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Case Study - Calculation of DGA Limit Values and Sampling Interval in Power Transformers
Abstract
The predictive maintenance in power transformers aims to manage the risk of the asset. This is achieved through the calculation and control of the health index of the power transformers. A very important parameter for calculating the health index in power transformers is the dissolved gas analysis (DGA). The current trend is online DGA monitoring, in addition to continuing to perform analyzes in the laboratory. In spite of the fact the DGA is well known, there is a lack of real data outside the guides. This case study uses a method for calculating the limits of the gas levels and of the annual increase in the concentration of combustible gases, in order to establish the optimum sampling interval and the alarm limits of the continuous monitoring equipment for each power transformer.
Index Terms - asset management, dissolved gas analysis, maintenance management, oil insulation, power transformers, predictive maintenance.
... Achieving a successful DGA program is a critical element; to achieve this, it is necessary to establish the appropriate alarm limits together with appropriate actions in the event of an alarm. This paper updates a previous work [18] by applying the new IEEE guide [19]. In this study, the typical gas concentration values and the typical rates of gas increase for 195 transformers that are in service in the north of Spain were calculated based on the DGA results obtained in the laboratory to determine an optimal sampling interval for each transformer. ...
... In this study, the typical gas concentration values and the typical rates of gas increase for 195 transformers that are in service in the north of Spain were calculated based on the DGA results obtained in the laboratory to determine an optimal sampling interval for each transformer. The improvements over the previous work [18] were that the number of DGA samples used in this study was increased by 50, the results were compared with the new limits established in the IEEE guide [19] and the acetylene concentrations used to calculate their typical concentrations and increases were separated into two groups in a similar way as indicated in the IEC guide [15]. The first group included the DGA samples from transformers without OLTC or without communicating OLTC, while the second group consisted of the DGA samples from transformers with communicating OLTC. ...
... This paper updates the results of a case study [18] to which the information in the new IEEE guide was applied [19]. This study presents the application and validation of the nearest-rank method to calculate the 90th and 95th percentiles of the typical gas concentration values and theoretical DGA sampling intervals for the transformers of a DSO. ...
Predictive maintenance strategies in power transformers aim to assess the risk through the calculation and monitoring of the health index of the power transformers. The parameter most used in predictive maintenance and to calculate the health index of power transformers is the dissolved gas analysis (DGA). The current tendency is the use of online DGA monitoring equipment while continuing to perform analyses in the laboratory. Although the DGA is well known, there is a lack of published experimental data beyond that in the guides. This study used the nearest-rank method for obtaining the typical gas concentration values and the typical rates of gas increase from a transformer population to establish the optimal sampling interval and alarm thresholds of the continuous monitoring devices for each power transformer. The percentiles calculated by the nearest-rank method were within the ranges of the percentiles obtained using the R software, so this simple method was validated for this study. The results obtained show that the calculated concentration limits are within the range of or very close to those proposed in IEEE C57.104-2019 and IEC 60599:2015. The sampling intervals calculated for each transformer were not correct in all cases since the trend of the historical DGA samples modified the severity of the calculated intervals.
... This paper used the reference values included in the IEC standard [19] and the publication [28]. The authors have also calculated the 90th percentile changes in the study group and used them for simulations. ...
The current use of health index algorithms is mainly limited to single assessments of the unit’s condition or the device comparison. The paper focuses on the changes in the health index values between the consecutive analyses. The algorithm used for this purpose was previously developed by the authors. The test group included 359 complete oil evaluation results from 86 power transformers monitored over several years. For each outcome, the influence of the sub-components of the main score was calculated. Additional health index increase simulations were performed based on the IEC 60599 standard guidelines. The highest increases and decreases in the total score were listed and analyzed to determine the main factors behind the changes. The study has shown that the changes in dissolved gases concentrations have a much more significant influence on the health index values than the changes in physicochemical properties of the oil and furfural content. Based on the magnitude of the observed changes and the simulation outcomes, the authors have proposed two assessment thresholds—the 50th percentile health index increase within a population as an alarm zone, and the 90th or 95th percentile increase as a pre-failure zone.
... In [21], Jahromi et al. state that a Power Transformer makes up 60% of the financial investment in a high voltage substation and their price is in the hundreds of thousands of euros [22]. Their expected lifetime is about 30 years [23], but in spite of that, there are transformers operating currently that are 60 years old [24], way beyond their intended use. Because of this, the importance of PT maintenance increases, as older transformers will be more prone to failures. ...
