apparatus. Then the transformer is diagnosed according to the measurement results. CERS has the ability to analyze multigas with high selectivity, sensitivity, and accuracy, it has great potential in gas sensing fields.

Power transformer protection using fuzzy logic based controller

The Power Transformer is one of the most expensive and important electrical equipment of a Power Transmission System, the loss of this equipment through catastrophic failure can be very costly. To avoid this, methods and systems for monitoring and protecting of Power Transformers in service are being developed. Since many mal-operation cases of transformer differential protection are caused by inrush current problems and thus, the rapid discrimination of inrush current from internal fault is most essential to avoid the inadvertent operation of the protective relay. Therefore, measures must be taken to ensure that the Power transformer differential relay operates precisely to differentiate between fault and non-fault conditions to ensure service reliability. This differential relay must restrain from tripping during magnetizing inrush and rapidly operate the tripping during internal faults. This paper presents a new relaying algorithm to enhance the fault detection sensitivities of conventional techniques by using fuzzy logic approach of MATLAB/Simulink. The proposed fuzzy-based relaying relies on the principle of harmoniccurrent restraint, where the magnetizing-inrush current is characterized by large second harmonic contents that are not noticeably present in fault currents

Denoising of acoustic partial discharge signals corrupted with random noise

Power transformers are one of the most important and expensive electrical equipment that require online condition monitoring. Partial discharge (PD) measurement is considered the most effective and non-destructive approach to observe the condition of power transformers in service. However, many sources of noise interfere with the captured PD signals leading to waveform deformation. Thus, it is important for the detection and classification of PD signals to initially suppress the noise encountered with PD measurement. In this paper, we investigate a method, named power spectral subtraction denoising (PSSD) that uses fast Fourier transform to restrain the random noise encountered in measured acoustic PD signals. The denoising performance of PSSD is compared with those of wavelet-based denoising techniques in addition to the mathematical morphological filter. The denoising techniques are first examined on PD signals contaminated with low and high levels of simulated random noise. The denoising evaluation metrics show the superiority of PSSD over the other techniques. Moreover, a modified PSSD (M-PSSD) method is presented to address the actual PD signals corrupted with real random noise. High reduction in noise levels are achieved using M-PSSD.

Power transformer diagnosis based on mechanical oscillations due to AC and DC currents

The reliable operation of power transformers is an essential factor for the high availability of electric energy. Reliability demands can be enforced using condition based asset management which relies on different diagnosis measurements and continuous monitoring. This contribution determines the abilities of asset monitoring by regarding transformers' mechanical oscillations during operation. The physical origins of oscillation sources are introduced as well as the common measurement technique using accelerometers. The contribution evaluates the method's principle diagnostic abilities by correlating the actual mechanical status of an active part and resulting mechanical vibrations in a laboratory setup. The practical usability is determined by long-term comparison of measurements on different power transformers in service. Several influencing factors are evaluated: the sensor position, the changing transformer's load and operating temperature during service as well as the position of the on-load tap changer. Additionally, mechanical oscillations are used to determine the influence of superimposed, undesirable DC components on 3-limb power transformers originated by coupling between nearby AC and HVDC lines.

Condition Evaluation for Aging State of Transformer Oil-paper Insulation Based on Time-frequency Domain Dielectric Characteristics

For the oil-impregnated power transformers in service, the condition of the main insulation system is very important. This article reports a non-destructive evaluation technique based on time-frequency domain dielectric characteristics. First, the polarization/depolarization current and frequency-domain spectroscopy were well measured under controlled laboratory conditions. Then, the recovery voltage curves and recovery voltage polarization spectroscopy were also obtained by using the conversion method between polarization/depolarization current and recovery voltage based on the extended Debye model. After that, the time-domain dielectric characteristics, which can represent oil-paper insulation aging, were extracted from the polarization/depolarization current curves, recovery voltage curves and recovery voltage polarization spectroscopy. In addition, the frequency-domain dielectric characteristics, which can represent the oil-paper insulation aging, were also proposed in terms of frequency domain spectroscopy. Finally, the change rules of time-frequency domain dielectric characteristics under different aging times were investigated in depth. Results show that the time-frequency domain dielectric characteristics are sensitive to the aging states of power transformers, which could be utilized to diagnose the aging state of oil-paper insulation in field power transformers.

Diagnostic Field Testing and Condition Assessment of Power Transformers in Service

In this era of reformation, liberalization and unbundling of electricity markets, asset management in power sector has assumed greater prominence. Power transformers are key components in any transmission & distribution network and loss of a transformer can have an enormous impact on reliability and availability of power supply and on cost. As society is more and more dependent on electricity for development, the utilities are under pressure to meet the ever-growing demands for reliable power supply. Economic factors are the main consideration and in order to minimize capital expenditure on new equipment, it is a common policy among utilities to maximize the use of existing networks by operating at their design capability. This can be achieved by according importance to the maintenance practice. A survey of the literature indicates that there are more failures of transformer due to poor maintenance, improper operation, severe weather conditions and manufacturing and design defects than due to insulation ageing. The utilities shall have a systematic O & M practice that include diagnostic tests for condition assessment and health checkup of the equipment. The objective of the condition monitoring tests is to detect the first symptoms of incipient faults, ageing development or other problems and monitor their evolution to enable the operator to take appropriate action to avoid major failure. The paper reviews the results of various diagnostic tests including dielectric response methods for condition assessment of power transformers.

