摘要
为实现对SCADA(数据采集与监视)系统海量告警信息的缺陷辨识和风险分析,提出了一种基于自然语义分析的电网告警信息文本缺陷风险预警方法。将基于BiLSTM-Attention神经网络的语义分析技术与模糊化缺陷风险评估方法相结合,首先对告警信息文本进行数据预处理;利用Word2vec模型进行词嵌入向量表征后作为BiLSTM层输入;然后通过注意力机制增强对告警信息中与缺陷程度相关的特征并完成缺陷等级分类;最后利用缺陷评估方案进行N-1风险定级和预警。经测试和应用分析表明:基于该方法的判断模型能够精准完成告警信息的缺陷分类定级,实现告警信息的缺陷风险预警,可为调控人员缺陷处置提供辅助决策。
For defect identification and risk analysis on enormous alarm information of SCADA(supervisory control and data acquisition),the paper proposes an early warning method of power grid alarm information defect risk based on semantic analysis:combining the fuzzy defect risk assessment method with semantic analysis technique based on the BiLSTM-Attention to preprocesses the alarm information texts;then the Word2vec model is used for word embedding vectors representation and then used as the input of BiLSTM layer;the defect level feature representation in the alarm information was enhanced through attention mechanism,and the defect level classification of alarm information was realized.Finally,the defect assessment scheme was used for N-1 risk rating and warning.The testing and application analysis results demonstrate that the diagnosis model based on this method can accurately classify and rate the defect of the alarm information,and can realize the defect risk warning of the alarm information,providing decision-making support for dispatch personnel to deal with equipment defects.
作者
郑俊翔
施正钗
周泰斌
陆千毅
黄达铁
ZHENG Junxiang;SHI Zhengchai;ZHOU Taibin;LU Qianyi;HUANG Datie(State Grid Wenzhou Power Supply Company,Wenzhou Zhejiang 325000,China)
出处
《浙江电力》
2021年第8期33-39,共7页
Zhejiang Electric Power
基金
国网浙江省电力有限公司科技项目(5211WZ18007F)。
