It is difficult to optimize the fault model parameters when Extreme Random Forest is used to detect the electric pitch system fault model of the double-fed wind turbine generator set. Therefore, Extreme Random Forest which was optimized by improved grey wolf algorithm (IGWO-ERF) was proposed to solve the problems mentioned above. First, IGWO-ERF imports the Cosine model to nonlinearize the linearly changing convergence factor α to balance the global exploration and local exploitation capabilities of the algorithm. Then, in the later stage of the algorithm iteration, α wolf generates its mirror wolf based on the lens imaging learning strategy to increase the diversity of the population and prevent local optimum of the population. The electric pitch system fault detection method of the wind turbine generator set sets the generator power of the variable pitch system as the main state parameter. First, it uses the Pearson correlation coefficient method to eliminate the features with low correlation with the electric pitch system generator power. Then, the remaining features are ranked by the importance of the RF features. Finally, the top N features are selected to construct the electric pitch system fault data set. The data set is divided into a training set and a test set. The training set is used to train the proposed fault detection model, and the test set is used for testing. Compared with other parameter optimization algorithms, the proposed method has lower FNR and FPR in the electric pitch system fault detection of the wind turbine generator set.

中文翻译：

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基于IGWO-ERF的风电变桨距系统故障检测

采用极限随机森林检测双馈风电机组电变桨距系统故障模型时，故障模型参数难以优化。因此，提出了通过改进灰狼算法（IGWO-ERF）优化的极端随机森林来解决上述问题。首先，IGWO-ERF 引入 Cosine 模型将线性变化的收敛因子 α 非线性化，以平衡算法的全局探索和局部开发能力。然后，在算法迭代的后期，α狼根据透镜成像学习策略生成自己的镜像狼，以增加种群的多样性，防止种群的局部最优。风力发电机组电变桨系统故障检测方法以变桨系统的发电机功率为主要状态参数。首先，它采用皮尔逊相关系数法消除与电动变桨系统发电机功率相关性低的特征。然后，其余特征按 RF 特征的重要性排序。最后，选取前N个特征构建电动变桨系统故障数据集。数据集分为训练集和测试集。训练集用于训练提出的故障检测模型，测试集用于测试。与其他参数优化算法相比，该方法在风力发电机组电变桨系统故障检测中具有较低的FNR和FPR。