Smart Transformer Protection: Hybrid Approach to Fault Discrimination
Differential Protection of Transformer
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In modern power transformers, conventional differential protection with harmonic restraint often struggles due to lower second harmonic levels from improved core materials and CT saturation during heavy faults. These challenges can lead to false operations by misidentifying benign phenomena such as magnetizing inrush, overexcitation, and internal faults. This book introduces a novel digital protection scheme that employs a hybrid KNN-GA algorithm to accurately distinguish true internal faults from non-fault conditions. Using discrete wavelet transform for feature extraction, the scheme capture...
In modern power transformers, conventional differential protection with harmonic restraint often struggles due to lower second harmonic levels from improved core materials and CT saturation during heavy faults. These challenges can lead to false operations by misidentifying benign phenomena such as magnetizing inrush, overexcitation, and internal faults. This book introduces a novel digital protection scheme that employs a hybrid KNN-GA algorithm to accurately distinguish true internal faults from non-fault conditions. Using discrete wavelet transform for feature extraction, the scheme captures critical statistical parameters, while the K-nearest Neighbor method, optimized with a Genetic Algorithm, enhances classification accuracy. Extensive laboratory experiments and real-time DSP implementation on the TMS320C6713T demonstrate that this hybrid approach outperforms traditional methods like ANN and SVM, providing faster, more secure, and more reliable transformer protection in modern power grids.
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