Ma Yuanchi, Liu Yongqian, Yang Zhiling, Yan Jie, Tao Tao, Infield David
State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China.
Wind Energy and Control Centre, Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XQ, UK.
Sensors (Basel). 2022 Nov 17;22(22):8891. doi: 10.3390/s22228891.
This paper focuses on how to identify normal, derated power and abnormal data in operation data, which is key to intelligent operation and maintenance applications such as wind turbine condition diagnosis and performance evaluation. Existing identification methods can distinguish normal data from the original data, but usually remove power curtailment data as outliers. A multi-Gaussian-discrete probability distribution model was used to characterize the joint probability distribution of wind speed and power from wind turbine SCADA data, taking the derated power of the wind turbine as a hidden random variable. The maximum expectation algorithm (EM), an iterative algorithm derived from model parameters estimation, was applied to achieve the maximum likelihood estimation of the proposed probability model. According to the posterior probability of the wind-power scatter points, the normal, derated power and abnormal data in the wind turbine SCADA data were identified. The validity of the proposed method was verified by three wind turbine operational data sets with different distribution characteristics. The results are that the proposed method has a degree of universality with regard to derated power operational data with different distribution characteristics, and in particular, it is able to identify the operating data with clustered distribution effectively.
本文聚焦于如何在运行数据中识别正常、降额功率和异常数据，这对于诸如风力发电机组状态诊断和性能评估等智能运维应用至关重要。现有识别方法能够从原始数据中区分出正常数据，但通常会将弃电数据作为异常值剔除。采用多高斯离散概率分布模型来表征风力发电机组SCADA数据中风速和功率的联合概率分布，将风力发电机组的降额功率作为隐藏随机变量。最大期望算法（EM）是一种从模型参数估计派生而来的迭代算法，用于实现所提概率模型的最大似然估计。根据风电散点的后验概率，识别出风力发电机组SCADA数据中的正常、降额功率和异常数据。通过三个具有不同分布特征的风力发电机组运行数据集验证了所提方法的有效性。结果表明，所提方法对于具有不同分布特征的降额功率运行数据具有一定的通用性，特别是能够有效识别具有聚类分布的运行数据。
