In-service Power Transformers Research Articles

In-Service Power Transformer Life Time Prospects: Review and Prospects

Power transformers are essential assets in power system networks that must be meticulously monitored throughout their operational life cycle. Given that a substantial percentage of in-service power transformers around the world have reached the end of their projected technical lifespan, utilities are adopting various transformer condition-based maintenance strategies to minimize potentially devastating equipment failure. Thus, further usage of aging fleet of power system assets can be improved by gaining a better understanding of life threatening factors. In-service power transformers and their auxiliary components are susceptible to a variety of operational risks, which can compromise their performance and efficiency, thus resulting in devastating failures, financial losses, and power outages. Hence, it is necessary to investigate transformer life time issues so as to fully grasp the operational and performance status in order to reduce failures and operating costs. This paper presents a unified framework of attributes pertaining to life time issues for transformer operation evaluation together with a summary of recent findings in order to explore the current status and progress of this rapidly progressing field. The failure statistics analysis is presented first, followed by an examination of the transformer’s insulation degradation and aging mechanism. Furthermore, emphasis was on detailing the commonly adopted models and strategies in transformer condition evaluation, fault diagnostics, and remnant life estimation. Despite significant advancements in ascertaining transformer health diagnosis and prognosis technologies, including test accuracy, quick, precise fault localization, and fault type classification, there are still several shortcomings that require additional research, and thus future research perspectives are also discussed.

Prediction of Insulation Sensitive Parameters of Power Transformer Using Detrended Fluctuation Analysis Based Method

Utilities prefer to perform a condition-based assessment of power transformer insulation in the least possible shutdown time. It is essential to estimate the values of various insulation condition sensitive performance parameters with higher accuracy. In the present work, a technique for the estimation of performance parameters, moisture content of paper, dissipation factor of the entire oil-paper insulation, paper conductivity, and activation energy is proposed. The evaluation of these parameters using reported techniques requires complete measurement of polarization and depolarization current profile, which requires around 20000 s of measurement time. The present method uses a forecasted polarization current profile obtained from 600 s of measured polarization current data. Detrended Fluctuation Analysis is applied to the forecasted polarization current data obtained from various in-service power transformers to obtain a suitable parameter. It is shown that this parameter maintains well-defined relationships with parameters; moisture content of paper, dissipation factor of oil-paper insulation, paper conductivity, and activation energy. The proposed technique is beneficial for utilities as it eliminates the requirement of measuring depolarization current and return voltage measurement for estimating activation energy.

Influence of De-Trapped Charge Polarity in Sensing Health of Power Transformer Insulation

Assessment of insulation condition by sensing and subsequent analysis of oil-impregnated paper response at periodic intervals is necessary for reliable operation of transformers. Sensing frequency domain insulation response requires the application of either a sinusoidal excitation voltage (over a wide range) or a custom excitation waveform. This not only requires a specially designed variable-frequency variable-amplitude excitation voltage source but also makes measurement practically problematic. Measurement of low-frequency data is time-consuming and the data tends to get affected by field noise. In comparison, a simpler and cheaper alternative is the analysis of time-domain insulation response. This involves the measurement of low amplitude polarization and depolarization current. A pico-ammeter or Electrometer is generally used for this purpose. The available interpretation scheme of time domain response sensed using Electrometer does not consider the de-trapping charge’s polarity. The result presented in the paper shows that such an assumption inadvertently leads to inaccurate diagnosis. This, in turn, generates doubt regarding the current sensing capability of the Electrometer involved. In the present work, a better interpretation scheme of the data sensed by the Electrometer is discussed. The significance and influence of de-trapped charge polarity on polarization and depolarization currents are investigated. The analysis is tested on data collected from real-life in-service power transformers.

