Life Expectancy Of Transformer Research Articles

Modelling and Analysis of Electromechanical Stress in Transformers Caused by Short-Circuits

A common reason for internal faults in transformers windings is the weakness insulation caused by vibration / deformation related to electromechanical forces produced by high short circuit currents. This phenomenon significantly reduces the transformer life expectancy and may even lead to its instantaneous or timing destruction. Focusing this subject this paper is aimed at presenting the performance of a time domain model inserted in a finite element program i.e. the 3D software package and the investigation of the relationship between high current levels occurring at transformer windings and the internal mechanical stresses. To highlight the overall model and the software performance, a laboratory 15 kVA transformer is utilized to show the programme facilities and potentially. The transformer has been built with concentric double-layer windings and ferromagnetic core with three columns and this equipment has been submitted to a balanced three-phase short- circuit. In addition, the computational simulations were carried out using distinct geometries to the windings i.e. with and without deformation.

Modeling and Predicting the Mechanical Behavior of Standard Insulating Kraft Paper Used in Power Transformers under Thermal Aging

The aim of this research is to predict the mechanical properties along with the behaviors of standard insulating paper used in power transformers under thermal aging. This is conducted by applying an artificial neural network (ANN) trained with a multiple regression model and a particle swarm optimization (MR-PSO) model. The aging of the paper insulation is monitored directly by the tensile strength and the degree of polymerization of the solid insulation and indirectly by chemical markers using 2-furfuraldehyde compound content in oil (2-FAL). A mathematical model is then developed to simulate the mechanical properties (degree of polymerization (DPV) and tensile index (Tidx)) of the aged insulation paper. First, the datasets obtained from experimental results are used to create the MR model, and then the optimizer method PSO is used to optimize its coefficients in order to improve the MR model. Then, an ANN method is trained using the MR-PSO to create a nonlinear correlation between the DPV and the time, temperature, and 2-FAL values. The acquired results are assessed and compared with the experimental data. The model presents almost the same behavior. In particular, it has the capability to accurately simulate the nonlinear property behavior of insulation under thermal aging with an acceptable margin of error. Since the life expectancy of power transformers is directly related to that of the insulating paper, the proposed model can be useful to maintenance planners.

Adaptive thermal model for loading of transformers in low carbon electricity distribution networks

The uptake of low carbon technologies, particularly Electric Vehicles (EVs) and Heat-Pumps (HPs), at the low voltage (LV) distribution network, in the quest of cutting down on greenhouse gas (GHG) emissions in the transportation and residential sectors, has the potential to cause general load increase and may lead to higher and longer peak load demand. This development can, as hinted in previous studies, pose a real challenge of capacity overloading to transformers at the LV distribution network of electricity system. Prolonged periods of transformer overloading could lead to premature transformer failure and shortens transformer's life expectancy. A direct solution to addressing transformer overloading is the upgrading of the transformer capacity. However, the number of LV distribution transformers in electricity system to be upgraded and the resources needed for such operation make the solution less desirable to the Distribution Network Operators (DNOs). Therefore, it is important to develop cost-effective solutions for the optimal utilization of the existing transformer capacity. Adaptive thermal loading of transformers is one of such solutions. This paper focusses on the Adaptive Thermal Loading (ATL) of transformers in LV distribution networks with considerable penetration level of EVs and HPs. The thermal model of a 500-kVA, 11/0.415-kV (no load), 50-Hz, Dyn11, ONAN mineral oil filled, free breathing, ground mounted transformer serving a real and typical urban LV network in the United Kingdom (UK) is developed based on IEC 60,076–7:2005 standard and used as the case study. A method of adaptive thermal loading of the transformer is presented to examine its capacity performance when serving the future load of the LV network following the integration of projected uptake figures of EVs and HPs for the years 2020, 2030, 2040 and 2050 into the network. Given the load and temperature forecasts of a day, the method aims at optimizing, considering the real and present conditions of the operating environment, the overall daily transformer capacity utilization that gives maximum daily return on investment without undermining reliability of supply and normal life expectancy of the transformer. Results show improved performances of the transformer when the adaptive thermal loading method is used.

