基于方差变化率判据-四分位的风电场功率异常数据识别

风电场运行中产生了数量巨大的历史数据，而提升历史数据的质量是实现风电场高效智能运维的前提。为此，文中分析了风电场风功率数据的分布特征和形成机理，提出基于方差变化率判据-四分位法组合的风电场风功率异常数据识别方法。首先，利用物理规则对原始风功率曲线进行预处理，剔除明显异常的数据；然后，利用风功率方差变化率判据法识别并清洗风功率曲线的堆积型异常功率数据点，判据的阈值借助箱型图自动获取；同时，利用四分位法识别并清洗剩余的离散型异常数据点；最后，通过算例验证了所提算法的可行性。研究结果表明所提算法具有易实现、效率高和通用性强的优点，其异常识别效果优于局部离群因子(local outlier factor,LOF)算法和Thompson tau-四分位算法，其耗时比LOF和Thompson tau-四分位算法分别减少9.6 s和0.49 s，且在5个不同位置的风电场验证了所提算法的通用性。

Anomaly data identification of wind power in wind farm with the criterion of variance change rate and quartile

A huge amount of historical data has been generated during the operation of wind farms,and the improvement of data quality is the prerequisite work for achieving high-efficient and intelligent maintenance of wind farms. Therefore,the distribution characteristics and formation mechanism of wind power data in wind farms are analyzed,and a variance change rate criterion and quartile combined method to identify abnormal wind power data is proposed. Firstly,the original wind power curve is preprocessed by physical rules,and the obviously abnormal data is eliminated. Then,the abnormal power data points of the accumulation type of the wind power curve are identified and cleaned by the wind power variance change rate criterion method,and the threshold value of the criterion is automatically obtained through the box plot. After that,the quartile method is used to identify and clean the discrete abnormal data points. Finally,the feasibility of the proposed algorithm is verified by an example. The results show that the proposed algorithm has the advantages of easy implementation,high efficiency,and strong universality. The anomaly recognition performance of the proposed method is superior to the local outlier factor (LOF) or Thompson tau-quartile algorithms,and the value of its time consumption is 9.6 s or 0.49 s lower than that of the LOF or Thompson tau-quartile algorithm,respectively. The universality of the proposed algorithm has been verified at 5 wind farms in different locations.