Depolarization Ratio Index (DRI) as alternative method in identifying oil-filled transformer internal faults

Dissolved gas analysis is the most common and well-established method used in identifying internal faults of power transformers. However, an onsite analysis using this method accuracy still need to be improved while the laboratories analysis process takes longer time. New alternative onsite and online method are most urgently need to be studied. Power transformer faults identification based on extended application of polarization and depolarization current (PDC) measurement data on oil sample from in-service power transformer was investigated in this research. The changes in the molecular properties of the insulating oil due to different fault conditions found had influenced the initial time response of the PDC measurement (from 0 to 100 s). In this research, the unique characteristic pattern of each condition was determined by a single unique numerical dimensionless quantity known as depolarization ratio index (DRI). DRI is used to express the changes of depolarization current shape with different fault conditions. The ratio of depolarization current between 5/100 and 10/100 s was found to have a better correlation at least more than 90% on the incipient fault in power transformer. Indeed, the DRI at 5/100 also was observed has higher accuracy especially on units with normal, overheating and arcing condition. In addition, a graphical representation of DRI value at 5/100 and 10/100 s was proposed for fault recognition in power transformer. Both DRI and graphical representation are well validated, and the proposed interpretation techniques have accuracy of more than 90% and able to identify and classify the normal, overheating and arcing condition of the in-service power transformer.

Evaluation of in-service power transformer health condition for Inspection, Repair, and Replacement (IRR) maintenance planning in electric utilities

Evaluation of in-service power transformer health condition for Inspection, Repair, and Replacement (IRR) maintenance planning in electric utilities

Dealing With Data Uncertainty for Transformer Insulation System Health Index

Health index has been widely accepted as a powerful tool for monitoring the condition of power transformer insulation system based on various diagnostic parameters. While this approach has been extensively discussed in the literature, not much attention was given to provide effective solutions to the uncertainty in the used data. According to CIGRE 761, data quality issues may arise due to measurement accuracy as well as incompleteness and unavailability of the required data. Therefore, this article presents the implementation and evaluation of a certainty level model for transformer insulation system health index to deal with data uncertainty. The impact of data unavailability on the health index results is also investigated. Certainty level of the health index is determined by the criticality level of available data, and is reported along with the health index result. A method to handle unavailable data by predicting the oil interfacial tension (IFT) using Random Forest approach is also presented. The proposed certainty level model is designed to accommodate the predicted value of missed data into the health index model while considering its prediction accuracy. The robustness of the developed model is validated through its application in assessing the health condition of six in-service power transformers. The results indicate that by including the proposed certainty level and the prediction approach to eliminate the issue of uncertain and missed diagnostic data, an asset management decision can be taken on operating power transformer fleets with high level of confidence.

In-Service Transformer Oil Regeneration Based on Laboratory-Scale Process

This paper described in-laboratory oil regeneration process of aged transformer oil using adsorbent; Fuller’s earth. The niche of this paper is the usage of real aged transformer oil taken from an 11kV/433V in-service power transformer made by ACEC Transformer in 1986. The regeneration process is achieved by forcing a mixture of aged transformer oil and Fuller’s earth through a filter paper with the aid of a vacuum pump, hence producing reclaimed transformer oil. This oil was then inserted in an amber glass bottle, blanketed with nitrogen, tightly sealed, and labelled. To test the effectiveness of Fuller’s earth as adsorbent, parameters of Dissolved Decay Product (DDP) were measured using UV-Visible Spectrophotometer (UV-Vis) by referring to ASTM D6802. In addition, Total Acid Number (TAN) and Breakdown Voltage (BdV) measurement were carried out complying the ASTM D974 and ASTM D1816 respectively. Results of UV-Vis indicated that Fuller’s earth can adsorbed 25.24% of DDP in aged transformer oil. As the DDP decreased, TAN is 84.62% reduced while BdV increased 50%. These findings are parallel with the breakdown voltage mechanism due to acidity emergence. It is proved that Fuller’s earth is effective adsorbent for oil regeneration process.

