# Fuzzy and Neural Network Approaches to Wind Turbine Fault Diagnosis
## 1 Introduction  
Wind power is the second largest source of renewable energy after solar technology. The global popularity of wind power has risen significantly due to the need to harness electrical power in order to limit and end the necessity of fossil fuels [1, 2]. Variable-speed horizontal-axis Wind Turbines (WTs) represent the most important Wind Energy Conversion System (WECS). Moreover, for effective and reliable power conversion, suitable technical and technological solutions have to be properly implemented and exploited in WECSs. Technological advances in WECSs have encouraged the installation of wind farms. Owing to their large size and requirement of high wind speeds, these installations are located in offshore areas. However, offshore WECSs result in disadvantages such as high installation costs, continuous supervision, and Operation and Maintenance (O & M), which in turn affect their degree of reliability and availability. O & M costs for offshore WTs have been found to be nearly 30-40% of the comprehensive life costs of WECSs [1], thus increasing the final production costs of the generated electricity. Reducing O&M costs and increasing the plant availability are the key points in operating and generating power from WECSs [1; 3]. The optimal conversion cost of WECS operation can be achieved in two ways: (i) by using efficient design techniques to prevent the unbalancing of loads and maintaining a rated power output throughout the operation, which can be achieved via appropriate modelling and monitoring strategies of the WT system; (ii) by developing suitable fault diagnosis techniques that prevent unnecessary O&M and shutdown periods due to failures. Technological advancements have been proposed and implemented in order to achieve effective designs for different models of WTs in recent years. However, the existence of faults is inevitable in the operation of WECSs, like any other industrial system. Electromechanical devices, actuators and sensors represent the most common components affected by faults in a WECS [1; 3]. Any faulty event in the WECS results in a deviation of the system from its objectives and causes possible damage to the process components. Moreover, a fault will deteriorate the performance of the overall plant, resulting in the unscheduled shutdown of the WECS. To remain operative and competitive, there is a high demand for improving the reliability and availability of WTs with reduced unplanned downtimes in the electricity market, thus limiting O&M and significantly reducing the final cost of energy. To this end, Fault Detection and Isolation (FDI) for WTs has been successfully exploited, together with Fault Tolerant Control (FTC) strategies, see e.g., [3; 4]. In general, FDI relies on three methods, i.e., parity equations, parameter estimation approaches and observer/filter methods [5]. In particular, model-based FDI schemes for WTs exploited sliding mode approaches, as shown e.g., in [6], linear observers [7], and Kalman filters [8]. However, these methods usually assume that an accurate mathematical description of the monitored process is available, as well as for the sources of uncertainty and disturbance. Therefore, any inaccurate information during faults may result in degradation of system performance and can cause instability during post-fault operation. This issue is of great significance in safety-critical systems such as WTs, having several sources of noise and uncertainties. Therefore, it is required to develop robust and reliable FDI strategies in a WECS to ensure its safe operation. Robust FDI strategies can be also based on data-driven solutions, as they are able to learn from experience, i.e., from the information brought by the input and the output measurements of the monitored process. Therefore, in contrast to model-based techniques, data-driven approaches for WTs can exploit e.g., fuzzy logic [9; 10], neural networks [11; 12; 13; 14], neuro-fuzzy [15], or mixed solutions [16]. With reference to this paper, the topic of the FDI for a WT system is analysed.