Study of the pitch behaviour of large‐scale wind turbines based on statistic evaluation

Liu, Deshun; Zhang, Fan; Dai, Juchuan; Xiao, Xiaocong; Wen, Zhang

doi:10.1049/rpg2.12166

Cited by 6 publications

(3 citation statements)

References 31 publications

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“…For example, in China, it aims to surpass a total installed capacity of 300 million kilowatts for wind power by 2025 following the "14th Five-Year Plan for Wind Power Development". However, as the scale of wind power rapidly expands, the operating environment for wind turbine equipment is increasingly harsh [1] . It is inevitable that wind turbines will occasionally fail due to the constant impact of wind forces and weather changes.…”

Section: Introductionmentioning

confidence: 99%

Improved SO-optimized SVM fault prediction for wind turbine pitch systems

Li,

et al. 2024

Preprint

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To address complex fault risk for wind turbine pitch systems working under the long-term operation and harsh environment, a fault prediction method based on Swarm Optimization (SO) algorithm and optimized Support Vector Machine (SVM) is proposed. Firstly, principal component analysis (PCA) is adopted to identify the core feature values from a datasets of wind turbine variable pitch systems containing operating data and feature extraction. Secondly, an Improved Swarm Optimization (ISO) algorithm is introduced to resolve sample imbalances and parameter selections in traditional SVM algorithms, which can improve the prediction accuracy and iteration speed. By utilizing Latin hypercube sampling, the algorithm searches for the optimal solution across all parameter spaces. It combines the frosting algorithm (RIME) with mirror imaging reverse learning to enhance adaptability to complex problems and improves the algorithm's convergence time by accelerating the iteration speed of the population. Lastly, the ISO-SVM algorithm is utilized to develop a fault prediction model for wind turbine pitch and performs excellently in fault prediction. The experimental results indicate the proposed method has achieved an increased prediction accuracy of 98.67% compared with other algorithms. Moreover, the proposed approach significantly improves the accuracy of fault prediction for the pitch system of wind turbines, addressing the primary issues in fault prediction for the pitch system of wind turbines.

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Section: Introductionmentioning

confidence: 99%

Improved SO-optimized SVM fault prediction for wind turbine pitch systems

Li,

et al. 2024

Preprint

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“…The blade pitch load plays a key role in the force distribution of the pitch system because its change will change other force states. Generally speaking, blade pitch load is affected by three aspects (Dai et al, 2010;Liu et al, 2021), namely, the gravity acting on the blade, the centrifugal force acting on the blade caused by the rotation of the wind rotor, and the aerodynamic force acting on the blade. More specifically, the gravity acting on the blade is determined by the mass distribution of the blade.…”

Section: Introductionmentioning

confidence: 99%

Research on force model and characteristics of large wind turbine pitch system based on SCADA data

Dai

Zhang

et al. 2023

Front. Energy Res.

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In large-scale wind turbines, the force state of the pitch system greatly influences safe operation and service life. This paper provides a novel method to estimate blade pitch load, bearing friction torque, and motor pitch torque. In this method, the force equilibrium equations are established by investigating the force of the pitch system under multiple operating conditions. The multidimensional BIN method is employed to classify the supervisory control and data acquisition (SCADA) data of wind turbines into several intervals. The multidimensional scatter data is processed in a single-valued way. Then, the estimating model of the pitch system forces is established by combining the obtained data and the equilibrium equations. Taking a 2 MW wind turbine as an example, the variation characteristics of blade pitch load, bearing friction torque, and motor pitch torque under multiple operating conditions are analyzed. Some interesting and valuable conclusions are obtained. For example, when the wind speed increases, the blade pitch load increases significantly in the maximum wind energy tracking region, but there is no obvious change in the observed constant power output region. The wind speed and azimuth have little effect on the bearing friction torque. The variation trend of motor pitch torque is consistent with that of blade pitch load in the maximum wind energy tracking region.

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“…As wind-speed fluctuations can significantly impact the values of relevant parameters such as impeller speed, gearbox speed, generator speed [7], pitch [8,9], and yaw system [10][11][12], their randomness must be considered. Liu et al [13] found that the mean wind speed and standard deviation had a certain correlation with the characteristic parameters of pitch operation, and the wind speed had a greater influence on the characteristic parameters of pitch distance operation. Xing et al [14] carried out the online state assessment of wind turbines by considering the fluctuation rate of wind speed and determined the division of the operating state level of the unit by analyzing the volatility trajectory of the historical data index parameters of the normal state unit.…”

Section: Introductionmentioning

confidence: 99%

Distribution Characteristics of Wind Speed Relative Volatility and Its Influence on Output Power

Shi-gang

Liu

2023

JMSE

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The stochastic fluctuations of wind speed and wind power curve modeling are complex tasks due to fluctuations in the difference between actual and theoretical power output, leading to a reduction in the accuracy of wind-power curve models. To address this issue, this paper proposes a normal distribution-modeling method based on relative volatility, which extracts the wind-speed variation patterns from the onsite SCADA (Supervisory Control And Data Acquisition) data, analyzes the correlation between wind-speed relative volatility and power relative volatility, and establishes a wind-power volatility-curve model to provide a basis for evaluating the efficiency of wind turbines. First, the definitions of relative volatility and probability vectors are provided, and a probability vector volatility-assessment function is designed to calculate the volatility-assessment index of the probability vector. Then, the relative volatility and probability vectors of wind speed are modeled, and features extracted from the onsite SCADA data, and characteristic parameters such as mean, standard deviation, and confidence interval of wind-speed relative volatility are statistically analyzed, as well as the wide-window coefficient, volatility-assessment index, attribute features (volatility center and volatility boundary), normal distribution features (mean and standard deviation) of the probability vectors of wind-speed relative volatility with different periods. The visualization descriptions of six typical probability vector distributions show that there is a correlation between the volatility assessment index of the probability vector based on relative volatility and the standard deviation of its distribution. Finally, the correlation between wind-speed relative volatility and power relative volatility is analyzed: in the maximum wind-energy tracking area, the derivative of power is linearly related to the derivative of wind speed, while in the constant power area, the derivative of the wind-energy utilization coefficient is linearly related to the derivative of wind speed. The conclusions obtained in this paper will provide a method reference for data processing to mine the parameter variation patterns and interrelationships of wind farm SCADA data and provide a basis for evaluating the power generation efficiency of wind turbines.

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Study of the pitch behaviour of large‐scale wind turbines based on statistic evaluation

Cited by 6 publications

References 31 publications

Improved SO-optimized SVM fault prediction for wind turbine pitch systems

Improved SO-optimized SVM fault prediction for wind turbine pitch systems

Research on force model and characteristics of large wind turbine pitch system based on SCADA data

Distribution Characteristics of Wind Speed Relative Volatility and Its Influence on Output Power

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