# Intelligent Fault Diagnosis Techniques Applied to an Offshore Wind Turbine System
###### Abstract
The most challenging issue, which should be considered in wind turbine FDI schemes, is that wind speed is poorly measured by anemometers due to the spatial/temporal effective wind speed distribution over the blade plane, turbulence, wind shear, and tower shadow effects. Therefore, wind speed is considered an an unknown disturbance, as is the consequent aerodynamic torque. Furthermore, FDI schemes should be robust to the considerable noise present in sensor measurements [4,7]. The most commonly-adopted model-based FDI techniques for wind turbines are the parity relation method and observer design [10]. However, these approaches require accurate mathematical models to simulate the dynamic behaviors of the process under diagnosis [11]. These methods do not require high-resolution signals, so there is no need for data acquisition hardware or installation of additional sensors. However, it is quite challenging to design an effective model that mimics real-world applications. Therefore, data-driven approaches, such as Neural Networks (NN) and fuzzy inference systems, can be used for wind turbine FDI designs. In fact, these artificial intelligence systems provide the best tools to represent the nonlinear and partially-known behavior of wind turbines [12]. The designed prototype is fed with actual/estimated inputs (i.e., those of the wind turbine) to generate redundant outputs. Some other works have proposed the use of this data-driven learning scheme for wind turbine FDI, and it has been considered and applied to different wind turbine components, e.g., gearboxes, generator faults, and pitch faults [10]. As an alternative approach, fault information can be directly extracted/inferred using this method, which relies on the design of an accurate a priori knowledge-based network, e.g., Adaptive Neuro-Fuzzy Inference System (ANFIS) or Fuzzy Inference System (FIS). Accordingly, expert knowledge must be included in the design, whether for numerical rules or fuzzy if/then linguistic rules. One of the advantages of fuzzy logic and fuzzy membership representation is that the uncertain measurement of the wind speed provided by the anemometer can be directly used [8]. Classification methods are also utilized for rotor imbalance/aerodynamic asymmetry fault diagnosis [13]. Therefore, the main contribution of this work is the development of viable and reliable solutions for the fault diagnosis of an offshore wind turbine model. The design of fault-tolerant controllers is not considered in this paper, but it would likely rely on the same tools considered here. In fact, the fault diagnosis module provides information on the faulty conditions of the system so that the controller activity can compensate. In particular, the FDI task was accomplished here by using fault estimators, which were obtained via these data-driven approaches, as they also offer effective tools for managing limited knowledge of the process dynamics, together with noise and disturbance effects. The first data-driven solution addressed in this paper relies on fuzzy Takagi-Sugeno models [14], which are derived from a clustering algorithm, followed by an identification procedure [15]. The second solution exploits NN to describe the nonlinear analytical links between measurement and fault signals. The chosen network architecture belongs to the Nonlinear AutoRegressive with eXogenous (NARX) input prototype, which can describe dynamic relationships over time. The training of the neural fault estimators exploits a standard training algorithm that processes the acquired data [16]. The developed fault diagnosis strategies were verified by means of a high-fidelity simulator that describes the normal and faulty behavior of a wind turbine plant. The achieved performances were verified in the presence of uncertainty and disturbance effects, thus validating the reliability and robustness features of the proposed schemes.