Distribution transformer overloading capability under cold-load pickup conditions

Aubin, J.; Bergeron, R.; Morin, R.

doi:10.1109/61.103685

Cited by 40 publications

(4 citation statements)

References 1 publication

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“…A simplified power consumption characteristic for CLPU is demonstrated in Eq. (1) [16]. Assume a cold load with rating active power of P CL,N is reintegrated into the restored subsystem at time T S0 , and the practical active power consumption of this load is denoted as P CL,t at time t. During the period from T S0 to T S1 , a peak power consumption estimated as K c P ,N is caused by the effect of CLPU and K c denotes the overload coefficient of CLPU.…”

Section: Characteristic Of Load Pickupmentioning

confidence: 99%

Sectionalizing strategies for minimizing outage durations of critical loads in parallel power system restoration with bi-level programming

Liu

Lin

Wen

et al. 2015

International Journal of Electrical Power & Energy Systems

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Section: Characteristic Of Load Pickupmentioning

confidence: 99%

Sectionalizing strategies for minimizing outage durations of critical loads in parallel power system restoration with bi-level programming

Liu

Lin

Wen

et al. 2015

International Journal of Electrical Power & Energy Systems

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“…9. AMBIENT TEMPERATURE [15][16][17][18][19] The authors of References [16][17][18][19] discuss the results of their computer programs based on varying ambient temperature conditions. They present the results of their analyses for winter loading when the temperature changes are quite rapid.…”

Section: Thermal Limits For Short-circuit Conditionsmentioning

confidence: 99%

Evaluation of transformer overloading capability

Tripathy

Lakervi²

2005

Euro. Trans. Electr. Power

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SUMMARYThe evaluation of transformer overload capabilities definitely leads into complex variables affecting the functional life of the power and distribution transformer. The combined effect of thermal overloads, mechanical stresses due to transient over-voltage and other parameters relating to the transformer components other than windings must be taken into account. A transient analysis and temperature rise programme is implemented on the information system.

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“…For feeders with high penetration of TCLs, CLPU can have serious consequences, such as restoration failure [4]- [8], transformers aging [9], [10], transformer overloading [11], and unacceptable voltage drops [12]. CLPU demand can continue several minutes to even several hours after extreme weather conditions [2].…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Framework for Assessing Cold Load Pick-Up Demand in Service Restoration

Dehghanpour

Wang

et al. 2019

IEEE Trans. Power Syst.

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Cold load pick-up (CLPU) has been a critical concern to utilities. Researchers and industry practitioners have underlined the impact of CLPU on distribution system design and service restoration. The recent large-scale deployment of smart meters has provided the industry with a huge amount of data that is highly granular, both temporally and spatially. In this paper, a data-driven framework is proposed for assessing CLPU demand of residential customers using smart meter data. The proposed framework consists of two interconnected layers: 1) At the feeder level, a nonlinear auto-regression model is applied to estimate the diversified demand during the system restoration and calculate the CLPU demand ratio. 2) At the customer level, Gaussian Mixture Models (GMM) and probabilistic reasoning are used to quantify the CLPU demand increase. The proposed methodology has been verified using real smart meter data and outage cases.Index Terms-Cold load pick-up, distribution systems, service restoration, least squares support vector machine, Gaussian Mixture Model.

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Distribution transformer overloading capability under cold-load pickup conditions

Cited by 40 publications

References 1 publication

Sectionalizing strategies for minimizing outage durations of critical loads in parallel power system restoration with bi-level programming

Sectionalizing strategies for minimizing outage durations of critical loads in parallel power system restoration with bi-level programming

Evaluation of transformer overloading capability

A Data-Driven Framework for Assessing Cold Load Pick-Up Demand in Service Restoration

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