Failure Rate Of Transformers Research Articles

Incorporating demand response effect on transformer replacement planning

• Long term effect of loading profile on failure rate of transformers. • Effect of DR (peak load shifting) on amount of load shed at peak hours. • Failure rate related value of transformers. • Although all power market participant benefits from DR application but the cost of DR is added to the objective function and yet it was beneficiary. Transformer replacement planning is a long-term optimization problem of power system engineering. Therefore, long-term effects as loading and ageing should be considered in the problem. The most crucial reason in such a long term problem in power system is reliability issues. Accordingly, in this paper, long-term effect of loading on transformer failure rates has been modelled and demand response (DR) has been implemented to reduce loading rate of the system. In order to optimize effectiveness of DR, its implementation cost has been added to the objective function of the problem. In addition, reducing the failure rate of existing transformer may decrease the need for a new transformer so that the reliability of the system has a desirable level. On the other hand, a decrease in existing transformer loading rate in the long run, makes them more valuable at the end of the planning horizon. Hence, the value of transformers should be embedded in the objective function. Additionally, reducing loading rate causes lower loss of load at contingencies caused by transformers from two point of view, first reduced failure rate and second reduced peak load. The developed model has been implemented on transformers of THE IEEE 24-bus reliability test system (IEEE RTS) in two case studies. Results show DR can enhance network performance technically by increasing network reliability and decreasing transformer loading and can improve network operation economically by decreasing operational and expansion costs

Failure Rate Estimation for Transformer Population based on Health Index through Markov Model Approach

This work examines the failure rate of the transformer population through the application of the Markov Model (MM) and Health Index (HI). Overall, the condition parameters data extracted from 3,192 oil samples were analysed in this study. The samples were from 370 transformers with the age range between 1 and 25 years. First, both HIs and failure rates of transformers were determined based on the condition parameters data of the oil samples known as Oil Quality Analysis (OQA), Dissolved Gas Analysis (DGA), Furanic Compounds Analysis (FCA) and age. A two-parameter exponential function model was applied to represent the relationship between the HI and failure rate. Once the failure rate state was obtained, the non-linear optimisation was used to determine the transition probability for each age band. Next, the future failure rate of the transformer population was computed through the MM prediction model. The goodness-of-fit test and Mean Absolute Percentage Error (MAPE) were utilised to determine the performance of the predicted failure rate. The current study reveals that the future state of the transformer population and failure rate could be predicted through MM based on updated transition probabilities. It is observed that the MAPE between predicted and computed failure rates is 7.3%.

Reliability evaluation of distribution transformers considering the negative and positive effects of rooftop photovoltaics

Rooftop photovoltaics (RPVs) have an impact on the ageing failure rate and reliability of transformers. The advantages and disadvantages of different penetrations of rooftop photovoltaic on the reliability of the transformers are studied. For the first time, the adverse effects of high penetration of RPVs on the transformer failure and power system reliability are examined. Regarding the growing interest in the widespread use of renewable energies on account of global warming, studying the negative effects of DG units presence is so important to predict the future of power system conditions. Monte Carlo simulation is used for the probabilistic nature of clearness index and reliability evaluation. Well-being criteria that show the different operation modes of the power system is used to identify the at-risk, marginal and healthy mode of the power system in the presence of different penetrations of rooftop photovoltaic. The results show that the maximum penetration of rooftop photovoltaic for improving the reliability of the power system is 5 kW per house in the feeder of Isin in Bandar Abbas, in Iran.

A Data Mining Approach for Transformer Failure Rate Modeling Based on Daily Oil Chromatographic Data

Evaluating the real-time failure rate of transformers can effectively guide the planning of maintenance and reduce their failure risk. This paper proposed a novel transformer failure rate model that considers the impact of maintenance based on daily oil chromatographic monitoring data mining. Firstly, to ensure the quality of the modeling data, an improved k-nearest neighbor (KNN) algorithm based on genetic algorithm (GA) is proposed to repair the missing monitoring data. The repaired data is then mapped to the equivalent state duration (ESD) by the M-BPNN proposed, which is used to modify the multistate Markov process of transformers so as to quantify the impact of maintenance on failure rate. Considering the changing characteristics of the dissolved gases’ content in the short period, the ESD is further merged in sequential periods to obtain the merged equivalent state duration (MESD). Finally, an analytical function of the transformer failure rate with respect to the MESD is obtained. Case studies on a typical substation demonstrate that the proposed approach has the ability to characterize the impact of maintenance and the actual failure rate, thereby improving the accuracy of the substation reliability assessment.

