Multi-sensor edge computing architecture for identification of failures short-circuits in wind turbine generators

Abstract

Towards connectivity and development of reliable systems, smarting sensors are vastly applied in hi-tech industries. The wind energy is a growing market that could benefit from edge processing technology by enhancing monitoring systems, decreasing downtime and guiding predictive maintenance. We proposed an embedded multi-sensor architecture to detect incipient short-circuit in wind turbine electrical generators, that is robust to both false positives and negatives. Five different sensor settings are tested in three feature extraction methods and four classifiers. An analysis of variance (ANOVA) and a Tukey’s honestly significant difference (HSD) statistical tests are used to determine which architectures should be embedded in a Raspberry Pi 3, NVIDIA Jetson TX2 and NVIDIA Xavier boards. A three current sensor setting with Fourier-MLP is the most suitable approach, achieving 81.20% of accuracy, 0% of false positive rate (FPR) and 0.08% of false negative rate (FNR), also detecting generator’s normal conditions 100% of the time. For a single sensor configuration, current sensor is the most suitable method for detecting fault or non-fault conditions, being 16 times more robust to false negatives than using an axial flux sensor. Comparing the processing time, the system embedded in a NVIDIA Xavier predicts a fault condition 37% faster than in a Raspberry Pi 3, with Fourier-MLP and using a single current sensor, thus being the most suitable configuration in fault detection.