In-service Transformers Research Articles

EVALUAREA SOLICITĂRILOR TERMICE ȘI A DURATEI DE VIAȚĂ A TRANSFORMATOARELOR DE DISTRIBUȚIE AFLATE ÎN REGIM PERIODIC NESINUSOIDAL

Electrical transformers are some of the most important equipment in the entire electricity distribution chain. Their operation with optimal values of the parameters (electrical, thermal and mechanical) ensures the continuous supply of consumers. The modern electrical loads of power distribution transformers are often nonlinear and generate several power quality problems, especially the distortion of the waveform of the current that flows through the windings of the transformer. This generates additional stresses (electrical and thermal) of the various components of the transformer (originally designed to operate in pure sinusoidal mode) which can cause abnormal (faulty) operation of the transformer and ultimately reducing its life (estimated by the manufacturer for permanent sinusoidal regime). In order to prevent or diminish the negative effects of the nonsinusoidal regime on the transformer, a deliberate limitation (reduction) of its maximum load is performed. The procedure is known as transformer derating. Its main aim is to establish the most appropriate declassification factors (denomination), resulting from the correlation of the nominal and constructive data of the transformer with the power quality parameters, measured in its secondary part (usually, these are the current distortion level and the corresponding spectrum harmonic). This paper analyzes qualitatively and quantitatively these aspects and proposes a downgrading procedure for in-service transformers which it illustrates in a case study.

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.

Detrapped Charge-Affected Depolarization-Current Estimation Using Short-Duration Dielectric Response for Diagnosis of Transformer Insulation

Analysis of detrapped charge [evaluated from polarization–depolarization current (PDC)] is effective in carrying out insulation diagnosis. However, computation of detrapped charge requires PDC data for at least 1600 s (PDCs each measured for 800 s). Such a technique of obtaining detrapped charge from short-duration PDC data considers few assumptions (including modeling of detrapping current by a straight line), which might not always be true for real-life equipment. Furthermore, PDC data are susceptible to noise and other environmental factors. Hence, it may be practically difficult to allow a measurement time of 1600 s during field measurement. Therefore, it is advisable to reduce the measurement time as well as the volume of data required. In this article, a method has been proposed, which is capable of estimating detrapped charge using only polarization current measured for 600 s. In addition, some unique advantages offered by the proposed method are also discussed in this article. The proposed technique is tested on data collected from several real-life in-service transformers.

Predictive Formulas to Improve Transformer Protection During Inrush Current Using the Proposed DC Equivalent Circuit

Switching- on a power transformer creates energizing inrush current (EIC) which in turn leads to sympathetic inrush current (SIC) in adjacent in-service transformers (ITs). Sympathetic inrush current changes the waveform and prolongs the duration of its origin EIC. This makes the analysis and estimation of EIC complicated. Occasionally, the stress of EIC can cause internal short circuit in energizing transformer. Detecting short-circuit fault current during EIC is a challenge for transformer differential protection. On the other hand, SIC may mislead the earth-fault protection of in-service-transformer. This paper, first, proposes a new dc equivalent-circuit based on the dc-component of inrush current, which is efficient in analyzing concurrent EIC and SIC. Next, based on this equivalent circuit, new predictive formulas for EIC and SIC are derived. By employing these formulas, the transformer internal fault during inrush current can be detected based on the comparison between the predicted and measured waveforms, which prevents differential protection from fail-to-trip. Also, the predicted SIC waveform can be compared with the measured waveform to prevent IT earth-fault protection from mal-trip, during inrush current. Finally, the proposed equivalent circuit and formulas are verified using the EMTP simulation results of a real grid, under different system conditions.

Impact of free radicals on the electrostatic charging tendency of transformer oils

In this contribution, the influence of free radical concentration on the electrostatic charging tendency (ECT) of aged mineral oils is reported. A free radical reagent, 2,2 diphenyl-1-picrylhydrazyl, is used to assess the relative concentration of free radicals. Prior to the ECT measurement, the oil samples were subjected to various stresses (corona discharges, multiple arcs or local overheating). Different ASTM standards were considered to assess the physicochemical properties before and after stress applications. It is found that free radicals affect the ECT. The theoretical premises that, free radicals may contribute to the ECT, are experimentally confirmed under laboratory conditions. The reason why this information is important is that ECT can significantly affect partial discharge inception in in-service transformers.