Three‐dimensional thermal modelling of transformers in transformer room for spatial and temporal failure analysis

Temperature is a key factor for failure analysis of power transformers. Conventionally, transformer failure rate is calculated with hot spot temperature induced from IEEE empirical equations. This article firstly introduces a spatial and temporal related failure model based on three-dimensional thermal simulations of transformer and the related environment. The proposed thermal model is established with computational fluid dynamics for ventilation calculation and heat generation equations for power device simulation. Cooling strategies and mutual heating effect of power equipment are considered for an accurate temperature distribution prediction. By incorporating the three-dimensional thermal model into the service life-dependent and temperature-dependent model, the failure rate of each spatial point in power transformer could be calculated according to Arrhenius theory and Weibull distribution. The simulation results show that the proposed model clearly improves the accuracy of failure analysis and can be used for thermal and ventilation design of transformer room.

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.

Optimal Economic Life Interval Evaluation Method of Transformers

This paper proposed a method of optimal economic life interval evaluation of transformer, thus providing scientific support for power system planning and reconstruction. First, obtaining the transformer failure rate by using Levenberg-Marquardt based least squares algorithm on the consumption that transformer failure mode meets three-parameters Weibull distribution. Second, calculating operation cost and failure cost of transformer during its life cycle on the basis of transformer failure rate. Then, acquiring the optimal economic life interval of transformer through life cycle minimal average annual cost based on the theory of interval analysis. Finally, the proposed method is proved to be effective by applying to some real transformers.

A Transformer Replacement Decision Method Based on Probability Assessment of Failure Rate

Reasonable probability assessment of transformer failure rate (FR) is a critical reference to the transformer replacement work. At present, the lack of support theory for transformer replacement usually causes reliability and economy issues for power companies. For this reason, a transformer replacement decision method based on probability assessment of FR is proposed. Firstly, a first order model of transformer paper degradation is proposed. Then, the Weibull Distribution is used by Monte Carlo Simulation (MCS) to generate the variations of Degree of Polymerization (DP) along with time based on the historical data, and the transformer FR is determined. When the FR is higher than a pre-defined threshold value, the transformer should be replaced for reliability purpose. Finally, the effectiveness of the proposed method for the transformer replacement decision is verified by a typical engineering application.

Analysis of Transformer Loadings and Failure Rate in Onitsha Electricity Distribution Network

This study investigated transformer loadings and failure rate in the Onitsha Electricity Distribution Network by using the Electrical Transient Analysis Program (ETAP) software 12.6 and the Statistical Package for the Social Sciences (SPSS) software 16.0. Data collected over the period 2011-2015 on the distribution network were simulated on ETAP software using the Newton-Raphson (N-R) technique to determine the transformer loadings while responses to 350 copies of questionnaire distributed among the technical staff were statistically analysed on the SPSS software to ascertain the failure rate among transformers in the network. The findings of the study show that during the 5 years period covered by the study, the sampled substations recorded transformer average failure rate of 11.7 %. It was further revealed that besides insulation issues which accounted for 24.2% of all the failures, overload (22.5%) was the next major cause of transformer breakdowns in the distribution network. The study recommends installation of more transformer units, use of high quality transformers, balanced loading of the transformers and proactive inspection and maintenance program of transformers units within the network. The outcome of this work would help electricity utilities provide more reliable and cost effective services to customers.

Optimal Capacity Management of Substation Transformers Over Long-Run

The management of transformers' capacity is a vital task for planners and asset managers due to their high cost. This paper proposes an optimization model for capacity management of transformers in a substation over long-run. The model considers the present worth costs of investment, losses, maintenance, reliability, and the salvage value of transformers for providing the optimal selection and scheduling of multistage transformer installations and their refurbishments. In the optimization model, growing failure rate of transformers along with their age is incorporated and the cumulative loss-of-life (LOL) of transformers is also used in determining their salvage value. The developed model is applied for planning and management of transformer capacities for a residential load dominant two-transformer primary distribution substation over a period of 40 years. The simulations are performed for various case studies representing the situations encountered by utilities. An extensive sensitivity analysis is also conducted for several conditions of the system parameters. The numerical results indicate the worth of inclusion of variable failure rate and LOL of transformers in their capacity management.

