Fault detection and identification for control systems in floating offshore wind farms: A supervised Deep Learning methodology

This study employs a data-driven Fault Detection and Isolation (FDI) methodology in Floating Offshore Wind Turbine (FOWT) farms. The main objective of the work lies in classifying faults impacting the components of the control subsystems across multiple turbines. Unlike existing research, the emphasis here is placed specifically on identifying and classifying non-critical faults, which may result in suboptimal farm performance without necessitating a shutdown. From a methodological perspective, a Deep Neural Network has been designed to solve the classification problem by providing a probability vector, the most probable class indicator of the true state. One of the major contributions of this work lies in its applicability to FOWT farms instead of being confined to individual devices, facilitating a comprehensive performance assessment at the global farm level. The integration of this data-driven methodology with tolerant control strategies might enable early intervention, mitigating the impact of these faults and enhancing overall power generation efficiency. The target case study is a three-FOWT farm modeled in a Simulink environment, allowing for the simulation of operational behavior under diverse conditions and various faults affecting sensors and actuators. This work considers ten distinct fault classes, including the healthy condition, and three possible faults for each FOWT: pitch angle sensor, pitch angle actuator, and generator speed sensor. These frequent faults pose challenges to the optimal functioning of the control system managing the FOWTs. The outcomes highlight that the estimated probability of the healthy state serves as a robust indicator for detecting unknown faults. Results also demonstrate the adequate efficacy of the method in pinpointing the fault origin. However, we observe confusion between pitch sensor and actuator faults that require further investigation for comprehensive understanding.