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Abstract
To address the issue of a large calculation and difficult optimization for the traditional fault detection of a wind turbine-based pitch control system, a fault detection model, based on LightGBM by the improved Harris Hawks optimization algorithm (light gradient boosting machine by the improved Harris Hawks optimization, IHHO-LightGBM) for the wind turbine-based pitch control system, is proposed in this article. Firstly, a trigonometric function model is introduced by IHHO to update the prey escape energy, to balance the global exploration ability and local development ability of the algorithm. In this model, the fault detection false alarm rate is used as the fitness function, and the two parameters are used as the optimization objects of the improved Harris Hawks optimization algorithm, to optimize the parameters, so as to achieve the global optimal parameters to improve the performance of the fault detection model. Three different fault data of the pitch control system in actual operations of domestic wind farms are used as the experimental data, the Pearson correlation analysis method is introduced, and the wind turbine power output is taken as the main state parameter, to analyze the correlation degree of all the characteristic variables of the data and screen the important characteristic variables out, so as to achieve the effective dimensionality reduction process of the data, by using the feature selection method. Three established fault detection models are selected and compared with the proposed method, to verify its feasibility. The experimental data indicate that compared with other algorithms, the fault detecting ability of the proposed model is improved in all aspects, and the false alarm rate and false negative rate are lower.
Highlights
In recent years, governments of various countries increased the investment in the wind power industry, when the concepts of carbon neutrality and carbon peaking were put forward one after another, which led to the sharp decline in wind energy cost and the rapid development of wind energy [1]
To confirm the superiority of the IHHO-LightGBM in the pitch control system fault detection, yearly WT operation data of the SCADA system for a 1.5 MW wind turbine in a wind power plant in Inner Mongolia is selected, and the datasets, including pitch control emergency stop fault, pitch control motor fault, and pitch control power supply alarm, are selected from the normal working condition data of SCADA, which are recorded as dataset
Compared with the gradient boosting decision tree (GBDT), XGBoost, and LightGBM algorithms, the false alarm rate (FAR) of IHHO is reduced by 0.08–15.34%, and the false negative rate (FNR) is reduced by 0.14–3.3%
Summary
Governments of various countries increased the investment in the wind power industry, when the concepts of carbon neutrality and carbon peaking were put forward one after another, which led to the sharp decline in wind energy cost and the rapid development of wind energy [1]. The methods of wind turbine fault detection can be categorized in two ways, the model-based method [10] and the data-driven method [11]. The data-driven method has high accuracy and good robustness Since it does not require precise modeling, it can be freely applied to other wind turbines. TK et al implemented a stacking model based on RF, XGBoost, and GBDN, to distinguish the hitch of the gearbox, and solves the problems of traditional lifting algorithms, containing inefficiency, low accuracy, and bad timely function when processing huge amounts of engineering data of wind turbine operations. In order to address the problem of difficult optimization for the fault detection parameters of the wind turbine-based pitch control system, a novel element-based optimization algorithm, combined with the LightGBM algorithm, is applied in this article. HHO is used to select the optimal parameters of LightGBM, and a wind turbine fault detection based on LightGBM, optimized by improved HHO, is proposed to improve the fault detecting ability
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