# Intelligent Fault Diagnosis Techniques Applied to an Offshore Wind Turbine System
###### Abstract  
Fault diagnosis of wind turbine systems is a challenging process, especially for offshore plants, and the search for solutions motivates the research discussed in this paper. In fact, these systems must have a high degree of reliability and availability to remain functional in specified operating conditions without needing expensive maintenance works. Especially for offshore plants, a clear conflict exists between ensuring a high degree of availability and reducing costly maintenance. Therefore, this paper presents viable fault detection and isolation techniques applied to a wind turbine system. The design of the so-called fault indicator relies on an estimate of the fault using data-driven methods and effective tools for managing partial knowledge of system dynamics, as well as noise and disturbance effects. In particular, the suggested data-driven strategies exploit fuzzy systems and neural networks that are used to determine nonlinear links between measurements and faults. The selected architectures are based on nonlinear autoregressive with exogenous input prototypes, which approximate dynamic relations with arbitrary accuracy. The designed fault diagnosis schemes were verified and validated using a high-fidelity simulator that describes the normal and faulty behavior of a realistic offshore wind turbine plant. Finally, by accounting for the uncertainty and disturbance in the wind turbine simulator, a hardware-in-the-loop test rig was used to assess the proposed methods for robustness and reliability. These aspects are fundamental when the developed fault diagnosis methods are applied to real offshore wind turbines. fault diagnosis; analytical redundancy; fuzzy prototypes; neural networks; diagnostic residuals; fault reconstruction; offshore wind turbine simulator +
Footnote †: journal: Applied  
solutions for managing faults are required to improve wind turbine features, particularly in faulty situations. Such features would prevent critical failures that may affect other wind turbine components, thus avoiding the unplanned replacement of functional parts and increased O&M costs. It is beneficial to keep maintenance costs as low as possible, decrease downtime, and consequently increase the amount of captured power and improve reliability despite the presence of faults [3]. Fault Detection and Isolation (FDI) techniques are powerful methods for this purpose. The fault information captured by FDI units can be used to optimize maintenance procedures via remote diagnosis [4]. The use of FDI renders the equipment robust to the considered faults and, as a result, maintains the performance of the wind turbine at the desired level, even with the occurrence of faults. Therefore, maintenance requirements and downtime will decrease, and the reliability of power generation will improve. Therefore, the final cost is kept as low as possible [5,6]. FDI designs for wind turbines have been significantly developed over the last decade. Most of the works in this field have been motivated by competitions conducted by kk-electronic a/c and MathWorks from 2009-2016 [4,7]. Accordingly, the number of studies and consequent publications has increased considerably, and the subject is intensively researched worldwide [8]. However, there are only a few available review papers in this field [7,9]. Hardware redundancy involves equipping components, such as sensors and actuators, with physically-identical counterparts to generate so-called residual signatures, which contain information on the possible fault. This approach increases the weight, occupied space, data acquisition complexity, and therefore, the final design cost. These issues are very problematic for offshore wind turbines. In contrast, software redundancy or computer-based FDI techniques have been developed for wind turbines throughout the last decade to overcome the aforementioned problems [1]. A mathematical model of a wind turbine is used to generate redundant signals and, accordingly, residuals.