Gassing Tendency of Fresh and Aged Mineral Oil and Ester Fluids under Electrical and Thermal Fault Conditions

Operational factors are known to affect the health of an in-service power transformer and to reduce the capabilities and readiness for energy transmission and distribution. Hence, it is important to understand the degradation rate and corresponding behavioral aspects of different insulating fluids under various fault conditions. In this article, the behavior of mineral oil and two environmentally friendly fluids (a synthetic and a natural ester) are reported under arcing, partial discharges, and thermal fault conditions. Arcing, partial discharges and thermal faults are simulated by 100 repeated breakdowns, top oil electrical discharge of 9 kV for five hours, and local hotspots respectively by using different laboratory-based setups. Some physicochemical properties along with the gassing tendency of fresh and aged insulating liquids are investigated after the different fault conditions. UV spectroscopy and turbidity measurements are used to report the degradation behavior and dissolved gas analysis is used to understand the gassing tendency. The changes in the degradation rate of oil under the influence of various faults and the corresponding dissolved gasses generated are analyzed. The fault gas generations are diagnosed by Duval’s triangle and pentagon methods for mineral and non-mineral oils. It is inferred that; the gassing tendency of the dielectric fluids evolve with respect to the degradation rate and is dependent on the intensity and type of fault.

De‐noising of time‐domain spectroscopy data for reliable assessment of power transformer insulation

Polarisation-depolarisation current (PDC) measurement and its analysis is a popular technique for assessing the condition of transformer insulation. Owing to the low magnitude of PDC, recording noise-free PDC data from in-situ power transformers is a challenge. Once the relaxation current data get affected by noise, it becomes difficult to formulate insulation model (as recorded data loses its characteristic shape). This further makes the data difficult to analyse and predict insulation condition. In this study, two de-noising techniques are discussed (one is based on Wavelet Transform while the other is based on Stockwell Transform) for eliminating low-frequency non-stationary noise from recorded PDC data. Comparison between these two techniques suggests de-noising using Stockwell Transform is advantageous over wavelet analysis. The proposed methodology is first tested on data recorded from the sample prepared in the laboratory and then on data measured from real-life in-service power transformer.

Dynamic Fault Prediction of Power Transformers Based on Hidden Markov Model of Dissolved Gases Analysis

Dissolved gases analysis (DGA) provides widely recognized practice for oil-immersed power transformers, and it is mainly interpreted for fault diagnosis. In order to accurately estimate the health index state of power transformers and predict the incipient operation failure, a dynamic fault prediction technique based on hidden Markov model (HMM) of DGA is proposed in this paper. Gaussian mixture model, as a soft clustering method, is used to extract the static features of different health states from a DGA dataset of 65 in-service power transformers with 1600 days operation. Especially, a sub-health state is introduced to enrich the health index and aging stages of power transformers. The static features between health states and concentrations of dissolved gases are built, and the effectiveness of clustering is cross validated. Furthermore, taking time sequence into consideration, transition probability of power transformer between different health states based on the HMM model is calculated and analyzed. The effectiveness of dynamic early warning and incipient fault prediction in sub-health status of in-service power transformers has been proved. Moreover, the dynamic fault prediction is able to provide decision-making basis for practical condition-based operation and maintenances.

Understanding the power transformer component failures for health index-based maintenance planning in electric utilities

Abstract This article presents an analysis of 343 distribution and power transformer component failures in the voltage range of 33 kV–400 kV over an operational period of 11 years from Tamil Nadu Electric Utility (TNEU), India. Two methods were employed in this study. Initially, failure data were analyzed using statistical analysis and grouped into four major failure level which includes electrical, thermal, mechanical and insulation degradation. Further, causes of failure were also identified through maintenance records, direct observations in repair yard and expert consultations. Subsequently, a Health Index (HI) approach to transformer maintenance has been proposed for implementation. By adopting this approach, a case study was presented where a health index was calculated for seven power transformers. The results of HI were used for maintenance decisions such as: should the transformer continue in service, be repaired, be refurbished, or be replaced. This study provides a practical guidance to maintenance personnel for early fault diagnosis that prevents unexpected failure of in-service power transformers and reduce maintenance cost in electric utilities.

