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Load identification is the premise of wind turbine blade design and control. In wind farms, due to the lack of a thorough understanding of the blade loads, catastrophic accidents occur occasionally, and the causes of some accidents cannot be reasonably explained. To further promote the depth of blade load research and application, the development of the blade industry, load theory, load measurement, and load control of large-scale Horizontal Axis Wind Turbines (HAWTs) are comprehensively reviewed in this paper. The key issues and the corresponding progress involved in the blade research and application are discussed in detail, covering aerodynamic load calculation and analysis, load measurement in both laboratory and field environments, and load control of wind turbine blades. Additionally, the main matters needing attention in the blade research and application in the future are outlined, including more accurate and faster load calculation, strain perception and load mapping in the field, reconstruction of historical service load, and time-history optimization of the load control algorithm. Some effective countermeasures, e.g., reconstructing the historical service loads on wind turbine blades with the help of both blade load sensors and the Supervisory Control and Data Acquisition (SCADA) system, are also proposed. • A comprehensive assessment of blade load research of wind turbines is presented. • The key issues and their progress involved in blade research and application are discussed. • Future developments and challenges in blade load research are proposed. [ABSTRACT FROM AUTHOR]

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]

Highlights • Multiple-units SCADA data is used to comparatively investigate the energy characteristics. • A newly improved algorithm for calculating power coefficient is proposed; • A fast and operable data dividing method is presented; • Understanding of mountain wind farm has been enriched and deepened. Abstract The energy output characteristics of mountain wind farms are more complex and unpredictable than that of flat wind farms because of complex mountainous terrain. Some questions about the energy output characteristics of mountain wind farms are still unclear. For example, the effect of local wind resource characteristics on the energy output of mountain wind farms. In another scenario, single-unit data from Supervisory Control and Data Acquisition (SCADA) system are used to investigate the performance of wind turbines in some cases. But, single-unit SCADA data may not be very comprehensive. To fill this gap, multiple-units SCADA data of 2 MW wind turbines are used to comparatively investigate the energy characteristics of wind turbines in a mountain wind farm. The main contribution of this paper is to obtain a new and comprehensive understanding of the actual characteristics of the energy output of mountain wind farms, rather than staying in the design stage or general theoretical analysis. Specifically, the following issues, including (1) influence of wind speed on energy output, (2) influence of wind direction on energy output, (3) relationship between rotor speed and energy output, (4) power coefficient, have been discussed in depth. To overcome the influence of inherent deviation in wind speed measurement and the inertia of the wind rotor, a newly improved algorithm for calculating power coefficient is proposed. Creatively, a fast and operable data dividing method is presented, and the power coefficients of different operating regions are given. Through the investigation, the influence of local wind resource characteristics on the energy output is obtained. It is found that in the same mountain wind farm, the energy output curves of different wind turbines are different. For instance, energy outputs for four wind turbines in December 2015 are 434 MW, 614 MW, 205 MW, 255 MW, respectively. [ABSTRACT FROM AUTHOR]

Due to the time-varying wind direction, yaw operation of wind turbines is a common state. Under yaw, the aerodynamic behavior of wind turbines is complicated, and also causes complex energy capture performance. To clarify some vague knowledge, a detailed investigation of yaw effect based on SCADA data is carried out. Firstly, a yaw coefficient definition and its specific calculation method are presented. Furthermore, to analyze the energy capture mechanism, the loss factor of energy capture (power coefficient) is subdivided into aerodynamic loss factor and inertia loss factor, which means both the effects of aerodynamic characteristic and mechanical inertia on energy capture are considered. According to this understanding, power coefficient is expanded as a function of four factors. Then, a single-valued processing method is employed to investigate the relationship between wind speed and output power. Subsequently, relationship between yaw coefficient and output power is investigated. Comparative investigations are carried out by two different methods, that is, least square fitting (LSF) method and kernel density estimation (KDE) method. Also, characteristics of power coefficient and rotor torque under yaw are investigated. Effect laws of yaw coefficient on wind turbine power, power coefficient, and rotor torque are obtained. Finally, the relationship between the internal control mechanism and external output of wind turbines is discussed. [ABSTRACT FROM AUTHOR]

