自适应迭代学习的调度自动化系统运行指标预测方法  

Adaptive Iterative Learning for Predicting Metrics of Dispatching Automation System

作　　者：沈嘉灵 季学纯 高尚 王宇冬 陈子韵 李昊 SHEN Jialing;JI Xuechun;GAO Shang;WANG Yudong;CHEN Ziyun;LI Hao(NARI Group Co.,Ltd.(State Grid Electric Power Research Institute Co.,Ltd.),Nanjing 211100,China)

机构地区：[1]南瑞集团有限公司(国网电力科学研究院有限公司),南京211100

出　　处：《计算机工程与应用》2025年第4期368-376,共9页Computer Engineering and Applications

基　　金：国家电网公司总部科技项目(SGFJ0000DKJS2310434)。

摘　　要：在调度自动化系统运行智能风险预警场景下,针对海量指标使用单一算法存在准确率不高以及未根据实时数据特征变化迭代更新等问题,提出一种自适应迭代学习的调度自动化系统运行指标预测方法。基于电力系统业务应用不同行为模式下运行指标时序数据特征,提出基于傅里叶变换和自相关系数的运行指标分类方法。根据分类结果采用自适应选择策略构建运行指标时序预测模型。动态捕捉实时运行指标数据变化,自适应迭代更新模型和预测结果。选取某系统部分运行指标数据进行算例分析,验证了所提方法在精确性与时效性方面均显著优于单一算法,消除了实时数据特征变化对系统运行指标趋势预测的影响。A adaptive iterative learning method for predicting metrics of dispatching automation systems is proposed to address issues such as low accuracy of a single algorithm applied to massive metrics and failure to iteratively update based on real-time data feature changes in the context of intelligent risk warning scenarios.Based on the temporal data characteristics of metrics under different behavioral patterns of business applications in power system,a classification method for metrics based on Fourier transform and autocorrelation coefficient is proposed.Based on the classification results,an adaptive selection strategy is adopted to construct a timeseries prediction model for the metric.Real-time metric changes are dynamically captured and adaptively iterated to update model and prediction results.This paper selects some metric data of a system for example analysis to verify that the proposed method is significantly better than a single algo-rithm in terms of accuracy and timeliness,eliminating the impact of real-time data feature changes on metric prediction.

关 键 词：调度自动化系统 自适应选择 迭代学习 自适应更新 运行指标时序预测 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]