Application of adaptive neuro fuzzy inference system to support power transformer life estimation and asset management decision

Power transformer is a critical asset in electrical transmission and distribution networks that need to be carefully monitored during its entire operational life. Considering the fact that a significant number of worldwide in-service power transformers have approached the end of their expected operational life, utilities have adopted various transformer condition-based maintenance techniques to avoid any potential catastrophic failure to the equipment. The extent of transformer insulation system ageing can be quantified through measuring several diagnostic indicators such as interfacial tension number of the insulating oil which has a strong correlation with the number of transformer operating years. Moisture and furanic compounds generated due to paper insulation degradation are indicators for solid insulation ageing. This paper introduces a new adaptive neuro fuzzy logic model to estimate the life of mineral oil-filled power transformers based on the values of insulating oil interfacial tension number, furan content in oil and the moisture content within the cellulose insulation. Also, an integrated asset management decision model is proposed. Results of the proposed model are validated against practical data collected from utility and industry mineral oil-filled power transformers of different ratings, designs, operating conditions and lifespans.

Measurements on partial discharge in on‐site operating power transformer: a case study

This study presents the case study of a substation in-service power transformer referred to the on-site partial discharge (PD) detection and evaluation methods. An original methodology for simultaneous application of three methods is proposed: electrical, acoustic and ultra-high frequency. Transformer is powered by the power grid and no external generator is required according to the proposed methodology. Furthermore, several possibilities of applying these concurrent measurements and benefits of such solution in terms of result interpretation, interference resistance and on-site measurement applicability are indicated. The proposed methodology allows for a fast, accurate and secure PD diagnostics in a power transformer under on-site operating conditions. In the described example, a failure as a surface PD in the vicinity of the first phase of high voltage winding output is detected. The research results provide the basis for further works on a diagnostic expert system, which exploits automatic detection and evaluation of the PD phenomenon in power transformers under normal operating conditions.

Fault Identification of In-Service Power Transformer using Depolarization Current Analysis

Preventive diagnostic testing of in-service power transformers require system outage and expert’s knowledge and experiences in interpreting the measurement results. The chemical oil analysis may cause significant variance to measurement results due to the different practices in oil sampling, storage, handling and transportation. Thus, a cost effective measuring technique by means of a simpler method that is able provide an accurate measurement results is highly required. The extended application of Polarization and Depolarization Current (PDC) measurement for characterization of different faults conditions on in-service power transformer has been presented in this paper. The oil sample from in-service power transformers with normal and 3 different faults type conditions were sampled and tested for Dissolved Gases Analysis (DGA) and PDC measurement. The DGA results was used to confirm type of faults inside the transformer while the PDC pattern of oil with normal, partial discharge, overheating and arcing were correlated to the oil sample conditions. The analysis result shows that depolarization current provides significant information to defferenciate fault types in power transformer. Thus this finding provides a new alternative in identifying incipient faults and such knowledge can be used to avoid catastrophic failures of power transformers.

Application of Frequency Response Analysis for in-service power transformers


 
 
 CIGRE, IEC and IEEE have recently approved the technique of Frequency Response Analysis (FRA) as an application tool for diagnosis of mechanical failures in power transformer’s active part, i.e., windings, leads and the core. The diagnosis is based on the discrepancy between frequency responses measured on power transformers mainly at different time points. In Vietnam, utilities such as Power Transmission Companies and Power Corporations are investigating this technique for application on their power transformers. 
 Mechanical failures in power transformers cause changes on measured frequency responses starting from a medium frequency range, from several hundreds of Hz or tens of kHz depending on transformer/winding type and power. For a reliable diagnosis, the understanding of transformer/winding structure on measured frequency responses is of importance; thus, the international standards suggested the simulation approach with physical distributed transformer circuits should be exploited. 
 The development of physical distributed circuits of power transformers normally needs availability of internal transformer structure and material properties for an analytical approach. However, for in-service power transformers, this task is challenging since the required data are not available. 
 For a feasible application of the simulation based FRA interpretation, this paper introduces an investigation on the development of a distributed equivalent circuit of an in-service 6.5 MVA 47/27.2 kV Yd5 power transformer. The result of this investigation is a feasible approach in determining electrical parameters in a physical distributed circuit, which supports analysis of frequency responses measured at transformer terminals for real application on in-service power transformers of utilities. 
 
