摘要
基于电网运行指标的态势感知是未来主要电网调度模式,电网运行异常识别是态势感知的重要内容。首先,从大电网调控运行需求出发构建了全面反映电网运行情况的综合指标体系;然后,采用由长短期记忆单元组成的自动编码机构建指标异常识别模型,在缺少电网运行指标异常数据的情况下,采用无监督的方式从电网正常运行状态下指标历史数据中学习指标的内在模式;最后,基于模型的重构误差分布提出了反映指标偏离正常状态的异常分数。将电网运行指标实时数据送入训练后的模型进行重构,当存在异常时会产生较大的异常分数,根据异常分数识别电网运行指标异常。实验结果表明,当电网运行指标出现异常时该模型可以根据异常分数进行有效识别,从而帮助电网调度人员及时感知电网运行风险,及时采取控制措施保障电网运行安全。
Situation awareness based on power grid operation indicators is the trend of future dispatching mode.Identification of anomalies is an important content of situation awareness.A comprehensive indicator system that comprehensively reflects the power grid operation is constructed.An auto-encoder composed of LSTM is used to construct the index abnormality identification model.In the absence of abnormal data on power grid operation indicators,an unsupervised approach is adopted to learn the internal model of the indicators from the historical data of the indicators under normal operating conditions of the power grid.On the basis of model reconstruction error distribution,this paper proposes an abnormal score reflecting the deviation of the index from the normal state.The real-time data of the grid operation indicators are sent to the trained model for reconstruction,and a large abnormal score will be generated when there is an abnormality.The experimental results show that the model can effectively identify the abnormal scores when the grid operation indicators are abnormal.This helps grid dispatchers perceive grid operation risks in a timely manner and take timely control measures to ensure grid security.
作者
庞传军
牟佳男
余建明
武力
PANG Chuanjun;MOU Jianan;YU Jianming;WU Li(NARI Group Corporation Co.,Ltd.(State Grid Electric Power Research Institute Co.,Ltd.),Nanjing 211106,China;Beijing Kedong Electric Power Control System Co.,Ltd.,Beijing 100192,China;Power Dispatching and Control Center,State Grid Corporation of China,Beijing 100031)
出处
《电力建设》
北大核心
2020年第7期17-24,共8页
Electric Power Construction
基金
国家电网公司科技项目(5100-201940013A-0-0-00)。
