电力工程技术2026，Vol.45Issue(5)：3-14,12.DOI:10.12158/j.2096-3203.2026.05.001
基于机理模型和数据驱动融合的电压暂降响应特性分析
Analysis for voltage sag response characteristics based on the fusion of mechanism models and data-driven approaches
摘要
Abstract
Voltage sag response characteristics reflect the time required for the physical parameters of an industrial process to cross the threshold when subjected to a voltage sag,which can be characterized by the process immunity time(PIT)curve.However,due to the lack of measured data in actual production processes,existing methods for analyzing voltage sag response characteristics suffer from issues such as insufficient consideration of influencing factors and difficulty in determining parameter values,making it impossible to accurately fit the PIT curve.Therefore,this paper proposes a method for analyzing voltage sag response characteristics based on the fusion of mechanism models and data-driven approaches.Firstly,the sensitive equipment in typical industrial processes and their con-nection relationships are analyzed to establish a mechanism model of the PIT curve and obtain mechanism data.Then,an adaptive weight allocation strategy is adopted to dynamically assign weights to the mechanism data and measured data,strengthening the learning of the measured data by the Wasserstein generative adversarial network with gradient penalty(WGAN-GP)and enabling the model to more accurately capture the characteristics of the measured data.Next,a bidirectional long-short-term memory network is used to extract temporal features and capture the time correlation between data,thereby improving the quality of the generated data.Finally,a feature-aware loss and a dynamic reconstruction loss are constructed to constrain the model training process through the deep features and dynamic characteristics of the data,thus enhancing the fitting accuracy of the PIT curve.The proposed method is applied to a simulation experiment of a high-power electrically driven centrifugal compressor system in a natural gas compression station in southwestern China.The results verify the effectiveness and accuracy of the method proposed in this paper.关键词
电能质量/电压暂降/敏感工业过程/过程免疫时间(PIT)/生成对抗网络/双向长短期记忆网络Key words
power quality/voltage sag/sensitive industrial process/process immunity time(PIT)/generative adversarial network/bidirectional long short-term memory network分类
信息技术与安全科学引用本文复制引用
徐方维,唐佳飞,郭凯,刘城,徐琳,丁理杰..基于机理模型和数据驱动融合的电压暂降响应特性分析[J].电力工程技术,2026,45(5):3-14,12.基金项目
国家自然科学基金资助项目(52277113) （52277113）
