基于GRU-贝叶斯的分布式光伏功率异常检测方法
GRU-BAYESIAN BASED METHOD FOR DISTRIBUTED PHOTOVOLTAIC POWER ANOMALY DETECTION
王耀龙 1吴裕宙 2刘韵艺 2李彬 1苏盛1
作者信息
- 1. 长沙理工大学电气与信息工程学院,长沙 410004
- 2. 广东电网有限责任公司东莞供电局,东莞 523000
- 折叠
摘要
为有效识别分布式光伏故障系统,提出一种基于GRU-贝叶斯神经网络的分布式光伏功率异常检测方法.首先,选取晴天为检测场景,降低天气因素的干扰;然后,引入灰色绝对关联度算法,利用同地区光伏系统出力的相似性,筛除不合格光伏出力数据,构建光伏用户正常的光伏出力数据集.使用GRU-贝叶斯神经网络训练得到用户正常的光伏功率区间再进行检测.最后,用实际数据进行算例分析,表明所提方法的可行性和有效性.
Abstract
For the effective identification of faults in distributed photovoltaic(PV)systems,this study proposes a GRU-Bayesian neural network-based method for anomaly detection in the power output of distributed PV systems.Firstly,sunny days are selected as the detection scene to reduce the interference of weather factors.Then,the gray absolute correlation degree algorithm is introduced to screen out unqualified PV output data by utilizing the similarity of PV system output in the same region and construct a dataset of normal PV output for users.The GRU-Bayesian neural network is used to train and obtain the normal PV power interval for detection.Finally,actual data is used for case analysis,demonstrating the feasibility and effectiveness of the proposed method.
关键词
分布式发电/光伏/贝叶斯神经网络/异常检测/灰色关联分析/门循环单元Key words
distributed power generation/PV/Bayesian networks/anomaly detection/grey correlation analysis/GRU引用本文复制引用
出版年
2024
NETL
NSTL
万方数据