Finite-time parameter estimation for an online monitoring of transformer: A system identification perspective

The effective online monitoring of thermal performance is the principal criterion that determines the Loss-of-Life (LoL), ageing rate, and loading capability of the transformer. To assess transformer thermal performance and life expectancy, Top-oil Temperature (TOT) and Hot spot Temperature (HST) should be accurately estimated. In general, the first-principle approach with a thermal-electrical analogy is used for the first-order dynamical model based on the Resistance-Capacitance (RC) circuit structure to approximate the evolution of thermal performance. Traditionally, the TOT model parameters are identified by looking for the input-output data that minimize the error between estimated and actual values. The Gradient Estimator (GE) based on the least-square minimization principle ensures the parameter convergence to their actual value and the parametric estimation error to zero only when the regressor (information vector) signals fulfill the stringent Persistence of Excitation (PE) condition. As the choice of input-output data plays crucial role in parameter estimation, the Design of Experiment (DoE) is generally conducted in the laboratory to satisfy the PE condition. Therefore, the TOT model parameter estimation problem is reformulated from a system identification perspective by exploring the finite-time estimators (FTEs) that accurately identify and estimate the TOT model parameters for the streaming data from real-time working transformers without any laboratory experiments and DoE. The experimental analysis on the sufficiency of data for parameter convergence is carried out on MATLAB to demonstrate the identified PE problem, the effect of DoE, and the performance of finite time estimators for real-time scenario based non-PE data in thermal modeling of the transformer.

Prediction of the Degree of Polymerization in Transformer Cellulose Insulation Using the Feedforward Backpropagation Artificial Neural Network

The life expectancy of power transformers is primarily determined by the integrity of the insulating oil and cellulose paper between the conductor turns, phases and phase to earth. During the course of their in-service lifetime, the solid insulating system of windings is contingent on long-standing ageing and decomposition. The decomposition of the cellulose paper insulation is strikingly grievous, as it reduces the tensile strength of the cellulose paper and can trigger premature failure. The latter can trigger premature failure, and to realize at which point during the operational life this may occur is a daunting task. Various methods of estimating the DP have been proposed in the literature; however, these methods yield different results, making it difficult to accurately estimate a reliable DP. In this work, a novel approach based on the Feedforward Backpropagation Artificial Neural Network has been proposed to predict the amount of DP in transformer cellulose insulation. Presently, no ANN model has been proposed to predict the remaining DP using 2FAL concentration. A databank comprising 100 data sets—70 for training and 30 for testing—is used to develop the proposed ANN using 2-furaldehyde (2FAL) as an input and DP as an output. The proposed model yields a correlation coefficient of 0.958 for training, 0.915 for validation, 0.996 for testing and an overall correlation of 0.958 for the model.

Reliability calculations based on an enhanced transformer life expectancy model

In this paper, The IEEE thermal ageing model is utilized to build an enhanced Transformer Life Expectancy (TLE) model. The enhanced model is developed relying on the current transformer Health Index (HI) in addition to the loading conditions. A direct correlation between the IEEE and the (HI) ageing factors is identified and projected. The model is capable of integrating factors affecting transformers. Transformer faults and maintenance are amongst factors taken into consideration to improve the model. The reliability calculations are applied for assessing the transformers as well as the distribution network. The calculated transformer (HI) score with the aid of historical failure rate are utilized during the calculation of the distribution network reliability. An application on a simple network connectivity including transformers and loads is demonstrated to verify the strategy effectiveness. The results of this research are the basis for an enhancement for the smart grids performance estimation.

A REVIEW PAPER ON DESIGN OF COOLING SYSTEM FOR ELECTRICAL TRANSFORMERS, CIRCUIT BREAKERS USED IN TRANSMISSION STATION

Transformer is a device that transfers electric energy from one alternating-current circuit to one or more circuits, either increasing (stepping up) or reducing (stepping down) the voltage. Transformer plays a key role in the power transmission, during this power transmission heat is generated. Cooling of a transformer is the dissipation of heat developed in the transformer to the surroundings. The losses occurring in the transformer are converted into heat which increases the temperature of the windings and the core. In order to dissipate the heat generated cooling should be used. A circuit breaker is an electrical switch that turns off automatically during an overload or short circuit. These switches are important because they protect your electrical components from overheating and breaking. By using different cooling methods we design an cooling system for the transformer and circuit breaker by using Revit software. By using this method, the temperature of transformer and circuit breakers can be reduced in short time. According to generally adopted estimate states that if a transformer’s maximum operating temperature is reduced by 6°C-10°C. The main purpose of the experiment is to improve the cooling system of transformer, to increase the life expectancy of transformer.

