Abstract:In this research, a differential protection technique for a power transformer is proposed by using random forest and boosting learning machines. The proposed learning machines aim to provide a protection expert system that distinguishes between different transformer status which are normal, inrush, overexcitation, CT saturation, or internal fault. Data for 20 different transformers with 5 operating cases are used in this research. The utilized random forest and boosting techniques are trained using these data.… Show more
“…Pattern recognition algorithms or machine learning techniques are other methods that have been used in this field. For a power transformer, Khatib and Arar [7] proposed a differential protection technique based on random forest and boosting learning machines. Afrasiabi et al [8] extracted statistical features from the normalized differential current gradient to train the robust soft learning vector quantization (RSLVQ) classifier for developing a new intelligent differential protection scheme.…”
Numerous methods exist to distinguish between inrush current and internal faults, but these approaches have not yet become practical due to their inherent limitations. As a result, conventional methods, despite their well-known drawbacks, continue to be widely used in practice. In this paper, a new method based on time-frequency analysis is presented for detecting inrush current situations. To do this, a diverse array of scenarios involving a power transformer switching ON and internal fault cases are simulated using the PSCAD/EMTDC software package. Then, a hyperbolic S-transformer is employed to extract a determining index from the simulation results. Finally, a suitable threshold value for this index is computed so that inrush current can be distinguished from fault current by comparing the index with its threshold. Evaluation of the efficiency of the proposed method using simulation and real data confirms its excellent accuracy. Therefore, it can be used in algorithms for power transformer differential protection to improve their stability during inrush current transients.
“…Pattern recognition algorithms or machine learning techniques are other methods that have been used in this field. For a power transformer, Khatib and Arar [7] proposed a differential protection technique based on random forest and boosting learning machines. Afrasiabi et al [8] extracted statistical features from the normalized differential current gradient to train the robust soft learning vector quantization (RSLVQ) classifier for developing a new intelligent differential protection scheme.…”
Numerous methods exist to distinguish between inrush current and internal faults, but these approaches have not yet become practical due to their inherent limitations. As a result, conventional methods, despite their well-known drawbacks, continue to be widely used in practice. In this paper, a new method based on time-frequency analysis is presented for detecting inrush current situations. To do this, a diverse array of scenarios involving a power transformer switching ON and internal fault cases are simulated using the PSCAD/EMTDC software package. Then, a hyperbolic S-transformer is employed to extract a determining index from the simulation results. Finally, a suitable threshold value for this index is computed so that inrush current can be distinguished from fault current by comparing the index with its threshold. Evaluation of the efficiency of the proposed method using simulation and real data confirms its excellent accuracy. Therefore, it can be used in algorithms for power transformer differential protection to improve their stability during inrush current transients.
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