Physics-informed turbulence intensity infusion: A new hybrid approach for marine current turbine rotor blade fault detection

Abstract

Ocean current turbines (OCT) convert the kinetic energy housed within the earth’s ocean currents into electricity. However, OCT technologies used to harvest this energy are still at an early stage in development due to technical and economic challenges stemming from the high operation and maintenance costs associated with limited geographical location access and harsh operating environments. In an effort to alleviate reliability concerns associated with marine electricity generation, this paper proposes a novel physics-guided rotor blade imbalance fault detection framework that combines non-intrusively acquired fault features obtained from the turbine’s electrical power signal with environmental condition data to enhance the fault detection capabilities. The combination of these two data sources paved the way for the development of a physics-informed neural network that ensures the classifications made by our framework are scientifically consistent with the underlining hydro-kinematic rotor dynamics of the OCT. The effectiveness of our framework is validated on simulation data produced by an in-house high-fidelity numerical simulation platform that includes temporally and spatially dynamic oceanic operating environment models. Test results demonstrate a Type-I error rate of 5.00% and a Type-II error rate of 2.92%.