New perspectives to overloading with accurate modeling of thermal transients in oil-immersed power transformers

Veken, Wim Van der; Declercq, Jan; Baelmans, Martine; Mileghem, S Van

doi:10.1109/tdc.2001.971225

Cited by 8 publications

(11 citation statements)

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“…Both models depend only on data received in a normal heat run test, (i.e., the top-oil in the tank of the transformer, the average winding-to-top-oil gradient), [49] and [67][68]. In addition, many other authors have suggested different solutions, such as Alegi, Blake, Declercq, Lesieutre, Pierce, Pradhan, Radakovic, Ryder, Tang, and Van der Veken in references [1], [5], [10], [39], [54], [55], [56,58], [61], [75][76] and [80] . It is necessary to stress the importance of the work published by Swift for further understanding of the state of the art in transformer thermal modelling, [72][73].…”

Section: Transformer Loading Capacity: Basic Aspectsmentioning

confidence: 99%

Dynamic Thermal Modelling of Power Transformers

Susa¹,

Lehtonen²,

Nordman

2005

IEEE Trans. Power Delivery

284

125

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Power 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. One of the most important parameters governing a transformer's life expectancy is the hot-spot temperature value.The classical approach has been to consider the hot-spot temperature as the sum of the ambient temperature, the top-oil temperature rise in tank, and the hot-spot-to-top-oil (in tank) temperature gradient. When fibre optic probes were taken into use to record local hot-spots in windings and oil ducts, it was noticed that the hot-spot temperature rise over top-oil temperature due to load changes is a function depending on time as well as the transformer loading (overshoot time dependent function). It has also been noticed that the top-oil temperature time constant is shorter than the time constant suggested by the present IEC loading guide, especially in cases where the oil is guided through the windings in a zigzag pattern for the ONAN and ONAF cooling modes. This results in winding hottest spot temperatures higher than those predicted by the loading guides during transient states after the load current increases, before the corresponding steady states have been reached.This thesis presents new and more accurate temperature calculation methods taking into account the findings mentioned above. The models are based on heat transfer theory, application of the lumped capacitance method, the thermal-electrical analogy and a new definition of nonlinear thermal resistances at different locations within a power transformer. The methods presented in this thesis take into account oil viscosity changes and loss variation with temperature. The changes in transformer time constants due to changes in the oil viscosity are also accounted for in the thermal models. In addition, the proposed equations are used to estimate the equivalent thermal capacitances of the transformer oil for different transformer designs and winding-oil circulations. The models are validated using experimental results, which have been obtained from a series of thermal tests performed on a range of power transformers. Most of the tested units were equipped with fibre optic sensors in the main windings. Some of them also had thermocouples in the core and structural parts. A significant advantage of the suggested thermal models is that they are tied to measured parameters that are readily available (i.e., data obtained from a normal heat run test performed by the transformer manufacturer). AbstractPower 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. One of the most important parameters governing a transformer's life expectancy is the hot-spot temperature value.The classical approach has been to consider the hot-spot temperature as the ...

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Section: Transformer Loading Capacity: Basic Aspectsmentioning

confidence: 99%

Dynamic Thermal Modelling of Power Transformers

Susa¹,

Lehtonen²,

Nordman

2005

IEEE Trans. Power Delivery

284

125

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“…An accurate prediction of this quantity allows an accurate estimation of the insulation state and the components' residual thermal life. Unfortunately, accurate hot-spot temperature evaluation is a difficult task as stressed by the numerous studies carried out on the subject [5], [10], [12].Difficultiesariseduetotheheattransferprocesswhichis distributedovermanycomplexsurfacesmadeupofdifferentmaterials. This makes the mathematical description of the temperature distribution in the transformer very complex.…”

Section: Dynamic Loading Capability Assessment Of Power Transformentioning

confidence: 99%

“…where a single step corresponds to 5 min and the horizon is expected to range over the interval [1,12] (i.e., from 5 min to 1 h). This model aims at predicting the hot-spot temperature at time , when the last available measure has been collected at time .…”

Section: B Black-box Modeling Approachmentioning

confidence: 99%

“…It follows that a black-box predictor can be used to predict the value of , given a set of inputs. We propose the following black-box structure to address the problem of short-term forecasting: (10) where the horizon ranges over the interval [1,12] (or equivalently in the range 5 min, 1 h). Note that the accessibility of the physical prediction allows the use of a reduced set of input features with respect to the black-box formulation in (7).…”

Section: ) the Identification Of The Corrective Modulementioning

confidence: 99%

“…In particular, the power systems community started adopting black-box models [10]- [12] thanks to the availability of empirical observations made possible by the advent of hot-spot temperature acquisition modules, based on fiber optical temperature sensing technology [7], [13], [14] or advanced noninvasive identification techniques [15], [16].…”

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The Role of Learning Methods in the Dynamic Assessment of Power Components Loading Capability

Villacci

Bontempi

Vaccaro

et al. 2005

IEEE Trans. Ind. Electron.

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Abstract-The need for dynamic loading of power components in the deregulated electricity market demands reliable assessment models that should be able to predict the thermal behavior when the load exceeds the nameplate value.When assessing network load capability, the hot-spot temperature of the components is known to be the most critical factor. The knowledge of the evolution of the hot-spot temperature during overload conditions is essential to evaluate the loss of insulation life and to evaluate the consequent risks of both technical and economical nature. This paper discusses an innovative grey-box architecture for integrating physical knowledge modeling (a.k.a. whitebox) with machine learning techniques (a.k.a. black-box). In particular, we focus on the problem of forecasting the hot-spot temperature of a mineral-oil-immersed transformer. We perform a set of experiments and we compare the predictions obtained by the grey-, white-, and black-box approaches.

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IEC 60076-7 loading guide thermal model constants estimation

Susa

Nordman

2012

Int. Trans. Electr. Energ. Syst.

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When fiber optic probes were taken into use to record local hot-spots in windings and oil ducts, it was noticed that the hot-spot temperature rise over top-oil temperature due to load changes is a function depending on time as well as the transformer loading (overshoot time dependent function). This results in winding hottest spot temperatures higher than those predicted by the old loading guide during transient states after the load current increases, before the corresponding steady states have been reached.The new loading guide, (IEC 60076-7), takes into account these findings by introducing two thermal models. The first one, which is given as an exponential equation solution, is suitable for a load variation according to a step function. This method is particularly suited to determination of the heat transfer parameters by test, especially by manufacturers. The second one, which is given as a differential equations solution, is suitable for arbitrarily time-varying load factor K and time-varying ambient temperature θa. This method is particularly applicable for on-line monitoring. This method is a mathematical variation of the exponential model. A significant advantage of the suggested thermal models is that they are tied to measured parameters that are readily available (i.e., data obtained from a non-truncated heat run test performed by the transformer manufacturer).However, it has been reported recently by many users that this new approach causes a lot of technical hitches. It is unclear when and why the exponential method should be used instead of or the differential method and vice versa. Also, is there any difference between the recommended models, especially in their output? How the new thermal constants, (k 11 , k 21 , and k 22 ) , should be applied and how they could be derived? Answers to these questions and more are given in the text below.

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New perspectives to overloading with accurate modeling of thermal transients in oil-immersed power transformers

Cited by 8 publications

References 1 publication

Dynamic Thermal Modelling of Power Transformers

Dynamic Thermal Modelling of Power Transformers

The Role of Learning Methods in the Dynamic Assessment of Power Components Loading Capability

IEC 60076-7 loading guide thermal model constants estimation

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