A reliable approach for detection of incipient faults of short-circuits in induction generators using machine learning. (October 2018) - Rebouças Filho, Pedro Pedrosa; Nascimento, Navar M.M.; Sousa, Igor R.; Medeiros, Cláudio M.S.; de Albuquerque, Victor Hugo C.
Highlights: An approach for detection of incipient faults of short-circuits in wind turbine induction generator using machine learning. This research address a solid approach to detect incipient stator winding inter-turn short-circuits in SCIG applied in wind turbines. A experimental setup for electrical current acquisition of different types of faults is proposed for further applications. The results driven to the neural network, Multi-layer Perceptron (MLP), and the Fourier transform as the better approach for our problem. Evaluation of several classifiers Machine Learning. Abstract: This research contributes with a reliable approach to detect incipient stator winding inter-turn short-circuits in induction generators applied in wind turbines. Using a wind turbine test-bench, we inserted different types of short-circuit in the generator. The electrical current is acquired to build a fault database. We propose the use of four feature extraction techniques with three classifiers. The MLP identified 100% of the generator's Normal conditions with less than 1% false positives and negatives. Using different topologies of MLP, it was possible to identify incipient short-circuits in 1.41% turns with 99.33% accuracy. The combination Fourier-MLP is more useful for fault detection, since it obtained 84.48% of accuracy, with 99.98% of Normal conditions correctly classified.