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FGM is a traumatic experience that may leave a lasting psychological mark and a negative impact on the psychological status of affected girls.

This study investigates the impact of electrical parameter variation of a high-frequency transformer model on its sweep frequency response analysis (SFRA) signature to help in classification and interpretation. The simulations have been done using MATLAB and compared with the reference data. The results of SFRA measurements are repeatable up to and beyond 1MHz. The proposed diagnostic methodology using the Cross-Correlation Coefficient Factor (CCF) is used to identify the transformer faults. CCF used to measure the degree of relationship between two variables that establish a relation between the predicted and actual data set. The results of this proposed methodology using the CCF compared with existing Chinese Standard factor (CSF) indicate that, the proposed method is valid to identify the transformer faults. Characteristics of the proposed scheme are fully analyzed by extensive MATLAB simulation studies that clearly reveal that this method can accurately identify the transformer faults compared with CSF. And also does not affected by different fault conditions such as transformer normal condition, Turn to Turn Fault for both HV, LV sides, Axial Fault and/or Radial Faults on both sides, Short Circuit Fault between H.V and L.V Sides, Short Circuit to Ground Fault for both HV, LV sides.

Incipient fault diagnosis of a power transformer is greatly influenced by the condition assessment of its insulation system oil and/or paper insulation. Dissolved gas-in-oil analysis (DGA) is one of the most powerfull techniques for the detection of incipient fault condition within oil-immersed transformers. The transformer data has been analyzed using key gases, Doernenburg, Roger, IEC and Duval triangle techniques. This paper introduce a MATLAB program to help in unification DGA interpretation techniques to investigate the accuracy of these techniques in interpreting the transformer condition and to provide the best suggestion for the type of the fault within the transformer based on fault percentage. It proposes a proper maintenance action based on DGA results which is useful for planning an appropriate maintenance strategy to keep the power transformer in acceptable condition. The evaluation is carried out on DGA data obtained from 352 oil samples has been summarized  into 46 samples that have been collected from a 38 different transformers of different rating and different life span.