基于BP神经网络的光伏电站在线监控系统设计研究
Research on the Design of Online Monitoring System for Photovoltaic Power Station Based on BP Neural Network
常进1
作者信息
- 1. 山东电力工程咨询院有限公司,山东济南 250013
- 折叠
摘要
文章设计一种基于反向传播(Back Propagation,BP)神经网络的光伏电站在线监控系统,通过分层架构实现数据采集、预处理、故障诊断以及预测预警功能.系统在某光伏电站的实证研究表明,设计的基于BP神经网络的监控系统在预警准确性、误报率、提前时间方面显著优于传统阈值判断方法,为光伏电站的智能运维提供有效支持.
Abstract
In this paper,an online monitoring system of photovoltaic power station based on Back Propagation(BP)neural network is designed,and the functions of data acquisition,preprocessing,fault diagnosis and prediction and early warning are realized through hierarchical architecture.The empirical study of the system in a photovoltaic power station shows that the monitoring system based on BP neural network is significantly superior to the traditional threshold judgment method in early warning accuracy,false alarm rate and lead time,which provides effective support for intelligent operation and maintenance of photovoltaic power stations.
关键词
反向传播(BP)神经网络/光伏电站/在线监控Key words
Back Propagation(BP)neural network/photovoltaic power station/online monitoring引用本文复制引用
出版年
2024
NETL
NSTL
万方数据