Determination of Hot-Spot Temperature for ONAN Distribution Transformers with Dynamic Thermal Modelling

Sönmez, Oluş; Kömürgöz, Güven

doi:10.1109/cmd.2018.8535752

Cited by 10 publications

(5 citation statements)

References 17 publications

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“…These data are then used with IEEE Standard C57.91-2011, which provides a method of determining the impact of load on transformer life and is [24], to advise on the number of services per transformer based on economic criteria. Reference [25] explains and compares different methods of determining transformer aging. Reference [26] proposes using a Cumulative Moving Average (CMA) with sensory data and IEEE Standard C57.91-2011 to continuously determine the remaining life of a transformer under various weather conditions.…”

Section: Load-related Transformer Failure Eventsmentioning

confidence: 99%

A Stochastic Load Forecasting Approach to Prevent Transformer Failures and Power Quality Issues Amid the Evolving Electrical Demands Facing Utilities

O’Donnell,

2023

Energies

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New technologies, such as electric vehicles, rooftop solar, and behind-the-meter storage, will lead to increased variation in electrical load, and the location and time of the penetration of these technologies are uncertain. Power quality, reliability, and protection issues can be the result if electric utilities do not consider the probability of load scenarios that have not yet occurred. The authors’ approach to addressing these concerns started with collecting the electrical load data for an expansive and diverse set of distribution transformers. This provided approximately two-and-a-half years of data that were used to develop new methods that will enable engineers to address emerging issues. The efficacy of the methods was then assessed with a real-world test dataset that was not used in the development of the new methods. This resulted in an approach to efficiently generate stochastic electrical load forecasts for elements of distribution circuits. Methods are also described that use those forecasts for engineering analysis that predict the likelihood of distribution transformer failures and power quality events. 100% of the transformers identified as most likely to fail either did fail or identified a data correction opportunity. The accuracy of the power quality results was 92% while allowing for a balance between measures of efficiency and customer satisfaction.

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Section: Load-related Transformer Failure Eventsmentioning

confidence: 99%

A Stochastic Load Forecasting Approach to Prevent Transformer Failures and Power Quality Issues Amid the Evolving Electrical Demands Facing Utilities

O’Donnell,

2023

Energies

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“…There are two methods to determine the TOT. The first is equivalent circuit thermal modelling provided in IEEE C57.91 and IEC 60076-7, which are costly in online monitoring [3][4][5] and the second is computational fluid dynamics (CFD) methods, which have a high computational burden [6]. Accordingly, different approaches have been proposed based on these methods [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

A new approach for top‐oil thermal modelling of power transformers using Unscented Kalman filter considering IEEE C57.91 standard

Bagheri

Allahbakhshi

Arefi

et al. 2022

IET Electric Power Appl

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Determining the transformer top-oil temperature (TOT) is one of the key issues in determining the transformer insulation life and reliability of the power system. Due to the non-linear nature of the model presented in the IEEE C57.91 standard to determine this temperature, a more precise method is needed to estimate the equation coefficients to estimate the TOT in the future. This paper presents a method for online thermal modelling of the transformer according to the IEEE C57.91 based on the Unscented Kalman filter (UKF). This method can be applied to transformers with a variety of cooling modes and estimates the TOT with an acceptable error. In order to evaluate the proposed method, the practical data of the 800 kVA distribution transformer with unknown equation coefficients and simulated data with known coefficients are used, and finally, by calculating the estimation error, the proper performance of the presented method is proved. It is proved that the proposed method predicts TOT even in the presence of noise with an error of less than 0.5°C and a delay of less than 1.5 h. It makes the proposed method can be implemented for purposes such as load management, and insulation life estimation of the transformer.

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“…Energies 2021, 14, 6072 2 of 15 One of the most important pieces of equipment with a large number installed along the electricity distribution grid is the distribution transformer. The data of the operation status, such as current, voltage, temperature, and other measurements derived from these quantities, related to this equipment helps the utility to provide a distribution grid with a safer and more reliable operation [1,[5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…The distribution grid has a level considered acceptable by the utility and the transformer, which allows the equipment to remain operating above its nominal power for a certain period of time [5,9]. If the overload reaches a level above what is allowed or if it lasts longer than expected, this will cause irreversible damage to the internal and essential components of the distribution transformer [1,[5][6][7]10,11].…”

Section: Introductionmentioning

confidence: 99%

Application Study in the Field of Solutions for the Monitoring Distribution Transformers of the Overhead Power Grid

Chaves

Martins

Martins³

et al. 2021

Energies

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This paper will present the development and application of overhead transformers of the distribution grid, as well as the different technologies used for the gathering of electrical quantities, in the field of a monitoring solution. The solution reports the values of the quantities and alarms of operation remotely and almost in real-time. The development of this solution seeks to provide an electrical grid with greater reliability, proposing the reduction of KPIs (Key Performance Indicator), such as SAIDI (System Average Interruption Duration Index) and SAIFI (System Average Interruption Frequency Index), through more transparent monitoring of the behavior of the electrical distribution grid and its equipment. Through the research and development (R&D) project Urban Futurability, ENEL (Ente nazionale per l’energia elettrica) Distribution São Paulo seeks to apply the solution by implementing the concept of smart grids, an idea that has been growing each year by virtue of technological developments and the employment of new sensors in electrical distribution grids. Urban Futurability seeks to innovate the automation area, digital twin, and construction enhancement in the Vila Olímpia neighborhood of São Paulo, Brazil.

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Determination of Hot-Spot Temperature for ONAN Distribution Transformers with Dynamic Thermal Modelling

Cited by 10 publications

References 17 publications

A Stochastic Load Forecasting Approach to Prevent Transformer Failures and Power Quality Issues Amid the Evolving Electrical Demands Facing Utilities

A Stochastic Load Forecasting Approach to Prevent Transformer Failures and Power Quality Issues Amid the Evolving Electrical Demands Facing Utilities

A new approach for top‐oil thermal modelling of power transformers using Unscented Kalman filter considering IEEE C57.91 standard

Application Study in the Field of Solutions for the Monitoring Distribution Transformers of the Overhead Power Grid

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