Transformer Condition Monitoring Based on Load-Varied Vibration Response and GRU Neural Networks

Study of the vibration mechanisms of transformer windings may lead to useful applications of vibration techniques to transformer online diagnosis. In a power transformer, the clamping force provides a boundary mechanical constraint to ensure the integrity of the winding. Thus, the looseness of the clamping force is an important health indicator for a transformer. Although the effect of clamping force on a winding’s stiffness and natural frequencies is known, the effect of time-varying load current on these natural frequencies remains unsolved. In this paper, this effect is investigated by studying the mechanical frequency response function of an on-load single-phase winding under different clamping forces and variation of the harmonic amplitude of in-service transformers with load current. Then, a gated recurrent unit (GRU) neural network is used to explore the relationship between current sequence and vibration sequence for operating transformers. This study shows that the electromagnetic force induced by the load current affects the vibration response of the winding structure, especially when the looseness of the clamping force is significant. A potential application of the observed phenomenon for online detection of winding conditions is also illustrated.

A Study of Nonlinear Behavior of Transformer Windings in Structural Degradation Detection

Abstract In this paper, the transient vibration responses of power transformer windings subjected to the mechanical and electrical impacts are studied with the aim of detecting the structural degradation of windings. The axial vibration mechanism of windings is described as a multi-degrees-of-freedom model with the Rayleigh damping formulation. Then, the nonlinear damping curves are extracted from the original vibrations by using Morlet wavelet transform. During the laboratory experiment, the model was validated on a full-size winding model, and the free vibrations from different coils were acquired when the mechanical impact was applied. Furthermore, the clamping force adjustment experiment was performed to simulate the structural degradation of windings, and the nonlinear relationship between the clamping force and the damping coefficients was investigated. Finally, the proposed method is applied to the tank vibrations collected during the de-energizing process for in-service transformers, and the results of the normal and the anomalous transformers are compared. It is concluded that the proposed method not only reveals the relationship between the transient vibration and the winding structure but also can effectively identify the winding condition.

Condition Assessment of Power Transformer Insulation Using Short-Duration Time-Domain Dielectric Spectroscopy Measurement Data

Utilities prefer noninvasive methods for assessing the condition of power transformer insulation. Analysis of polarization–depolarization current (PDC) is one such popular method. One such analysis involves the estimation of trapped charge released from the interfacial region of oil–paper insulation. The literature shows that such charges can be reliably used for the diagnosis of transformer insulation. However, such analysis requires a complete profile of PDC. PDC measurement (an offline technique) takes a large amount of time (several hours) to complete. The magnitude of PDC data for a larger value of time is also sensitive to changes in environmental conditions and field noise as its magnitude is low. Hence, a reliable estimation of detrapped charge may require numerous PDC measurements. This situation is not convenient for utilities as it prolongs shut down time. In this article, a method has been proposed which is capable of estimating detrapping charge using PDC data measured for a short span of time. The proposed method is tested on data collected from several real-life in-service transformers.

Estimation of paper conductivity from short duration polarisation–depolarisation current for diagnosis of power transformer

The value of paper conductivity provides quantitative evaluation of transformer insulation health. However, proper identification of paper conductivity requires complete profile of polarisation–depolarisation current (PDC). PDC measurement being a time-consuming offline process generally takes several hours to complete. Furthermore, magnitude of PDC becomes very low at larger value of time, which makes it sensitive to changes in environmental conditions and field noise. Hence, accuracy of paper conductivity identification can be ensured by conducting multiple measurements. This in-turn prolongs shutdown time of equipment and become less advantageous to utilities. Here, a method is proposed which is capable of estimating paper conductivity using PDC data recorded for only 800 s. The proposed technique is tested on data collected from several real-life in-service transformers. In order to illustrate the accuracy of the proposed technique, paper conductivities (calculated from short duration PDC) were compared with those computed using PDC measured for 10,000 s.

