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The primary wind turbines' in-service performance evaluation method is mining and analyzing the SCADA data. However, there are complex mathematical and physical relationships between multiple operating parameters, and so far, there is a lack of systematic understanding. To solve this issue, the distribution of wind turbines' operating parameters was first analyzed according to the characteristics of the energy flow of wind turbines. Then, the correlation calculation was performed using the Spearman correlation coefficient method based on the minute-level data and second-level data. According to the numerical characteristics of the nacelle vibration acceleration, the data preprocessing technology sliding window maximum (SWM) was proposed during the calculation. In addition, taking temperature correlation as an example, two-dimensional scatter (including single-valued scatter) and three-dimensional scatter features were combined with numerical analysis and physical mechanism analysis to understand the correlation characteristics better. On this basis, a quantitative description model of the temperature characteristics of the gearbox oil pool was constructed. Through this research work, the complex mathematical and physical relationships among the multi-parameters of the wind turbines were comprehensively obtained, which provides data and theoretical support for the design, operation, and maintenance. [ABSTRACT FROM AUTHOR]

Compared to theoretical calculations, field blade load testing can more accurately obtain actual load characteristics. The main challenge is the field relationship correction of strain vs load. The initial state of the strain sensing installation can be divided into two scenarios. One scenario is to install before the blade is lifted and another is to install online during the service of wind turbines. The key analysis is conducted on the field correction of the relationship of strain vs load, and corresponding correction strategies are constructed. A decoupled calculation model for strains in the edgewise direction induced by the aerodynamic moment and the gravity moment is proposed. Ground and online experiments for blade strain testing have been carried out. Through the combination of strain testing data and SCADA data, some new insights have been gained on strain characteristics. For example, the mapping coefficient of strain vs load induced by the aerodynamic moment varies under different operating conditions, while the mapping coefficient of strain vs load induced by gravitational moment is relatively stable. When the power of the wind turbine is 552 kW, 853 kW, and 1379 kW, the coefficients induced by the aerodynamic moment are 2270 N m/ μɛ , 2480 N m/ μɛ , and 3224 N m/ μɛ , respectively. [ABSTRACT FROM AUTHOR]

Due to the complex and variable conditions under which wind turbines operate, existing working condition classification methods are inadequate for condition assessment of the main transmission system. Because working conditions are too few after classification, it cannot effectively describe the complex and variable working conditions of wind turbine. This can lead to high false-alarm rates in the condition monitoring, which affect normal operations. This paper proposes a working condition classification method for the main transmission system of wind turbines based on supervisory control and data acquisition (SCADA) data. Firstly, correlation analysis of SCADA data acquired by wind farm is used to select the parameters relevant to the main transmission system. Secondly, according to the wind turbine control principle, the working conditions are initially divided into four phases: shutdown, start-up, maximum wind energy tracking, and constant speed. The k-means clustering algorithm is used to subdivide the maximum wind energy-tracking phase and constant speed phase, which account for a larger proportion of the working conditions, to achieve better classification. Finally, a case study is used to demonstrate the calculation of alarm thresholds and alarm rates for each working condition. The results are compared with the direct use of k-means clustering for working condition classification. It is concluded that the proposed method can significantly reduce the false-alarm rate of the vibration detection process. [ABSTRACT FROM AUTHOR]

• It is difficult to measure the dynamic status because of the wind turbine's far field working. A method for vibration analysis of wind turbine blades based on videogrammetry is proposed. • To obtain the space arc-like path coordinates of rotating blades, a videogrammetry experimental platform for rotating wind power blades was constructed. • The vibration information and modal frequency of wind turbine blades under operating conditions are calculated by extracting and analyzing the local periodic trajectory in a short time. • The vibration analysis of wind turbine blades based on videogrammetry method is compared with the calculation results of FEA and hammering experiment. The average error between the proposed method and the two methods is 4.355% and 4.63% respectively, within 5%. • This research can provide dynamic videogrammetry modal identification method and technical prototype for wind turbine blade condition monitoring and maintenance. It is difficult to measure the dynamic status because of the far fields working environment of the wind turbine. A method for vibration analysis of wind turbine blades based on videogrammetry is proposed. In order to obtain the space arc-like path coordinates of rotating blades, a videogrammetry experimental platform for rotating wind power blades was constructed. The space arc-like blade measuring points are collected and the error analysis is carried out by fitting the circular arc-like paths. The natural frequency of blade vibration is calculated based on Ensemble Empirical Mode Decomposition (EEMD) and fast Fourier transform (FFT). Evaluate the accuracy of the proposed method through finite element analysis (FEA) and hammering experiment. The results show that the modal & frequency from the presented method are basically consistent with the results of FEA and hammering experiment. It provides a theoretical method and technical means for measuring the dynamic vibration characteristics of blades. [ABSTRACT FROM AUTHOR]