Modeling of Aging Transformer Failure Rate and the Retirement Timing Decision

When making an updating strategy for aging transformer, both the condition of transformer and the risk effect of its aging on power system needed to be taken into account. Based on the model of aging transformer failure rate by insulation age, a comparative analysis of the risk caused by aging failure within the planned period and the profit from delaying updating was undertaken; then the optimal timing strategy to retire transformer was developed on the basis of a balance between risk and profit. The analysis of examples indicated that the proposed process of the strategy could be applied to plan the retiring time according to both the actual condition of transformer and the impact of the risk to the grid, thus making full use of transformer and allowing the power company to get the maximum benefit.

The Maintenance Strategy for Optimizing Distribution Transformer Life Cycle Cost

During the operation, the distribution transformer performance will deteriorate. The maintenance is an essential task in enhancing the safety of distribution transformer. Based on equal deterioration theory, the relationship among distribution transformer failure rate in each period of maintenance was deduced. After analyzing the maintenance costs and the failure costs in detail, the model of optimal life cycle costs for distribution transformer was established. Then the genetic algorithm was used to solve the model for obtaining the optimal maintenance strategy. Finally, the optimization results and the influence factors were analyzed. The results demonstrate the feasibility and validity of the proposed method. DOI: http://dx.doi.org/10.11591/telkomnika.v11i10.3458

An accurate online calibration system based on combined clamp-shape coil for high voltage electronic current transformers

Electronic transformers are widely used in power systems because of their wide bandwidth and good transient performance. However, as an emerging technology, the failure rate of electronic transformers is higher than that of traditional transformers. As a result, the calibration period needs to be shortened. Traditional calibration methods require the power of transmission line be cut off, which results in complicated operation and power off loss. This paper proposes an online calibration system which can calibrate electronic current transformers without power off. In this work, the high accuracy standard current transformer and online operation method are the key techniques. Based on the clamp-shape iron-core coil and clamp-shape air-core coil, a combined clamp-shape coil is designed as the standard current transformer. By analyzing the output characteristics of the two coils, the combined clamp-shape coil can achieve verification of the accuracy. So the accuracy of the online calibration system can be guaranteed. Moreover, by employing the earth potential working method and using two insulating rods to connect the combined clamp-shape coil to the high voltage bus, the operation becomes simple and safe. Tests in China National Center for High Voltage Measurement and field experiments show that the proposed system has a high accuracy of up to 0.05 class.

Outage data analysis of utility power transformers based on outage reports during 2002–2009

Statistical analysis of failures and forced outages of power transformers constitute an important basis for asset management of these transformers. Results of the statistical analysis can be used, for example, to enhance utility reliability, influence transformer design and technology, and improve maintenance and condition monitoring practices. In addition, various methods for transformer reliability evaluation require that the expected values of component outage rates, outage durations, and repair durations be known. In this paper, outage data are obtained from the Egyptian Electricity Transmission Company (EETC). This work presents outage data analysis over eight years, from 2002 to 2009, for 1922 (average number) transformers in voltage populations ranging from 33kV to 500kV and MVA rating from 5MVA to 500MVA. Forced outages due to correct and false action of transformer’s protection systems are carefully considered. Outage data analysis is conducted according to two basic phases. In the first phase, failure and repair analysis of transformers is performed while in the second phase impact of transformer outages on customers is assessed. Percentage average number of failures (%AANF) and annual average repair time (AART) per transformer are used to represent the failure and repair data of power transformers. Two indicators are used to represent the impact of transformer outages on customer interruptions. These indicators are the annual average interrupted MW (AAIMW) and annual average customer-interruption duration (AACID). A summary of the main outcomes of the work presented in this paper is provided in the conclusions’ section; however, it is worthy to be mentioned here that the fire-fighting systems are responsible for the highest number of false trips in all voltage subpopulations except the 220kV subpopulation where the dominant cause of false trips is the busbar protection. Therefore, it is recommended to improve the maintenance and design of this protection equipment to reduce the failure rate of power transformers.

A Two-Dimensional Cloud Model for Condition Assessment of HVDC Converter Transformers

Converter transformers are the key and the most important components in high voltage direct current (HVDC) power transmission systems. Statistics show that the failure rate of HVDC converter transformers is approximately twice of that of transformers in AC power systems. This paper presents an approach integrated with a two-dimensional cloud model and an entropy-based weight model to evaluate the condition of HVDC converter transformers. The integrated approach can describe the complexity of HVDC converter transformers and achieve an effective assessment of their condition. Data from electrical testing, DGA, oil testing, and visual inspection were chosen to form the double-level assessment index system. Analysis results show that the integrated approach is capable of providing a relevant and effective assessment which in turn, provides valuable information for the maintenance of HVDC converter transformers.

