Oil exponent thermal modelling for traction transformer under multiple overloads

Zhou, Lijun; Wang, Lujia; Tang, Haolong; Wang, Jian; Guo, Lan-Ping; Cui, Yi

doi:10.1049/iet-gtd.2018.5084

Cited by 15 publications

(12 citation statements)

References 21 publications

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“…As recommended by IEC 60076‐7 and IEEE C57.91, the definition for the oil exponent n is presented as

normalΔ θ_{oil} = normalΔ θ_{oil, rated} {(\frac{I_{pu}^{2} R + 1}{R + 1})}^{n}

In (13), there is a strong non‐linear relationship between the TOT rise over ambient temperature and the total loss. In order to use linear regression to fit the oil exponent, we take the common logarithm on both sides of (13) [26]

l g ()(, \frac{normalΔ θ_{oil}}{normalΔ θ_{oil, rated}}) = n \lg ()(, \frac{I_{pu}^{2} R + 1}{R + 1})

let the variable Y = lg (Δ θ oil /Δ θ oil, rated ), and let the variable

X = \lg ()(, I_{pu}^{2} R + 1 / R + 1)

. Based on (14) with information shown in Table 1, the fitting curve for oil exponent was plotted for a load range from 0.7 to 1.4 pu, as shown in Fig.…”

Section: Experimental Set‐up and Resultsmentioning

confidence: 99%

Top‐oil temperature modelling by calibrating oil time constant for an oil natural air natural distribution transformer

Wang

Zhang

Villarroel

et al. 2020

IET Generation, Transmission & Distribution

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Integration of low carbon technologies poses a technical challenge on distribution transformers due to the dynamic loading and potentially frequent overloading scenarios. Transformer dynamic thermal rating is hence required, which is the most economical approach to tackle this challenge and ensure the safe operation. To reach the aim, it is important to enhance the accuracy of the dynamic thermal model, where the top oil temperature is a key thermal parameter. In this paper, a wide range of constant load temperature rise tests were carried out on an 11/0.433 kV distribution transformer to study the dynamic thermal behaviour of the top-oil temperature. A model based on the IEC 60076-7 thermal model but with an improved oil time constant calibration was deduced for top oil temperature modelling. The oil time constant calibration was inspired by IEEE C57.91 and verified by 8 temperature rise tests with load factors ranging from 0.7 pu to 1.4 pu. In addition, the improved top-oil temperature modelling was further verified in experiments under multiple load profiles. Nomenclature Cth., oilOil thermal capacity (J/kg). I Load current (A). P Loss (W). k Time step index for discrete calculation. R Ratio of load loss at rated current to no-load loss. ∆t Sampling period (min). n Oil exponent. u Constant in Susa's model. Greek: θamb Ambient temperature (°C). θoil Top-oil temperature (°C). τoil Top-oil time constant (min). τoil, rated Rated top-oil time constant (min). τaveoil, rated Rated average-oil time constant (min). τoil, pu Relative top-oil time constant. μ Oil dynamic viscosity (kg/m•s). ∆θoil Top-oil rise over ambient temperature (K). ∆θoil, initial Initial top-oil rise over ambient temperature (K). ∆θoil, rated Rated top-oil rise over ambient temperature (K). ∆θoil, ultimate Ultimate top-oil rise over ambient temperature at the load considered (K).

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“…As recommended by IEC 60076‐7 and IEEE C57.91, the definition for the oil exponent n is presented as

normalΔ θ_{oil} = normalΔ θ_{oil, rated} {(\frac{I_{pu}^{2} R + 1}{R + 1})}^{n}

l g ()(, \frac{normalΔ θ_{oil}}{normalΔ θ_{oil, rated}}) = n \lg ()(, \frac{I_{pu}^{2} R + 1}{R + 1})

let the variable Y = lg (Δ θ oil /Δ θ oil, rated ), and let the variable

X = \lg ()(, I_{pu}^{2} R + 1 / R + 1)

. Based on (14) with information shown in Table 1, the fitting curve for oil exponent was plotted for a load range from 0.7 to 1.4 pu, as shown in Fig.…”