Evaluating the Effects of Demand Response Programs on Life Expectancy of Distribution Transformers

This paper presents a new method to investigate the effects of demand response programs on the life expectancy of distribution transformers. The proposed method has been applied on a realistic distribution network, and the results are evaluated under various models of the demand response program and different levels of tariffs. According to the results, distribution transformers’ life extension under various scenarios of applying demand response programs, in spite of differences among them, brings a great economic benefit. The results show a significant life extension in the interval of about 9 to 33 years. Also, this life extension brings a considerable benefit between 624.91$ to 821.669$, per year. However, the amount of obtained benefit considerably depends on the model of the demand response program and the level of tariffs. Besides, an economic analysis from both utility’s and customer’s perspectives is carried out in order to determine the optimal demand response model. To do this, an economic index is presented, and the best solution is determined by an Analytical Hierarchy Process so that it can satisfy both utilities and customers. As revealed by the results, the total annual benefits of the utility and customers are increased by 762.64$ and 73.85$.

A framework for day-ahead optimal charging scheduling of electric vehicles providing route mapping: Kowloon case study

With the ever-increasing growth of electric vehicles (EV)s in the power industry, their significance as a flexible load has increased drastically. On the other hand, uncontrolled charging of these vehicles can cause serious problems in the grid, such as a peak in demand, a decrease in the life expectancy of transformers and as a result, an increase in charging costs of EVs for the EV owners. In this paper, a framework for day-ahead optimal charging of EVs is proposed which through optimization of active and reactive power exchange at each time interval, could prevent the problems mentioned above and at the same time increase the benefit of EV owners and network operators simultaneously. Furthermore, taking into account the effective factors on electrical energy consumption of EVs and the driving pattern of their owners, a route mapping algorithm is developed based on the proposed framework, so as to provide the EV owners with better services. The simulations are carried out using a hybrid interior-point optimization approach, based on traffic and geographic data collected from the city of Kowloon and a standard IEEE 33 bus system is used. The simulation results show that integrating optimal charging of EVs with a route mapping algorithm into the proposed framework can reduce the loss costs of the network during the hours of EVs’ presence in the framework and the selling price of electricity to EV owners by 24.93% and 33.6%, respectively in comparison with the uncontrolled mode. Also, the average life expectancy of power transformers is increased by 2.97% in the optimal charging mode compared to the uncontrolled mode.

A Coupled, Semi-Numerical Model for Thermal Analysis of Medium Frequency Transformer

Medium-frequency (MF) transformer has gained much popularity in power conversion systems. Temperature control is a paramount concern, as the unexpected high temperature declines the safety and life expectancy of transformer. The scrutiny of losses and thermal-fluid behavior are thereby critical for the design of MF transformers. This paper proposes a coupled, semi-numerical model for electromagnetic and thermal-fluid analysis of MF oil natural air natural (ONAN) transformer. An analytical model that is based on spatial distribution of flux density and AC factor is exploited to calculate the system losses, while the thermal-hydraulic behavior is modelled numerically leveraging the computational fluid dynamics (CFD) method. A close-loop iterative framework is formulated by coupling the analytical model-based electromagnetic analysis and CFD-based thermal-fluid analysis to address the temperature dependence. Experiments are performed on two transformer prototypes with different conductor types and physical geometries for validation purpose. Results suggest that the proposed model can accurately model the AC effects, losses, and the temperature rises at different system components. The proposed model is computationally more efficient than the full numerical method but it reserves accurate thermal-hydraulic characterization, thus it is promising for engineering utilization.

A Novel method to estimate Economic Replacing Time of Transformer Using Monte Carlo Algorithm and ANN

A hybrid method for developing a more principled approach is presented to determine the life expectancy of transformers. The approach is constructed on an economic analysis of the transformers operational characteristics in combination  with the technical issues incorporated in the decision process. In this method, firstly life time of transformer is estimated using a hybrid method based on Monte Carlo algorithm and artificial neural network. Also Pareto distribution function is applied to consider health history of transformer and uncertainty in DP behavior of transformer. In the next step, a method is proposed in order to estimate economic replacement time of transformer. This method is based on the well-known bathtub failure model, containing repairs and scheduled maintenance, in order to achieve at a more economically aim  replacing decision. This aim is obtained in part by considering the uncertainty intrinsic in transformer failures and the corresponding discontinuations in power. In essence, this method organizes a decision support system for determination the life expectancy of a transformer. Simulation results show the high accuracy and functionality of the proposed approach in estimating economic replacing time of the Transformer.

