基于STL-Bayesian时空模型的分布式光伏系统异常检测
Anomaly Detection for Distributed Photovoltaic Systems Based on STL-Bayesian Spatio-Temporal Model
刘韵艺 1汤渊 1苏盛 2吴裕宙 1王晓倩2
作者信息
- 1. 广东电网有限责任公司东莞供电局,广东东莞 523008
- 2. 长沙理工大学电气与信息工程学院,湖南长沙 410004
- 折叠
摘要
分布式光伏发电系统一般不配备多种类的传感器和监测设备,反映设备运行状态且可用于异常检测的数据有限.提出了基于STL-Bayesian时空模型的光伏异常状态检测方法,利用气象在时空上的传递性,挖掘光伏发电出力的关联性进而完成异常检测.首先,用季节性分解(seasonal and trend decomposition using loess,STL)将光伏发电有功功率时序数据分解为 3 个分量;然后,研究不同长度数据输入对分解结果的影响和区域内分量的时空分布特性;接着,通过构建贝叶斯模型分别对趋势分量和剩余分量做短期和超短期空间插值,得到区域内光伏出力;最后,计算真实值与回归值的推土机距离(earth move's distance,EMD)用于检测异常状态.算例分析表明,所提模型在分布式光伏场景检测可逆异常和不可逆异常状态均有较高准确率.
Abstract
Distributed photovoltaic(PV)power generation systems generally do not come equipped with a variety of sensors and monitoring devices,limiting the data available for reflecting equipment operation and conducting anomaly detection.This article proposes a PV anomaly detection method based on the STL-Bayesian spatio-temporal model,which utilizes the spatio-temporal transferability of meteorological data to uncover the correlation of PV power output and perform anomaly detection.Firstly,the seasonal and trend decomposition using Loess(STL)is employed to decompose the PV active power time series data into three components.Then,the influence of different lengths of input data on the decomposition results and the spatio-temporal distribution characteristics of the components within the region are investigated.Subsequently,Bayesian models are constructed to perform short-term and ultra-short-term spatial interpolation on the trend component and the residual component,respectively,so as to obtain the PV output within the region.Finally,the earth move's distance(EMD)between the actual values and regression values is calculated to detect abnormal states.The analysis of the algorithm shows that the model has a high accuracy in the detection of both reversible and irreversible anomalies under distributed PV scenarios.
关键词
分布式光伏/时序分解/空间插值/异常状态检测/时空分布特性Key words
distributed photovoltaics/time series data decomposition/spatial interpolation/abnormal states detection/spatio-temporal distribution characteristics引用本文复制引用
基金项目
国家自然科学基金资助项目(51777015)
中国南方电网有限公司科技项目(031900KK 52220039)
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据