Operation and maintenance (O&M) activities represent a significant share of the total expenditure of a wind farm. Of these expenses, costs associated with unexpected failures account for the highest percentage. Therefore, it is clear that early detection of wind turbine (WT) failures, which can be achieved through appropriate condition monitoring (CM), is critical to reduce O&M costs. The use of Supervisory Control and Data Acquisition (SCADA) data has recently been recognized as an effective solution for CM since most modern WTs record large amounts of parameters using their SCADA systems. Artificial intelligence (AI) techniques can convert SCADA data into information that can be used for early detection of WT failures. This work presents a systematic literature review (SLR) with the aim to assess the use of SCADA data and AI for CM of WTs. To this end, we formulated four research questions as follows: (i) What are the current challenges of WT CM? (ii) What are the WT components to which CM has been applied? (iii) What are the SCADA variables used? and (iv) What AI techniques are currently under research? Further to answering the research questions, we identify the lack of accessible WT SCADA data towards research and the need for its standardization. Our SLR was developed by reviewing more than 95 scientific articles published in the last three years.

Operation and maintenance costs of wind power plants represent a significant share of the total expenditure of a wind farm project. It is therefore vital to optimise maintenance activities in order to reduce costs while improving wind turbine reliability and availability. The induction generator is a major contributor to failure rates and downtime of wind turbines, where doubly-fed induction generators are the dominant variable-speed technology employed. Actual data from operating wind turbines is seldom presented in the scientific literature. This paper analyses two in-service doubly-fed induction generators from wind turbines operating in a Spanish wind power plant. One of the generators under study reported excessive temperature on the rotor windings, while the other did not. In order to achieve a diagnosis for the reported excess of temperature, current signature analyses were performed on both machines. Fault-related frequency components were identified in the current spectra of the faulty machine, and were compared against the healthy one. A diagnosis was achieved for the faulty machine: dynamic eccentricity caused by a rotor fault was identified as the cause of the excessive temperature in the rotor windings.

The goal of the present paper is to achieve the diagnosis of an in-service 1.5 MW wind turbine equipped with a doubly-fed induction generator through current signature and vibration analyses. Real data from operating machines have rarely been analysed in the scientific literature through current signature analysis supported by vibrations. The wind turbine under study was originally misdiagnosed by the operator, where a healthy component was replaced and the actual failure continued progressing. The chronological evolution of both the electrical current and vibration spectra is presented to conduct an in-depth tracking of the fault. The diagnosis is achieved through spectral analysis of the stator currents, where fault frequency components related to rotor mechanical unbalance are identified. This is confirmed by the vibration analysis, which provides insightful information on the health of the drive train. These results can be implemented in condition monitoring strategies, which is of great interest to optimise operation and maintenance costs of wind farms.