Thermal management of a distribution transformer: An optimization study of the cooling system using CFD and response surface methodology

In this paper, a numerical scheme has been developed to examine the effective parameters on thermal management of distribution transformers and subsequently to optimize their cooling systems. In this regard, the response surface methodology (RSM) was used as the optimization method to minimize the hotspot temperature in the transformer as the response factor. A comprehensive three-dimensional computational scheme was employed considering the detailed geometrical specifications of an actual 200kVA distribution transformer to obtain the temperature field and the hotspot temperature. The accuracy of the numerical model was established via comparing the numerical results with the measured temperatures of a running transformer. The thermal variations of the thermo-physical properties of the transformer oil are determined experimentally and incorporated in the numerical modeling. A comprehensive parametric study among seven evidently effective parameters has been performed to identify the most effective parameters on the thermal performance of the transformer. It was found that fin height, length, and spacing are the more influential parameters among the examined parameters, which are also considered as the input variables in the optimization procedure. According to the RSM, the effects of the variations of these input variables in pre-specified ranges on the response, which is the hotspot temperature, are examined through the suggested runs by RSM. The results indicated that the hotspot temperature is more influenced by the fin height as compared to the fin length and spacing. Furthermore, the hotspot temperature decreases with the increase in fin height and length, while decreases as fin spacing increases. In addition, a correlation for the variations of the hotspot temperature as a function of the fin height (H), length (L), and spacing (S) is suggested using RSM. The significant finding is that the proposed optimum transformer configuration (H = 0.9 m, L = 0.08 m and S = 0.036 m) leads to the hotspot temperature reduction of about 16 °C as compared to the actual transformer geometry, which greatly affects the transformer life expectancy and safer performance.

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.

Potential of determining moisture content in mineral insulating oil by fourier transform infrared spectroscopy

Transformer oils are vulnerable to moisture, which can have adverse effects including a dramatic reduction in dielectric strength, accelerating the aging of the solid insulation (cellulose depolymerization) and the formation of decompositional gasses at high temperatures. Moisture may be generated inside the transformer due to degradation of the oil-paper insulation or there may be ingress of moisture due to free-breathing arrangements. A moisture content exceeding 50% of oil saturation greatly reduces the breakdown voltage, resulting in catastrophic failures as well as the potential for transformer fires [1]. The detrimental effect of water within a transformer is not only limited to the breakdown voltage of the oil. The rate of degradation of paper insulation increases in direct proportion to the water content [2], [3]. Moisture also has a decisive effect on the maximum temperature that the transformer can run at before moisture-induced failure occurs. Consequently, transformer life expectancy can be calculated from water content and temperature [3]. A failure of transformer accessories such as condenser bushings sometimes leads to a transformer failure and long-term outage. Often, moisture is a cause of explosion for service-aged bushings [4].

Data-Driven Thermal Modeling of Residential Service Transformers

Sales of privately-owned plug-in electric vehicles (PEVs) are projected to increase dramatically in coming years and their charging will impact residential service transformer loads. Transformer life expectancy is strongly related to the cumulative effects of internal winding temperatures, which are a function of loading. Thermal models exist (e.g., IEEE Standard C57.91) for predicting these internal temperatures, the most sophisticated being the Annex G model. While this model has been validated with measurements from large power transformers, small residential service transformers have been given less attention. Given increasing PEV loads, a better understanding of service transformer aging could be useful in replacement planning processes. Empirical data from this paper indicate that the Annex G model over-estimates internal temperatures in small 25 kVA 65 °C rise mineral-oil-immersed transformers. This paper presents an alternative model to Annex G by using a genetic program. Empirical results using a thermally-instrumented transformer suggest that this model is both simpler and more accurate at tracking empirical transformer data. We conclude that one can use a simple thermal model in combination with data from advanced metering infrastructure to more accurately estimate service transformer lifetimes, and thus better plan for transformer replacement.

