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This paper integrates a Real Power Differential Scheme (RPDS) for power transformer protection. The suggested RPDS for power transformer computes the active power loci during normal operation, switching, normal, and internal, involves turn to turn, and external faults at varied load angles. The proposed RPDS concept is based on monitoring and comparing the transformers primary and secondary active and reactive powers. The dynamic response of the proposed RPDS is tested 300 MVA, 220/66 kV, Y/Δ transformer. Furthermore, the suggested scheme is simulated with the use of Matlab/Simulink then tested for various fault and switching conditions. Moreover, the RPDS is checked for inter turn fault conditions at primary and secondary sides. The evaluation of the suggested scheme confirms the superiority of the proposed scheme to distinguish internal and external faults as well as magnetizing inrush currents with good selectivity, high speed, sensitivity, stability limits and high accuracy response of the power differential scheme. Finally, the suggested scheme is able to detect correctly the turn to turn faults for wide range of fault resistances but fails at very low values.

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Authors: Zaida Chinchilla-Rodríguez, Anuska Ferligoj, Sandra Miguel, Luka Kronegger, and Félix de Moya-Anegón

each cluster. The clusters were numbered and separated by blue lines. The numbers were assigned to the authors to preserve their anonymity. The best blockmodel was chosen by calculating the percentage of inconsistencies (criterion function

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theoretical comparisons of selected criterion functions for document clustering . Machine Learning 55 3 311 – 331 10.1023/B:MACH.0000027785.44527.d6 .

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the specificity of IRT models, in which the underlying structure is estimated independently of these factors. It is assumed that a criterion functions differently when persons with the same GD severity differ in their responses because of other

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