Frequency response analysis and short-circuit impedance measurement in detection of winding deformation within power transformers

Power transformers are in service under different environmental, electrical, and mechanical conditions [1] and may be subject to enormous hazards during the course of operation [2], [3]. They are commonly considered to be the heart of the transmission and distribution sectors of electric power systems; monitoring their condition and diagnosing faults are important parts of the maintenance function [4]. Utility engineers strive to keep power transformers in service and to prevent even shortterm outages. Failure of a transformer can cause extensive damage to equipment owned by consumers or the utility [5].

Phased-ultrasonic receiving-planar array transducer for partial discharge location in transformer

Partial discharge (PD) location in transformers is very important, and many methods that have been brought forward in past decades have a limitation theoretically, namely, they cannot locate multiple PDs in electrical equipment. In this paper, a new PD location method based on UHF and ultrasonic-phased arrays receiving theory has been presented, which has a possibility to locate multiple PDs. According to the method, a phased-ultrasonic receiving-planar array sensor that possesses 16 * 16 elements is designed; and, based on the phased-array theory, the characteristics of the plane sensor are studied. The laboratory experimental tests on the plane sensor element indicates that it has a good performance within the frequency band of the main ultrasonic energy produced by PD in transformer oil. Location tests are conducted on one or two piezoelectric ultrasonic sources in oil, which are both simulated as PD sources and triggered by an electrical pulse whose front is considered as a time benchmark in the locating algorithm. The test results show locations to one and two PDs can be realized in a single measurement, which lays a foundation for locating PDs in a power transformer in service.

Review of condition assessment of power transformers in service

As transformers age, their internal condition degrades, which increases the risk of failure. To prevent these failures and to maintain transformers in good operating condition is a very important issue for utilities. Traditionally, routine preventative maintenance programs combined with regular testing were used. The change to condition-based maintenance has resulted in the reduction, or even elimination, of routine time-based maintenance. Instead of doing maintenance at a regular interval, maintenance is only carried out if the condition of the equipment requires it. Hence, there is an increasing need for better nonintrusive diagnostic and monitoring tools to assess the internal condition of the transformers. If there is a problem, the transformer can then be repaired or replaced before it fails. An extensive review is given of diagnostic and monitoring tests, and equipment available that assess the condition of power transformers and provide an early warning of potential failure.

A fundamental approach to transformer thermal modeling. II. Field verification

For pt.I see ibid., vol.16, no.2, p.171-5 (2001). This paper has two main objectives. One is to show that the top oil rise thermal model proposed in part I is valid, for a large power transformer in service. The second is to show that there is a convenient way of estimating the parameters without removing the transformer from service. A Manitoba Hydro 250 MVA OFAF transformer was chosen and instrumented with data-gathering equipment. Two twenty-four hour test runs were performed, one in February of 1999 and the other in July of 1999. The most basic parameter to be determined was the rated top oil rise but also found were the top oil line constant and the nonlinearity exponent, commonly given the symbol n. The results are very positive.

Monitoring of power transformers using the transfer function method

Summary form only given as follows. The transfer function concept is well known as an additional method of evaluating the impulse test of power transformers in the test laboratory. Another application for this method is monitoring of power transformers in service. According to the method of how to measure transient signals for the calculation of transfer functions, two kinds of monitoring can be distinguished: off-line and on-line monitoring. Both kinds of monitoring as well as their influencing factors are discussed with on-site measurements on power transformers in service.

An improved transformer top oil temperature model for use in an on-line monitoring and diagnostic system

In this paper, the authors examine dynamic models of power transformer top oil temperature for use in an online monitoring and diagnostic system. Data taken from large transformers in the field indicate that the IEEE model of top oil temperature rise over ambient temperature does not adequately account for daily variations in ambient temperature. The authors propose a modification that accurately predicts top oil temperature and can be implemented in an online system. This model is verified using data from a large power transformer in service.

Detection of faults in new and old transformers by dissolved-gas analysis

The author looks at the technique of monitoring the condition of oil immersed power transformers in service by analysing the gases dissolved in the oil. Results from a 130 mVA 135.4/69.5 kV autotransformer and from a 47 MVA 120/26.4 kV transformer are discussed. >

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.

Correction: Discussion on “Mechanical strength of power transformers in service”

Correction: Discussion on “Mechanical strength of power transformers in service”

Discussion on “Mechanical strength of power transformers in service”

Discussion on “Mechanical strength of power transformers in service”