
The non linear characteristic of transformer magnetizing core leads more current/voltage signal distortion in the event of system disturbance/fault. The harmonic penetration in the current signal depends of the transformer core, characteristic of load and abnormal conditions. Different situations or abnormalities have different harmonics content and consequently different waveform signature. In this article, authors have utilized the harmonics content to identify normal, inrush condition and fault condition in power transformer. Total Harmonic Distortion (THD) of the current waveform for one cycle, is measured by performing Fast Fourier Transformation (FFT) analysis. Authors observed that the percentage THD is generally high in the case of inrush condition due to initial flux setup in magnetic core. Conversely, during fault condition, the %THD will be low (within a particular range) due to symmetric waveform. The magnitude of %THD below predefined range will be considered as normal condition and excess THD will be considered as inrush condition. On the other hand, if THD remains within a particular range then, it is considered as fault condition. In this article, authenticity of the presented scheme is checked by performing various test cases like initial inrush, internal fault, switching of considered transformer in faulty condition and CT saturation during fault condition. The hardware validation proves the competency of the proposed THD based discrimination scheme for transformer protection. The scheme presented here is simple and capable to solve complex problem of classification of inrush condition and fault condition.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 21 | |
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| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
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