Development of a Low-Cost Self-Diagnostic Module for Oil-Immerse Forced-Air Cooling Transformers

Fault detection, fault prognosis, and life expectancy estimation of transformers are important issues in improving the reliability of smart grids. Regular maintenance checks can detect the transformer's faulty conditions; however, such checks can only be performed limited times annually due to high cost and disruption of service. Therefore, faults that occur between such checks take a long time to be detected. This paper proposes a simple online monitoring algorithm that uses a minimum set of sensor feedback to estimate oil-immersed forced-air cooling transformer's life expectancy parameters. Abrupt changes or sufficient deviations of these estimations from their nominal values can be used as an indicator of transformer fault. The algorithm can also estimate the transformer-life expectancy during normal operation. A transformer-monitoring prototype has been developed based on the proposed algorithm. The transformer-monitoring prototype that uses wireless communication capability to transmit transformer life expectancy parameters to the substation has been tested, verified with lab experiments, and deployed to a utility substation.

Impacts of high penetration level of fully electric vehicles charging loads on the thermal ageing of power transformers

Abstract This paper develops a methodology to determine the impacts of high penetration level of fully electric vehicles (FEVs) charging loads on the thermal ageing of power distribution transformers. The method proposed in this paper is stochastically formulated by modelling the transformer life consumption due to FEVs charging loads as a function of ambient temperature, start time of FEVs charging, initial state-of-charge and charging modes. FEVs loads are modelled using the results from an analytical solution that predicts a cluster of FEVs chargers. A UK generic LV distribution network model and real load demand data are used to simulate FEVs’ impacts on the thermal ageing of LV power distribution transformers. Results show that the ambient temperature, FEVs penetration level, and start time of charging are the main factors that affect the transformer life expectancy. It was concluded that the smart charging scenario generally shows the best outcome from the loss of life reduction perspective. Meanwhile, public charging which shifts a large percentage of charging load to commercial and industrial areas can significantly alleviate the residential transformer loading thus has little impact on the loss of life of transformers. The proposed method in this paper can be easily applied to the determination of the optimum charging time as a function of existing loads, and ambient temperature.

Development Of GUI Based Programme For Thermal Characterisation And Life Expectancy Of Transformer Using Matlab

Transformers represent the largest portion of capital investment in transmission and distribution substations. In addition, power transformer outages have a considerable economic impact on the operation of an electrical network. The most important parameters governing a transformer's life expectancy is the hot-spot temperature (HST) value and loss of life. Here in this paper by using graphical user interface (GUI) programme we are going to find the hot-spot temperature (HST) with and without harmonics, top oil temperature (TOT) with and without harmonics, efficiency and loss of life. This approach has been made to consider the hot-spot temperature as the sum of the ambient temperature, the top-oil temperature rise in tank. With MATLAB-GUI we are finding the (TOT), (HST), Efficiency and loss of life. This paper is applied on a 220KVA, 20KV, ONAF type

Distribution transformer cooling system improvement by innovative tank panel geometries

Increase of temperature rise and hot spot values affects dramatically transformer aging and life expectancy. The present article investigates several improved designs of ONAN transformer cooling system by means of advanced numerical heat transfer-fluid flow model. Novel tank designs are examined in conjunction with other crucial parameters, as the number and location of the winding cooling ducts, so as to define the best geometry that ensures maximum efficiency of the transformer cooling system, with the aim of expanding transformer lifetime.

Intelligent Diagnostic Method for Ageing Analysis of Transformer

The fuzzy method is proposed for ageing analysis of transformer. The fuzzy controller is used for various input and one output, strictly depends on the number of membership function and there rule base and the type of the defuzzification method. The ageing of transformers is influenced by short term and long term over loads, number and intensity of short circuits, incidence of lightning, and internal faults. The recent development of various techniques for detecting the incipient fault conditions have been improved to some extent; the life expectancy of transformers by resorting to corrective actions in time. The ageing behavior is likely to be different for different types of transformers. The life span of the transformer, thus depends initially on the design and quality of manufacture and later on service conditions and maintenance standard, these factors vary considerably and affect the useful span of service life which therefore needs to be taken into account for residual life assessment. During the natural ageing of transformers, the insulation of winding deteriorates. Cellulose insulation degrades due to heating or electrical breakdown which is dissolved in oil. Hence, the chemical analysis of the Transformer oil gives evidence of changes that are taking place in the winding insulation during operation. Deterioration in transformer cellulose decreases both its electrical and mechanical strength. In this paper a novel fuzzy based algorithm has been implemented on three samples of power transformer oil.