Power Transformers are one of the main components of a power grid and their downtime impacts the entire network. Repairing their failures can also be very costly, and so sophisticated maintenance techniques are necessary. In this thesis, we present a mixed-integer optimization model that both schedules this maintenance and the transformer load in order to optimize profit. We illustrate this model by comparing three of the most common maintenance strategies, Time Based Maintenance, Condition Based Maintenance and Predictive Maintenance. We do Sensitivity Analysis based on sampling to test the robustness of the model, using Gaussian Processes to minimize the number of samples required. Furthermore, some foundations for the incorporation of Machine Learning into an improved model are also presented.
... From the classification of transformers as contaminated or uncontaminated performed in this study, the calculation results for the limit values of the C 2 H 2 concentration [39] are improved and divided into two groups, depending on the OLTC communication between the main tank and the OLTC compartment, similar to the IEC classification. ...
Power transformers are considered to be the most important assets in power substations. Thus, their maintenance is important to ensure the reliability of the power transmission and distribution system. One of the most commonly used methods for managing the maintenance and establishing the health status of power transformers is dissolved gas analysis (DGA). The presence of acetylene in the DGA results may indicate arcing or high-temperature thermal faults in the transformer. In old transformers with an on-load tap-changer (OLTC), oil or gases can be filtered from the OLTC compartment to the transformer’s main tank. This paper presents a method for determining the transformer oil contamination from the OLTC gases in a group of power transformers for a distribution system operator (DSO) based on the application of the guides and the knowledge of experts. As a result, twenty-six out of the 175 transformers studied are defined as contaminated from the OLTC gases. In addition, this paper presents a methodology based on machine learning techniques that allows the system to determine the transformer oil contamination from the DGA results. The trained model achieves an accuracy of 99.76% in identifying oil contamination.
Cutting and punching of the steel used in power transformer core may cause edge burrs. This, along with the degradation of the lamination insulation, can lead to interlaminar short circuits. Analysing these faults helps understanding their effect on the transformer reliability and performance. In this light, the actual paper aims to experimentally simulate and analyse both faults using a 15 kVA three phase power transformer. Effects produced from both selected faults are experimentally investigated in this paper where different scenarios are considered such as the area of the affected regions and the number of short-circuited laminations. Various flux densities are considered ranging from 0.5 to 1.8 T. Of interest, the current at no load is recorded and the test is repeated for any given scenario. The obtained results are presented and discussed to study the effect of each fault on the transformer performance. Overall, the transformer current increases with the number of short-circuits between laminations for both faults. This increase is related to the flux density, which is dependent and sensitive to the short circuit location. Such findings represent a good indication of the severity of short circuits relative to their position in the transformer core, and can be exploited to discuss the power losses in the transformer core.
This paper presents a review on the sources of failures of transformer in the substation. Different investigations and test analyses have been conducted to identify the root causes of failure of the transformer in the power system, and to identify the preventive measures to avoid these breakdowns. The review work has been presented with the focus on bushing-failure, which has been identified as one of the major causes of transformer breakdown in different reported studies. In determining the lifespan of the insulation of the high voltage bushing, along with different factors, moisture effect has also been considered in presenting the degradation analysis. On-line measurement has been preferred over off-line measurements due to its benefits measuring the reliability and uninterrupted functioning of the future generation, transmission and distribution systems. Post fire-outbreak, external inspection of the damaged bushing along with analytical results, obtained with the aid of Scanning Electron Microscope (SEM) and Energy Dispersive X-ray Spectroscopy (EDS) analysis, have also been included in this study. In the analysis, electric field and equipotential lines with different oil levels have been simulated using COMSOL Multiphysics software.
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.
Energy is a basic necessity in every country. The worldwide demand for energy will rise due to the developments of power generation in industrial, service, and residential sectors. A healthy power system is therefore very important to guarantee continuous electricity supply to the end users and this can be achieved through asset management. A proper asset management will allow asset managers to conduct quality assessment of conditions and to develop future management strategies of the electrical assets such as transformers. The execution of transformer asset management involves an investigation of the transformer's condition by employing Transformer's Health Index (THI). Mathematical equation/algorithm or expert judgment has been investigated by many previous studies as one of the technique to determine health index (HI). Some of the established methods of HI determination such as scoring and ranking method, tier method, matrices and multi-feature assessment model have led to the different interpretations of the final condition of a transformer. This paper critically examines and explores the previous studies related to transformer health index by using mathematical equation/algorithm or expert judgment. The concept of HI and its formulation are presented in this study. Generally, there are three parts of HI formulation which are input, algorithm for HI and the output of HI. The application of HI is discussed in terms of the performance of in-service transformer. The limitations of the available methods are also discussed and future works to overcome the problems are suggested.
