摘要
为捕捉序列中相隔较远信息之间的联系,设计了基于门控循环单元深度学习算法的舰船用电数据挖掘方法。利用采集终端实时采集发电机组、甲板机械等设备的原始舰船用电数据;将原始用电数据转换为标准格式,根据舰船电力系统电能质量分析与故障诊断等需求,运用深度学习算法中的门控循环单元,在原始用电数据内捕捉序列中相隔较远信息之间的联系,挖掘舰船用电数据规律特征,为舰船用电管理优化提供决策支持。实验证明:该方法可有效实时采集原始舰船用电数据,并有效挖掘舰船用电数据规律特征;应用该方法后,可有效提升舰船电力系统负荷预测精度。
Designed a deep learning algorithm for ship electricity data mining based on gated recurrent units to capture the connections between distant information in the sequence.Real time collection of raw ship electricity data from generator sets,deck machinery,and other equipment using collection terminals;Convert the original power consumption data into a standard format,and use the gate controlled loop unit in deep learning algorithms to capture the connections between distant information in the sequence within the original power consumption data,based on the requirements of power quality analysis and fault diagnosis of ship power systems.Explore the regular features of ship power consumption data,and provide decision support for optimizing ship power management.Experimental results have shown that this method can effectively collect real-time raw ship electricity data and effectively mine the regular features of ship electricity data;After applying this method,the accuracy of load forecasting for ship power systems can be effectively improved.
作者
王晓辉
高鹏翔
孙守强
WANG Xiaohui;GAO Pengxiang;SUN Shouqiang(Qingdao Huanghai University,Qingdao 266427,China;Qingdao University,Qingdao 266500,China)
出处
《舰船科学技术》
北大核心
2025年第9期170-174,共5页
Ship Science and Technology
基金
山东省教育厅本科教学改革研究项目(M2022328)。
关键词
深度学习算法
舰船用电数据
数据挖掘
长短期记忆网络
deep learning algorithms
ship power consumption data
data mining
long short term memory network
作者简介
王晓辉(1983-),女,硕士,副教授,研究方向为数据分析及数据挖掘。