Ageing of a wind turbine and its components is inevitable. It will affect the reliability and power generation of the turbine over time. Therefore, performing the ageing assessment of wind turbines is of significance not only to optimize the operation and maintenance strategy of the wind turbine but also to improve the management of a wind farm. However, in contrast to the significant number of research on wind turbine condition monitoring and reliability analysis, little effort was made before to investigate the ageing led performance degradation issue of wind turbines over time. To fill such a technology gap, four SCADA-based wind turbine ageing assessment criteria are proposed in this paper for measuring the ageing resultant performance degradation of the turbine. With the aid of these four criteria, a reliable information fusion based wind turbine ageing assessment method is developed and verified in the end using the real wind farm SCADA data. It has been shown that the proposed method is effective and reliable in performing the ageing assessment of a wind turbine. [ABSTRACT FROM AUTHOR]

Wind resource characteristics in a mountain wind farm is more complicated and changeable than that in a flat wind farm due to the influence of complex mountain terrain-topography on air flow. Although there have been a large number of investigations on wind resource characteristics, the local wind resource characteristics in a mountain wind farm are still a lack. To fill this knowledge gap, high-resolution wind data are extracted for investigating from multiple-unit SCADA data, that is, wind data are collected from four selected wind turbines with SCADA system, which means wind data are from four different locations. For each location, more than thirty million sets of wind data are extracted for investigation in 2015. Then, several kinds of probability density functions (PDFs) are compared, and one-dimensional and multidimensional kernel density estimation method is selected for the investigation of the frequency distribution. Data preprocessing methods of both wind speed and wind direction are also presented. Finally, the results of this investigation reveal the specific change characteristics of wind speed and wind direction in the investigated mountain wind farm. Yearly mean wind speeds for four different locations are 3.8 m/s, 5.1 m/s, 5.4 m/s and 5.6 m/s, respectively. [ABSTRACT FROM AUTHOR]

Power coefficient C p is an important parameter for wind turbine design and operational control. Wind speed is the basic calculation parameter of the power coefficient. Since the anemometer is fixed on the nacelle, the measured wind speed is different from the wind speed in front of the wind rotor. Calculation error will produced if the directly measured wind speed is used to calculate the power coefficient. In this paper, a calculation model of the wind speed in front of the wind rotor is presented based on the SCADA data and the aerodynamic theory. Two power coefficient calculation methods are proposed. One is based on the statistical data and the other is based on the real-time data. An actual calculation result for a 2 MW wind turbine shows that the power coefficient is near or greater than 0.593 (the theoretical maximum value) if the directly measured wind speed is used during the maximum power point tracking (MPPT). After wind speed correction, the power coefficient is reduced to 0.397 that is more realistic. When using the real time data, the power coefficient is time-varying even in the region of MPPT, since wind speed is time-varying and the wind rotor rotational speed regulation is delayed due to the wind rotor moment of inertia. [ABSTRACT FROM AUTHOR]

Variable pitch technology is an indispensable key technology of large-scale wind turbines. The reliable pitch mechanism is the basic guarantee for achieving variable pitch. At present, the main problem with the design and maintenance of the variable pitch mechanism is that the pitch load is not clearly known. This paper focuses on obtaining pitch load characteristics through extracting SCADA (Supervisory Control and Data Acquisition) data. Here, the pitch load refers to the resistance moment to be overcome when the wind turbine blade is rotated on its own axis. From the data collected by the SCADA system, although the edgewise moment and the flapwise moment cannot be obtained, the pitch torque (load) can be extracted indirectly. This provides data support for the research. Specifically, the pitch moment is obtained by indirect calculation of the pitch motor current. Then, the effects of the wind speed, rotor speed, hub angle and pitch angle on the pitch load are theoretically analyzed. To obtain more reliable results, data preprocessing algorithms are presented to consider the data filtering range, the elimination of abnormal values and data dispersity. Subsequently, the influence mechanisms of wind speed, rotor speed, hub angle and pitch angle on the pitch load are investigated in detail based on the SCADA data. [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]