Electric power transformers are the link between the generators of a power system and the transmission lines and between lines of different voltage levels. Power transformers undergo changes in their operational life expectancy and reliability over the years. Currently, several tools for diagnosis and assessment of their operational condition are available, including diagnostic techniques based on dissolved gas analysis in the insulating oil. Through monitoring of dissolved gases in oil, it is possible to perform detailed data analysis, seeking systemic failure prediction. The adoption of new technologies for maintenance of power transformers can induce substantial changes in the reliability of such equipment in view of the existence of a global trend to decrease operational costs, predict maintenances and control substations in a centralized way. This paper describes the main factors that lead to lifetime reduction in transformers and reviews the main methods used for predictive maintenance based on dissolved gas analysis. The advantages and disadvantages of each one are outlined and some future directions for research are proposed.
This paper presents a new method for calculating a health index for transformers (for operating voltage 69 kV or less) based on diagnostic tests. The method relies on the use of furan analysis, dissolved gas analysis, and other oil analysis results as a means of calculating the health index using fuzzy set theory. Real field data for 90 working transformers were used to test the proposed method. The results were compared with the results calculated for the same set of transformers by an experienced asset management and health assessment consulting company. The comparison shows that the results are highly reliable.
Power transformers have the single highest value of the equipment installed in high-voltage substations, comprising up to 60% of total investment. An increasing demand for improved financial and technical performance has pushed most power utilities to assess the actual condition of their transformers [1]-[3]. To achieve the optimal balance among capital investments, asset maintenance costs, and operating performance, there is a need to provide economic and technical justifications for engineering decisions and capital replacement plans.
Methods are presented in this paper allowing individual networks or transformer users to calculate their own specific DGA gas limits and required sampling intervals as a function of gas concentrations and rates of gas increase in service. This calculation can be done on the entire transformer population or on more specific segments of it. The purpose of this paper is only to provide a tool for calculating gas levels requiring more frequent sampling intervals for DGA. The actions to be taken on the equipment at any of these gas levels and sampling intervals (e.g., removal or not from service, additional testing, installation of on-line gas monitors) are outside the scope of this paper. These actions depend on a large number of other parameters (e.g., experience and maintenance practices of individual users, strategic importance and type of equipment used, type and location of the fault, fault active or not). The actual sampling intervals to be used also remain the decision of maintenance personnel, based on their best operational practices.
DNO Common Network Asset Indices Methodology
- Working Group
- G B Dnos
- Nie
Working Group GB DNOs and NIE, "DNO Common Network Asset
Indices Methodology," January 2017.
Mineral oil-filled electrical equipment in service -Guidance on the interpretation of dissolved and free gases analysis
Mineral oil-filled electrical equipment in service -Guidance on the
interpretation of dissolved and free gases analysis, IEC 60599:2015,
2015.
He received his BSc in Energy Resources Engineering and his MSc in Mining Engineering Specialized in Energy from University of Cantabria (UC)
Sergio Bustamante was born in Antequera, Málaga, Spain, on November
22, 1987. He received his BSc in Energy Resources Engineering and his
MSc in Mining Engineering Specialized in Energy from University of
Cantabria (UC), Spain, in 2014 and 2017, respectively. Since 2017, he is a
Ph.D. student in Industrial Engineering at University of Cantabria (UC),
Spain.
Since 2016, he works as research assistant in the Department of
Electrical and Energy Engineering in Group of Advanced Electrotechnology
Techniques (GTEA).
From 2011 to 2016, she works as research assistant in the Department of Electrical and Energy Engineering in Group of Advanced Electrotechnology Techniques (GTEA)
- Spain
Spain, in 2011 and 2012, respectively. She received her Ph.D. in Industrial
Engineering from University of Cantabria (UC), Spain, in 2016.
From 2011 to 2016, she works as research assistant in the Department of
Electrical and Energy Engineering in Group of Advanced Electrotechnology
Techniques (GTEA). In 2016, she continuous in the same department and
research group as research associate.
IEEE Guide for the Interpretation of Gases Generated in Oil-Immersed Transformers
IEEE Guide for the Interpretation of Gases Generated in Oil-Immersed Transformers, IEEE Standard C57.104-2008, 2008.
Spain, respectively. From 2010 to 2018 he was Maintenance Manager of Power Lines at Viesgo Distribución Eléctrica. Since 2018, he is Asset Maintenance Manager at Viesgo Distribución Eléctrica
- Antonio González
Antonio González was born in Santander, Cantabria, Spain, on March 3,
1970. He received his Industrial Technical Engineering at the University of
Cantabria, Spain, in 1992. In 1997 and 2016, he received his MSc in
Industrial Engineering and his Ph.D. in Industrial Engineering from the
University of Cantabria (UC), Spain, respectively.
From 2010 to 2018 he was Maintenance Manager of Power Lines at
Viesgo Distribución Eléctrica. Since 2018, he is Asset Maintenance
Manager at Viesgo Distribución Eléctrica. From 1996 to 1998 and from
2009 to 2014, he was Associate Professor in the Department of Electrical
and Energy Engineering of the University of Cantabria (UC).