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Due to the recent rapid development of the wind energy industry, many wind turbines that have been operational over long periods will face degradation caused by aging effects; hence, an appropriate aging assessment method for wind turbines and their components is essential for optimizing the asset management and maintenance strategy of a wind farm. An aging assessment method is proposed for wind‐turbine electric‐pitch systems by introducing four individual aging indicators based on the examination of supervisory control and data acquisition (SCADA) and failure data, i.e. function, energy‐consumption, temperature, and reliability indicators. To obtain a reliable comprehensive assessment, an information‐fusion method has been developed based on a given reference value; the weighting factors in the information fusion were calculated based on the reference value, while reliability and robustness were verified using the SCADA and failure data from a wind farm over three years.

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The direct access to 2-amino-5-homoallylfurans has been realized by a palladium-catalyzed tandem cycloisomerization/Heck-type coupling between homoallenyl amides and allyltrialkylsilanes, using a novel DDQ/MnO combination as the efficient oxidant. The reaction exclusively affords γ-allylation products in good to excellent yields with broad substrate scope under exceptionally mild reaction conditions. It represents one of the rare examples of the Pd-catalyzed intermolecular Heck-type coupling of allytrialkylsilanes terminated by β-silyl elimination, thus complementing traditional allylation methods because of the excellent γ-selectivity.

Abstract:With the rapid development of wind energy, it is important to reduce operation and maintenance (O&M) costs of wind turbines (WTs), especially for a pitch system, which suffers the highest failure rate and downtime. This paper proposes a data-driven method for pitch-system condition monitoring (CM) by only using supervisory control and data acquisition (SCADA) data without any faults, which could be applied to reduce O&M costs of pitch system by providing fault alarms. The pitch-motor temperature is selected as the indicator, and three feature-selection algorithms are employed to select the most appropriate input parameters for modeling. Six data-driven algorithms are applied to model pitch-motor temperature and the support vector regression (SVR) model has the highest accuracy. The control-chart method based on the residual errors between model output and measured value is utilized to calculate the outliers, thus the abnormal condition could be clearly identified once the outliers appear for a period of time. The effectiveness of the proposed method is demonstrated by several case studies, and compared with the classification models. Due to the adaptive ability and low cost, the proposed approach is suitable for online CM of pitch systems, and provides a strategy for CM of new WTs.

Supervisory control and data acquisition data including comprehensive signal information have been widely applied to fault diagnosis. However, because of the complex operational condition of wind turbines, supervisory control and data acquisition data become complicated and abstract to study. This article proposes a pitch fault diagnosis method of wind turbines in multiple operational states using supervisory control and data acquisition data. According to the performance of characteristic parameters in nine operational states of wind turbines, Gaussian mixture model clustering and the analysis of normal performance curves are applied to model the relationship of pitch angle, rotor speed, and wind speed. Four cases have been studied to demonstrate the feasibility of the proposed method. The advantages of the proposed approach are as follows: (1) simplifying the analysis of supervisory control and data acquisition data through dividing the data into nine parts; (2) detecting pitch faults earlier than supervisory control and data acquisition monitoring system; (3) visualizing the abnormal behavior of the pitch system; and (4) improving the interpretability of the method with the incorporation of domain knowledge.

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Sprouting, a life history strategy found in woody plant communities, enables woody plants to persist in situ through disturbance events. The 'persistence niche' of sprouting has important influences on species coexistence, community assembly, and ecosystem stability. However, the mechanism of the 'persistence niche' in maintaining species diversity is not well understood. Based on data collected in a 5 ha plot in a mid-subtropical evergreen broad-leaved forest in the Gutianshan National Natural Reserve of Zhejiang Province, China, we analyzed the characteristics of sprouting and their relationships with species diversity. Our results revealed that the sprouting species had a great proportion of 63.95% in richness and a high proportion of 38.53% in abundance, especially a higher abundance proportion of 59.51% of potential sprouting at the community level. Sprouting occurred in most taxa, and there was high ability of sprouting in Fagaceae, Ericaceae, Hamamelidaceae, and Theaceae. There were significant negative correlations between abundance proportion of sprouting species and the biodiversity index of the community, despite no relationships between richness proportion of sprouting species and biodiversity index. Therefore, the sprouters could retain their position in forests and reduce biodiversity of the forest community. This trade-off of sprouting may result in the maintenance of community stability.