基于暂态负载大数据的光伏并网异常智能告警算法  

Intelligent alarm algorithm for abnormal photovoltaic grid connection based on transient load big data

作　　者：杨燕伟 YANG Yanwei(Huaneng Lancang River New Energy Co.,Ltd.,Kunming 650051,China)

机构地区：[1]华能澜沧江新能源有限公司,云南昆明650051

出　　处：《无线互联科技》2024年第15期57-59,共3页Wireless Internet Science and Technology

摘　　要：光伏并网异常智能告警目前受限于静态数据,导致告警准确性低。为此,文章提出基于暂态负载大数据的异常智能告警算法。该算法通过构建暂态负载监测数据采集模型,提取中心权重向量并描述电力负荷变化,采用自回归过滤和时序特征子序列变换(Time Series Shapelet Transform,Shapelet,TSSTS)处理数据,提取时序轨迹特征,并基于卷积神经网络构建异常分级告警结构,实现深度学习并准确输出告警结果。实验结果显示,该算法的曲线下面积(Area Under the Curve,AUC)值高达0.96,满足光伏并网异常检测要求。The abnormal intelligent alarm of photovoltaic grid connection is currently limited to static data,resulting in low alarm accuracy.To this end,this paper proposes an abnormal intelligent alarm algorithm based on transient load big data.By constructing a transient load monitoring data acquisition model,the algorithm extracts the center weight vector and describes the change of power load,uses self-regression filtering and time series shapelet transform,shapelet(TSSTS)to process the data,and extracts the timing trajectory characteristics.Finally,the paper constructs an abnormal classification alarm structure based on convolutional neural network,realizes deep learning and accurately outputs alarm results.Experiments show that the area under the curve(AUC)value of the algorithm is as high as 0.96,which meets the requirements of photovoltaic grid-connected anomaly detection.

关 键 词：暂态负载 大数据 光伏并网 特征提取 异常状态 智能告警 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]