Section: Experimental Set‐up and Resultsmentioning

confidence: 99%

Top‐oil temperature modelling by calibrating oil time constant for an oil natural air natural distribution transformer

Wang

Zhang

Villarroel

et al. 2020

IET Generation, Transmission & Distribution

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“…On the other hand, if the modelling framework is determined, it is critical to find a group of accurate thermal parameters [20], [21], [22]. The authors also used to research the TOT modelling, especially investigating one dominant thermal parameter: oil exponent, which contains much information [23]. So, in the manuscript, the first attempt is to find the connection between fans' condition (mainly air flow rate) and oil exponent.…”

Section: Introductionmentioning

confidence: 99%

A Method for Fans’ Potential Malfunction Detection of ONAF Transformer Using Top-Oil Temperature Monitoring

Wang¹,

Zuo

Yang

et al. 2021

IEEE Access

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The fan is one of the key components of the power transformer cooling system. The operating condition of fans determines transformers' internal temperature rise and long-term reliability. However, at present, the fans' condition monitoring only includes switch status (online) and regular maintenance (offline), online direct monitoring of the fans' operating condition is lacking due to economic costs. In view of the above-mentioned problem, this paper proposes a transformer fan early fault detection method based on the oil exponent, which is monitored by the existing transformer top oil temperature data, thereby detecting the abnormality of the fans. In this method, the oil exponent was chosen as the characteristic criterion. First, to obtain the range of oil exponent in different cooling modes, a set of physical models describing global oil flow and its interaction with air was established based on fluid dynamics and heat transfer principle. Then, regarding the constantly changing top-oil temperature, ambient temperature and load current, an oil exponent tracking algorithm using particle swarm optimization (PSO) was proposed within an improved IEC dynamic thermal model. The operation data from an oil-immersed transformer with a rated capacity of 120-MVA and rated voltage of 220-kV was selected to verify the above methods under two different scenarios.INDEX TERMS Power transformer, cooling system, fan, top-oil temperature, condition monitoring, oil exponent This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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“…As an essential part of urban rail transit, the traction transformer operation's reliability depends on its insulation performance [1], [2]. The traction transformer's insulation performance is closely related to the heat generation and heat dissipation inside the traction transformer [3], [4]. The traction transformer's operation instability or damage is mainly due to the temperature of the transformer windings exceeding the insulating material limit's thermal resistance.…”

Section: Introductionmentioning

confidence: 99%

A Prediction Model of Hot Spot Temperature for Split-Windings Traction Transformer Considering the Load Characteristics

et al. 2021

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Hot spot temperature (HST) is an important parameter reflecting the working state of the traction transformer, and the load characteristics have a great influence on the HST. The current researches rarely consider the load characteristics of transformers in-depth, and most of the research on the temperature field focuses on steady load. Therefore, a prediction model of HST for the traction transformer considering the load characteristics is established by the finite element simulation in this paper. Considering the impact and nonlinearity of traction load, the influence of the load characteristics on the HST is studied. Analysis of the load curve revealed that the steady and step load combination could well simulate the traction load. Based on the analysis of traction transformer load characteristics, the temperature field coupling calculation of traction transformer is carried out according to the VI normative duty class. On this basis, the influence of load rate and time on HST is studied. The present findings show that the method can predict the HST of traction transformer under different working conditions and provide a reference for load dispatching of traction transformer. The simulation data are in good agreement with the test results, and the average relative error is 3.4%. The results verify the effectiveness of the proposed method.INDEX TERMS Traction transformers, hot spot temperature, load characteristics, temperature field analysis, finite element simulation.

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Oil exponent thermal modelling for traction transformer under multiple overloads

Cited by 15 publications

References 21 publications

Top‐oil temperature modelling by calibrating oil time constant for an oil natural air natural distribution transformer

Top‐oil temperature modelling by calibrating oil time constant for an oil natural air natural distribution transformer

A Method for Fans’ Potential Malfunction Detection of ONAF Transformer Using Top-Oil Temperature Monitoring

A Prediction Model of Hot Spot Temperature for Split-Windings Traction Transformer Considering the Load Characteristics

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