Fault Detection for Pitch System of Wind Turbine-Driven Doubly Fed Based on IHHO-LightGBM

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.

H3O-LGBM: hybrid Harris hawk optimization based light gradient boosting machine model for real-time trading

“…The performance of the proposed Hybrid HHO-model is compared with different architectures such as LightGBM-based Particle Swarm Optimization (PSO) (Tang et al 2021 ), Improved Harris hawk optimization (IHHO-LightGBM) (Tang et al 2021 ), and Bayesian optimization algorithm (BOA) based LightGBM model (Huang et al 2022 ). The comparison is mainly held for a single-step horizontal prediction in terms of RMSE, MAPE, and MAE.…”

Mining passenger-seeking road patterns of high-occupancy taxis based on cross-tabulation analysis

“…In this study, the main study is on the distribution of primary and secondary roads, and the general branch roads have fewer hotspots and are difficult to gain passengers. The factors influencing the number of taxi passengers can be classified into two categories, the first being hotspot attraction factors [5,6] and the second being road factors. The interaction between the number of taxi passengers and the hotspot attraction factor and road factor, respectively, is shown in Figures 6 and 7.…”

A Fault and Capacity Loss Prediction Method of Wind Power Station under Extreme Weather

“…The model contour coefficient was 0.89, and the clustering results were obtained; the clustering results are shown in Table 1, and the Cluster distribution map is shown in Figure 6. At the same time, in order to characterize the operation status of the cluster, the average intracluster correlation coefficient (AICC) of each unit in the cluster is used in Equation (11) [32], and the unit with the highest AICC in the cluster is the representative unit, and its operation status is defined as the cluster operation status.…”

“…Among them, statistical methods based on machine learning (ML) can automatically mine the connections between data features [6,7], are simple to model, have fast calculation speed and high prediction accuracy, and have been widely used. Literature [8] selected key features according to the icing mechanism in extremely cold weather and used particle swarm optimization (PSO) optimized support vector machine (SVM) to predict the icing fault of wind turbine blades; Zhang et al [9] studied the use of monitoring and data acquisition system data to detect icing on wind turbine blades, and proposed a prediction model based on the random forest (RF) algorithm; The study fully considers the mixed characteristics of short-term and long-term icing effects based on the physical extraction of bottom icing, and uses these characteristics to establish a Stacked-extreme gradient boosting (XGBoost) model to realize leaf icing diagnosis [10]; Tang et al [11] proposed a fault detection model for doubly-fed wind turbine pitch system based on IHHO-light gradient boosting machine (LightGBM); literature [12] introduced a modeling method using weather research and forecasting models to predict the failure probability of wind turbines under typhoon weather. In addition, neural network methods have also been applied due to their efficient feature mining capabilities, including artificial neural network [13], long short-term memory [14], recurrent neural network [15], etc.…”