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The post‐silicon validation and debug is the most important task in the contemporary integrated circuit design methodology. The vital problem prevailing in this system is that it has limited observability and controllability due to the minimum number of storage space in the trace buffer. This tends to select the signals prudently in order to maximize state reconstruction. In the reported works, to select and to restore the signals efficiently it is categorized into two types like low simulation with high‐quality technique and high simulation with low‐quality technique. In this work, a node‐based combinational gate signal selection algorithm is proposed based on machine learning method that maximizes the state restoration capability. A significant improvement (80%) has made to achieve adequate simulation time with the high‐quality associated with the state‐of‐the‐art of supplementary methods. [ABSTRACT FROM AUTHOR]

Sweep frequency response analysis (SFRA) is a diagnostic technique which can identify faults within transformers. Though SFRA approaches are used to identify faults, their effectiveness in discriminating two same level faults at two different locations within transformer is not much analysed and reported. Experiments were carried out on a 1 kVA, single phase, 240 V/240 V laboratory transformers and on a 315 kVA, three phase, 11 kV/433 V, Dyn11, ONAN, distribution transformer, to analyse the usefulness of SFRA in inter turn short identification and discriminations. Faulty conditions were created artificially in transformers, by shorting the tappings provided in one of their windings, at different levels and locations. Open circuit self-impedance and transfer voltage transfer function approaches were experimented and transfer function plots representing winding impedance and voltage transfer ratios at various frequencies were developed for transformers, under normal and faulty conditions. Significant differences were identified at some frequencies while comparing normal case plots with faulty case plots. It was observed that the SFRA is effective in identifying shorts within transformer winding as well as, discriminating two same level shorts (i.e.) locating shorts. Statistical analysis using absolute difference and min-max ratio also confirmed the efficacy of these approaches. [ABSTRACT FROM AUTHOR]

Electrical shorts in transformers on load can be diagnosed by frequency response analysis (FRA) using impulse or sinusoidal sweep signal. At HV laboratory of SASTRA, a low-voltage impulse based on-line on-load FRA (OLOL FRA) was carried out on a single phase 50 Hz, 1 kVA, 240 V/240 V small laboratory transformer while delivering power to lamp load, and the effectiveness in electrical short diagnosis was analysed. For this purpose, some artificial faults were created within one of the windings of the transformer investigated, and the responses of the winding to different frequency components of the impulse voltages were observed before and after introducing these faults. Comparison of the frequency response (impedance) of the winding under normal condition with frequency response under fault condition was made, which clearly indicated that faults had altered the winding impedance for some frequency components of the impulse. This reveals that fault within the winding can be identified and located, by the OLOL FRA technique, through careful comparisons of frequency response. Statistical analysis of transfer function magnitudes with correlation coefficient, standard deviation and absolute sum of logarithmic error has also validated the same. [ABSTRACT FROM AUTHOR]

Electrical shorts in transformers on load can be diagnosed by frequency response analysis (FRA) using impulse or sinusoidal sweep signal. At HV laboratory of SASTRA, a low‐voltage impulse based on‐line on‐load FRA (OLOL FRA) was carried out on a single phase 50 Hz, 1 kVA, 240 V/240 V small laboratory transformer while delivering power to lamp load, and the effectiveness in electrical short diagnosis was analysed. For this purpose, some artificial faults were created within one of the windings of the transformer investigated, and the responses of the winding to different frequency components of the impulse voltages were observed before and after introducing these faults. Comparison of the frequency response (impedance) of the winding under normal condition with frequency response under fault condition was made, which clearly indicated that faults had altered the winding impedance for some frequency components of the impulse. This reveals that fault within the winding can be identified and located, by the OLOL FRA technique, through careful comparisons of frequency response. Statistical analysis of transfer function magnitudes with correlation coefficient, standard deviation and absolute sum of logarithmic error has also validated the same. [ABSTRACT FROM AUTHOR]