A computational tool to assist the analysis of the transformer behavior related to lightning

This paper proposes the application of computational intelligence techniques to assist complex problems concerning lightning in transformers. In order to estimate the currents related to lightning in a transformer, a neural tool is presented. ATP has generated the training vectors. The input variables used in Artificial Neural Networks (ANN) were the wave front time, the wave tail time, the voltage variation rate and the output variable is the maximum current in the secondary of the transformer. These parameters can define the behavior and severity of lightning. Based on these concepts and from the results obtained, it can be verified that the overvoltages at the secondary of transformer are also affected by the discharge waveform in a similar way to the primary side. By using the tool developed, the high voltage process in the distribution transformers can be mapped and estimated with more precision aiding the transformer project process, minimizing empirics and evaluation errors, and contributing to minimize the failure rate of transformers.

Magnetising inrush current in transformers: an overview

In India, the failure rate of distribution transformers has been reported very high especially in the rural and remote areas. In these areas, frequent inrush current is produced in distribution transformers during their energisation. The generation of inrush current during each energisation may be dangerous for health of the distribution transformers. This has motivated the authors for extensive study on inrush current such as causes, modelling, effects and reduction techniques. A case study has also been given on role of creep in the windings due to inrush current as one of the reasons for failure of distribution transformers.

Reliability assessment of 3-phase three winding converter transformer of HVDC transmission system

High voltage direct current (HVDC) is today recognized as an effective and efficient means of transmitting bulk power over long distances through over head lines or cables. The reliability of HVDC links have always been of primary concern in planning and operating of power systems. The existence of spares and maintenance practices do affect the availability of HVDC stations. The HVDC converter transformer plays a relevant roll in HVDC converter station, not only due to the direct cost associated to them but also causes its availability affects the HVDC transmission capability (or) capacity. In this paper the effect of converter transformer failure rate, repair time on expected capacity level of three phase three winding converter transformer with and without spares is analyzed and compared. It also effect of number of spares on expected capacity level is presented. Key words: HVDC transmission system, converter transformer, unavailability, Morkov modeling.

The Electromagnetic Compatibility Research Group: research questions

This paper summarises the Universidad Nacional de Colombia’s Electrical and Electronic Engineering Department’s Electromagnetic Compatibility Research Group (EMC-UNC) activities during the last 30 years. The group was involved in developing experimental tools during the early 1980s, such as constructing high-voltage apparatus, developing high-voltage practical work for students and observing electrical discharges. These tools enabled the group to spend a decade focused on resolving one of the Colombian electrical sector’s main EMC problems: distribution transformer’s failures caused by lightning. For almost a decade this investigation was focused on understanding the causes of the extremely high failure index in Colombian rural areas, especially in the Rionegro basin. The main result of this investigation was a reduction by one order of magnitude in mean 10% distribution transformer failure rate. During this research work a noticeable pattern was observed of several electrically-isolated metallic bodies immersed in an electric field (i.e. floating electrodes). This was led to initiating floating electrode studies and formulating a new scientific question, “How do corona electrical discharges interact with floating electrodes?” This new research question was dealt with during the second half of the 1990s and the first decade of the 2000s. This investigation was related to using electrostatically-accumulated charge on a floating electrode. This question opened up four research areas: gas discharge physics, generating fast current impulses, harvesting energy from the electric field and the possibility of high impedance current sources. This paper has summarised the most relevant work done by the EMC-UNC group on these topics. This floating electrode research work started by formulating four patents. Fresh research questions for the 2010s were related to measuring lightning electromagnetic pulses (LEMP), intentional electromagnetic interference (IEMI) studies, measuring picoseconds-rise-time impulses, cleaning water by corona discharge and harvesting, accumulating and using small amounts of energy extracted from electric and electromagnetic fields. The future of the EMC-UNC group is closely related to interaction with other groups.

A Neural Approach to Evaluate the Effect of Lightning in Power Transformers

This paper proposes the application of computational intelligence techniques to assist complex problems concerning lightning in transformers. In order to estimate the currents related to lightning in a transformer, a neural tool is presented. ATP has generated the training vectors. The input variables used in Artificial Neural Networks (ANN) were the wave front time, the wave tail time, the voltage variation rate and the output variable is the maximum current in the secondary of the transformer. These parameters can define the behavior and severity of lightning. Based on these concepts and from the results obtained, it can be verified that the overvoltages at the secondary of transformer are also affected by the discharge waveform in a similar way to the primary side. By using the tool developed, the high voltage process in the distribution transformers can be mapped and estimated with more precision aiding the transformer project process, minimizing empirics and evaluation errors, and contributing to minimize the failure rate of transformers.