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The rapid evolution of wind energy in reducing CO2 emissions worldwide is undeniable, which is, in fact, expected to continue or even increase its impressive yearly capacity growth. In this regard, optimizing operations and maintenance of wind turbines (WTs) and farms is considered to be one of the options for reducing the levelized cost of electricity of wind energy. This can be achieved by developing innovative condition monitoring methods. To this end, the use of the windowed scalogram difference (WSD) algorithm, based on wavelets, is proposed as an alternative solution, combined with current signature analysis (CSA). The electric generator is one of the major contributors to WT failure rates and downtime, and doubly-fed induction generators (DFIGs) are the dominant technology in variable-speed WTs. In the present work, operational data on an in-service WT DFIG are analyzed over a period of eight months, in contrast to the majority of the studies in this field, which rely on laboratory or simulated data. The evolution of the fault, namely rotor mechanical asymmetry, at an early stage, is analyzed and quantified implementing WSD to the stator current signals, supported by the previous diagnosis achieved through CSA. The combination of CSA and WSD shows strong potential for diagnosing and tracking, respectively, incipient faults in in-service WT DFIGs.

Wind power plants are becoming a generally accepted resource in the generation mix of many utilities. At the same time, the size and the power rating of individual wind turbines have increased considerably. Under these circumstances, the sector is increasingly demanding an accurate characterization of vertical wind speed profiles to estimate properly the incoming wind speed at the rotor swept area and, consequently, assess the potential for a wind power plant site. This paper describes a shape-based clustering characterization and visualization of real vertical wind speed data. The proposed solution allows us to identify the most likely vertical wind speed patterns for a specific location based on real wind speed measurements. Moreover, this clustering approach also provides characterization and classification of such vertical wind profiles. This solution is highly suitable for a large amount of data collected by remote sensing equipment, where wind speed values at different heights within the rotor swept area are available for subsequent analysis. The methodology is based on $z$ -normalization, shape-based distance metric solution, and the Ward-hierarchical clustering method. Real vertical wind speed profile data corresponding to a Spanish wind power plant and collected by using commercial Windcube equipment during several months are used to assess the proposed characterization and clustering process, involving more than 100 000 wind speed data values. All analyses have been implemented using open-source R-software. From the results, at least four different vertical wind speed patterns are identified to characterize properly over 90% of the collected wind speed data along the day. Therefore, alternative analytical function criteria should be subsequently proposed for vertical wind speed characterization purposes.

While wind turbine (WT) power capacities continue to increase and new offshore developments are being deployed, operation and maintenance (O&M) costs continue to rise, becoming the center of attention in the wind energy sector. The electric generator is among the top three contributors to failure rates and downtime of WTs, where the doubly fed induction generator (DFIG) is the dominant technology among variable speed WTs. Thus, the early detection of generator faults, which can be achieved through predictive maintenance, is vital in order to reduce O&M costs. The goal of this paper is to analyze a long-term monitoring campaign of an in-service WT equipped with a DFIG. A novel method named the harmonic order tracking analysis is used with two main objectives: first, to facilitate the data interpretation for non-trained maintenance personnel, and second, to reduce the amount of data that must be stored and transferred for the diagnosis of the DFIG. This method is applied and validated for the first time on an operating WT.