浙江电力2025，Vol.44Issue(5)：42-52,11.DOI:10.19585/j.zjdl.202505005
基于图像特征检测的光伏异常数据识别算法
A photovoltaic anomaly data identification method based on image feature detection
摘要
Abstract
The supervisory control and data acquisition(SCADA)system of photovoltaic(PV)power plants records a substantial volume of operational and maintenance data that is crucial for the routine maintenance.However,ab-normal data resulting from extreme weather,sensor failures,and other factors significantly degrade data quality,thus affecting PV power forecasting and routine maintenance.To address this,this paper introduces an anomaly data identification algorithm based on image feature detection and dual-threshold processing.This method maps numeri-cal data to images,transforming the anomaly detection problem into an image processing problem.First,abnormal data is categorized into negative value anomalies,discrete anomalies,and stacked anomalies.Discrete anomalies are identified based on the density of image data.Next,stacked anomalies are detected using Canny edge detection and Hough transform,with a dual threshold image processing mechanism introduced to enhance the method's gener-alizability.Finally,the proposed method is compared with traditional statistical methods using a real-world dataset,demonstrating its adaptability.关键词
光伏电站/异常数据识别/图像处理/Canny边缘检测/Hough变换/双阈值Key words
photovoltaic power plant/anomaly data identification/image processing/Canny edge detection/Hough transform/dual threshold引用本文复制引用
裘愉涛,张磊,周开运,严慜,孙金通,龙寰..基于图像特征检测的光伏异常数据识别算法[J].浙江电力,2025,44(5):42-52,11.基金项目
江苏省碳达峰碳中和科技创新专项(BE2023093-2) （BE2023093-2）
国网浙江省电力有限公司科技项目(B311UZ23000A) （B311UZ23000A）