Top oil heat distribution pattern of ONAN corn oil based transformer with presence of hot spot study using FEMM

Transformer thermal modelling is a crucial aspect to be considered as this may help the determination of heat capacity of transformer. This paper present, simulation study on Oil Natural Air Natural (ONAN) transformer heat distribution pattern with and without presence of hot spot temperature (HST). This paper aims to compare the effects of different HST value at different locations inside the transformer unit as well as to evaluate the top oil thermal behaviour of corn oil as cooling mechanism in a transformer. To achieve aforementioned objectives, three HSTs were introduced to the 30 MVA transformer winding to find the total heat build-up in the top of the transformer tank. The outcome of thermal properties is examined using x-y temperature plot. From the results found that the location of HST affects overall transformer’s temperature. HST at the top of the winding give a significant effect compared to when HST is at the bottom of the winding. It is also evident that the usage of corn oil reduced the temperature distribution of the transformer. The findings suggest that the temperature distribution study especially on transformer is important to monitor in-service transformer in a non-invasive manner.

Use of Interfacial Charge for Diagnosis and Activation Energy Prediction of Oil-Paper Insulation Used in Power Transformer

Activation energy is popularly used for the estimation of remaining life of transformer insulation. It is defined as the average rate of all reactions that happen with cellulose. Existing literature shows that the activation energy of oil–paper insulation can be obtained from polarization depolarization current (PDC) and return voltage measurement (RVM) data that are measured at a specific temperature. It is practically difficult to ensure the same measurement temperature for both PDC and RVM data. On the other hand, PDC data and its analysis get influenced by de-trapping current. This de-trapping current is generated by ionic charge carriers that get freed from trap sites during PDC measurement process. Formation of these trap sites is related to physical, chemical reactions that happen at oil–paper interface. This paper proposes a methodology which uses de-trapped charge, dislodged from deep and shallow traps, to assess insulation condition and for the prediction of activation energy. Thus, eliminating the need of RVM data. The proposed method is tested using data collected from various real-life in-service transformers.

Application of Machine Learning in Transformer Health Index Prediction

The presented paper aims to establish a strong basis for utilizing machine learning (ML) towards the prediction of the overall insulation health condition of medium voltage distribution transformers based on their oil test results. To validate the presented approach, the ML algorithms were tested on two databases of more than 1000 medium voltage transformer oil samples of ratings in the order of tens of MVA. The oil test results were acquired from in-service transformers (during oil sampling time) of two different utility companies in the gulf region. The illustrated procedure aimed to mimic a realistic scenario of how the utility would benefit from the use of different ML tools towards understanding the insulation health index of their transformers. This objective was achieved using two procedural steps. In the first step, three different data training and testing scenarios were used with several pattern recognition tools for classifying the transformer health condition based on the full set of input test features. In the second step, the same pattern recognition tools were used along with the three training/testing scenarios for a reduced number of test features. Also, a previously developed reduced model was the basis to reduce the needed number of tests for transformer health index calculations. It was found that reducing the number of tests did not influence the accuracy of the ML prediction models, which is considered as a significant advantage in terms of transformer asset management (TAM) cost reduction.

Calculating a Health Index for Power Transformers Using a Subsystem-Based GRNN Approach

A power transformer is one of the most crucial items of equipment in the electricity supply chain. The reliability of this valuable asset is strongly dependent on the condition of its subsystems such as insulation, core, windings, bushings and tap changer. Integration of various measured parameters of these subsystems makes it possible to evaluate the overall health condition of an in-service transformer. This paper develops an artificially intelligent algorithm based on multiple general regression neural networks to combine the operating condition of various subsystems of a transformer to form a quantitative health index. The model is developed using a training set derived from four conditional boundaries based on IEEE standards, the literature and the knowledge of transformer experts. Performance of the proposed method is compared with expert classifications using a database of 345 power transformers. This shows that the proposed method is reliable and effective for condition assessment and is sensitive to poor condition of any single subsystem.

Transformer Sympathetic Inrush Characteristics and Identification Based on Substation-Area Information

Sympathetic inrush is a magnetizing inrush phenomenon between two or more transformers when one of them is switched on under no load condition. This phenomenon has caused transformer differential protection maloperation in several cases due to the failure of the second harmonic restraint algorithm. Sympathetic inrush is very different in both the mechanism and characteristics from general inrush generated by a single transformer. Hence, a special identification method is required. The physical mechanism of sympathetic inrush between transformers is thoroughly studied to understand its characteristics, especially the alternate saturation feature. A substation-area information-based identification method is proposed to identify sympathetic inrush of the in-service transformer (IT). The alternate opposite saturation characteristic is used in the method. Both locking and unlocking criteria are designed for current differential protection to prevent the IT protection from maloperation. The proposed method is verified to be effective under different system conditions by simulations conducted in PSCAD and RTDS.