 

Condition monitoring of vegetable oil insulation in in-service power transformers: some data spanning 10 years

Power transformers are expected to operate for several decades [1]. The insulating oil of a power transformer can be replaced if it becomes too degraded. However, it is obviously financially preferable to a utility that the insulating oil last as long as practically possible. Vegetable oils were among some of the earliest types of dielectric liquid, e.g., a team led by George Westinghouse used caster and linseed oils from the late 1880s onward [2]. However, a disadvantage was that the vegetable oils readily oxidized, and so mineral oils were adopted. In the mid to late 1990s there was renewed interest in vegetable oil-based dielectrics, to which antioxidants had been added [3]. Subsequently, vegetable oil-based transformer oils became commercially available. Initially, they were used only in smaller distribution transformers. However, as the electrical industry became more confident, they began to be used in ever larger power transformers from the early 2000s.

CO2/CO concentration ratio: A complementary method for determining the degree of polymerization of power transformer paper insulation

Since power transformers are valuable components of power networks, reliable determination of the remaining lifetime of a transformer is very important for power utilities. Power transformers consist of oil, a core, wire, and paper (cellulose), and their performance and remaining useful lifetime depend on the state of the oil-impregnated paper. Changing the oil over the lifetime of a transformer is possible, but it is not possible to change the insulation paper. Thus, the end of the paper lifetime will also be the end of the transformer lifetime (typically more than 40 years). The health condition of an in-service power transformer can be assessed by chemical and electrical tests of the oil and the paper [1].

Moisture-Dependent Thermal Modelling of Power Transformer

Hot-spot temperature (HST) is recognized as one of the key factors affecting the life expectancy of a transformer. In this study, a moisture-dependent thermal model (MDTM) for estimating the transformer hot-spot temperature is proposed. In this model, nonlinear thermal resistance is formulated by considering oil and cellulose (paper and pressboard) of the transformer. Especially, the effect of moisture concentration and temperature on the thermal resistance of cellulose is taken into account. The proposed MDTM is verified by using historical data, including moisture-in-oil measurements, temperature measurements, and loading profiles, which were collected from two in-service power transformers, that is, a vegetable oil-filled transformer and a mineral oil-filled transformer. Comparisons between the proposed MDTM and a number of existing thermal models were performed on the basis of adequacy and accuracy metrics.

Winding Condition Assessment of Power Transformers Based on Vibration Correlation

In this paper, a winding condition assessment model using vibration signals is presented, which can be used to diagnose power transformers online. The basic principle of this model is based on the correlation analysis of winding vibrations. In the model, the fundamental frequency vibration analysis is used to separate the winding vibration from the mixed signal. Then, a health parameter is proposed via principal component analysis. Another parameter is also proposed to detect the fault locations for suspected faulty transformers. In laboratory tests, the model is validated on a specifically designed 110-kV transformer. During the tests, man-made winding deformations are simulated to compare the vibrations under different conditions. The model has also been tested on several in-service power transformers. The preliminary study shows that the proposed model is feasible to assess the power transformer winding condition.

Application of fuzzy support vector machine for determining the health index of the insulation system of in-service power transformers

With the integration of data and information obtained from a variety of chemical and electrical tests on transformer insulating oil, it is possible to evaluate the health condition of the insulation system of an in-service power transformer. This paper develops an intelligent algorithm for automatically processing the data collected from oil tests and determining a health index for the transformer insulation system. This intelligent algorithm adopts a fuzzy support vector machine (FSVM) approach, which constructs a statistical model using a training database based on the historic data collected from 181 in-service power transformers. The procedure of constructing the training database, the formulation and implementation of FSVM and the data preprocessing methods for dealing with a class imbalanced training database is presented in this paper. Numerical experiments are also conducted to evaluate the performance of the algorithms developed in the paper.