Parameter estimation of three-winding transformers using genetic algorithm

Thilagar, S. Hosimin; Rao, G. Sridhara

doi:10.1016/s0952-1976(02)00087-8

Cited by 39 publications

(14 citation statements)

References 6 publications

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“…Some of the popular methods are inrush current tests [3], [4]; genetic algorithm techniques [5], [6]; least square error methods [7], [8]; open-circuit and shortcircuit tests [9], [10]. All these methods require disconnection of the transformer from the power grid prior to inspection.…”

Section: Introductionmentioning

confidence: 99%

Advanced monitoring and on-line health diagnosis of single-phase transformers

Reddy

Sao

Rajpurohit

2015

2015 International Conference on Power and Advanced Control Engineering (ICPACE)

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Transformers range in size from a few kVA to several hundred MVA are one of the most critical components of power system with with replacement costs around millions of dollars for power transformers. A sudden in-service failure of a transformer not only results in substantial economic losses but also can result in death of utility personnel apart from the environmental damages caused and grid instability induced. The condition assessment and life estimation of a transformer is an important part of reliable power system operation. The equivalent parameters of a transformer are not affected by external faults and change only in the presence of internal abberations making them suitable for internal condition assessment of transformers. Presently, there is no accurate measurement method for the transformer winding parameters and generally require the transformer to be disconnected from the power system. A new simple algorithm for extracting transformer winding parameters which can be implemented online is presented in this paper. This method doesn't require the transformer to be disconnected from the power grid. The primary and secondary currents and voltages are taken as inputs and winding parameters are obtained by solving the equivalent circuit equations in real time continuously which allows for interpretation and detection of faults in real-time. The proposed method has been tested and validated by simulations. Different cases of parameter variations and their possible interpretations are discussed in order to detect any incipient faults or anomalies in an in-service transformer which serve as advance alarms.

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Section: Introductionmentioning

confidence: 99%

Advanced monitoring and on-line health diagnosis of single-phase transformers

Reddy

Sao

Rajpurohit

2015

2015 International Conference on Power and Advanced Control Engineering (ICPACE)

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“…An improved GA for single and double objective optimum design problems of power transformers is proposed [12] as a search method. The GA based parameter estimation method is compared with conventional methods [13] for the equivalent circuit parameter estimation of a three-winding transformer. A heuristic method for transformer design minimizing the cost is proposed in [14] based on the given specifications and available materials.…”

Section: Introductionmentioning

confidence: 99%

Optimal design of a furnace transformer by intelligent evolutionary methods

Rao

Karsiti

2012

International Journal of Electrical Power & Energy Systems

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“…Evolutionary computation is a powerful tool for solving complex optimization models. Successful applications of evolutionary computation algorithms have also been reported in a variety of domains [22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%