An Attempt to Investigate Transformer Failure by Dissolved Gas Analysis

Reliable and continued performance of power transformer is the key to profitable generation, transmission & distribution. Power transformer failures lead to power supply interruptions in developing nations like India. DGA technique plays an important role in nullifying these unforeseen interruptions. One of the best methods of detecting certain problems which can lead to failure of the transformer is dissolved gas analysis (DGA). failure rate of transformers is very high in India - 25% per annum which is not favorable compared to international failure rate of 1-2%. To prevent failures, effective analysis and diagnosis needs to be investigated. Transformer failures presented in this paper are catastrophic and of high interest. Case studies are presented here for the transformers which have higher fault gas concentrations. An attempt has been made in this paper to assess in-service transformers, new transformers after factory tests and internal condition of service on load tap changers by dissolved gas analysis.

Transformer oil reclamation by combining several strategies enhanced by the use of four adsorbents

From an environmental perspective, petroleum-based aged oils removed from power transformers are source of several pollutants and therefore cannot be disposed of without due care. The degradation of oil in in-service transformers is due to various factors concurrent with the operation of the units over several years. The present study proposes a new strategy to rejuvenate used mineral oils by combining centrifugation, dehydration and sorption with four different adsorbents: activated carbon (ACH), silica gel (SG), magnesium oxide (MO) and activated bentonite (AB). The process of regeneration proposed in this study resulted in a level of restoration that saw the used oil take on the characteristics of new oil (colour, dissipation factor, resistivity, permittivity, acid number). The results also showed that the optimum form of the re-refined base oil can be attributed to a 10% (w/w) quaternary mixture of the adsorbents, itself comprised of 1% ACH, 6% SG, 1% MO and 2% AB. The anticipated benefits are reduced risk of dielectric breakdown blamed for over 75% of extra high-voltage (EHV) power transformer failures and extended transformer life expectancy by retarding the solid insulation aging processes.

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. 
 
 

Investigation into the Physical Mechanism and Influencing Factors of Sympathetic Inrush for Transformers in Series

Sympathetic inrush is a magnetizing inrush phenomenon generated in an in-service transformer, when a nearby transformer in series or in parallel is energized. This phenomenon lasts longer than the general inrush and can cause protection maloperation. The study focuses on sympathetic inrush in series transformers. Flux linkage expressions for the series transformers are derived using the Laplace transform. The residual flux, switching angle, and system impedance factors are included in the expressions. The physical mechanism of the sympathetic inrush for series transformers is explained based on the characteristics of the flux linkages. The interaction between the saturated transformers during sympathetic inrush is also investigated. Moreover, the influence of the switching angle, residual flux, system impedance, and load on the inrush currents is analyzed and validated.

A vibration measurement system for health monitoring of power transformers

Abstract Health and usage monitoring (HUM) systems are critical for power transformers, and should provide accurate diagnostic information without delay. Diagnostic methodologies are the core of HUM systems. In this paper, we propose four complementary diagnostic techniques for power transformers based on vibrations that provide health metrics from different perspectives. During the field experiment, the vibrations of several in-service transformers under different conditions are collected, and the corresponding diagnostic results are obtained. Next, a health grade system is defined, and the suggested thresholds for each technique are also presented. The effectiveness of the health grade system is illustrated experimentally on three representative transformers. The consistency between the results of the proposed techniques and the actual condition of the test transformers show that the proposed techniques can distinguish transformers under different conditions.

An early degradation phenomenon identified through transformer oil database analysis

Ageing of large transformer fleets is a challenge for utilities. To assess the condition of existing transformer fleets, transformer oil is commonly tested for multiple parameters and the data are recorded in large databases for subsequent interpretation. Through analysing multiple databases including oil test results and individual transformer details pertaining to UK in-service transformers operating at primary voltage levels of 33, 132, 275 and 400 kV, population analyses revealed a generic early degradation phenomenon as indicated by an early peak in acidity and 2-FAL trends with in-service age. By exploring the phenomenon from manufacturer, loading and oil chemistry change perspectives, results suggested that the early degradation was most likely due to an oil chemistry change resulting from hydrotreatment oil refining method introduced in the late 1980s. Judging from the faster degradation trend of the affected transformers, a separate asset management strategy may be needed.