自适应迭代学习的调度自动化系统运行指标预测方法

doi:10.3778/j.issn.1002-8331.2309-0462

摘要/Abstract

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。

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

Abstract: 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 algorithm in terms of accuracy and timeliness, eliminating the impact of real-time data feature changes on metric prediction.

Key words: dispatching automation system, adaptive selection, iterative learning, adaptive updates, timeseries prediction of metrics

沈嘉灵, 季学纯, 高尚, 王宇冬, 陈子韵, 李昊. 自适应迭代学习的调度自动化系统运行指标预测方法[J]. 计算机工程与应用, 2025, 61(4): 368-376.

SHEN Jialing, JI Xuechun, GAO Shang, WANG Yudong, CHEN Ziyun, LI Hao. Adaptive Iterative Learning for Predicting Metrics of Dispatching Automation System[J]. Computer Engineering and Applications, 2025, 61(4): 368-376.

参考文献

[1] 闪鑫, 陆晓, 翟明玉, 等. 人工智能应用于电网调控的关键技术分析[J]. 电力系统自动化, 2019, 43(1): 49-57.
SHAN X, LU X, ZHAI M Y, et al. Analysis of key technologies for artificial intelligence applied to power grid dispatch and control[J]. Automation of Electric Power Systems, 2019, 43(1): 49-57.
[2] 姚建国, 杨胜春, 单茂华. 面向未来互联电网的调度技术支持系统架构思考[J]. 电力系统自动化, 2013, 37(21): 52-59.
YAO J G, YANG S C, SHAN M H. Reflections on operation supporting system architecture for future interconnected power grid[J]. Automation of Electric Power Systems, 2013, 37(21): 52-59.
[3] 孙名扬, 于芳, 赵家庆, 等. 新一代调控系统一体化运维架构及关键技术[J]. 电力系统自动化, 2019, 43(22): 217-223.
SUN M Y, YU F, ZHAO J Q, et al. Integrated operation/maintenance architecture and key technologies of new generation dispatching and control system[J]. Automation of Electric Power Systems, 2019, 43(22): 217-223.
[4] ANDRéS A R, OTERO A, AMAVILAH V H. Using deep learning neural networks to predict the knowledge economy index for developing and emerging economies[J]. Expert Systems with Applications, 2021, 184: 115514.
[5] YAO W, HUANG P, JIA Z. Multidimensional LSTM networks to predict wind speed[C]//Proceedings of the 2018 37th Chinese Control Conference. Piscataway: IEEE, 2018: 7493-7497.
[6] 邓德军, 徐洪珍, 韦诗玥. E-V-ALSTM模型的股价预测[J]. 计算机工程与应用, 2023, 59(6): 101-112.
DENG D J, XU H Z, WEI S Y. Stock price prediction based on E-V-ALSTM model[J]. Computer Engineering and Applications, 2023, 59(6): 101-112.
[7] 高榕, 万以亮, 邵雄凯, 等. 面向改进的时空Transformer的交通流量预测模型[J]. 计算机工程与应用, 2023, 59(7): 250-260.
GAO R, WAN Y L, SHAO X K, et al. Traffic flow forecasting model for improved spatio-temporal transformer[J]. Computer Engineering and Applications, 2023, 59(7): 250-260.
[8] 张兴锐, 刘畅, 陈哲, 等. 基于时空图卷积网络的机场地铁短时客流预测[J]. 计算机工程与应用, 2023, 59(8): 322-330.
ZHANG X R, LIU C, CHEN Z, et al. Short-term passenger flow prediction of airport subway based on spatio-temporal graph convolutional network[J]. Computer Engineering and Applications, 2023, 59(8): 322-330.
[9] 王逸文, 王维莉. 基于LSTM-RELM组合模型的电商GMV预测研究[J]. 计算机工程与应用, 2023, 59(10): 321-327.
WANG Y W, WANG W L. Research on GMV prediction of E-commerce based on LSTM-RELM combination model[J]. Computer Engineering and Applications, 2023, 59(10): 321-327.
[10] 陈纬楠, 胡志坚, 岳菁鹏, 等. 基于长短期记忆网络和LightGBM组合模型的短期负荷预测[J]. 电力系统自动化, 2021, 45(4): 91-97.
CHEN W N, HU Z J, YUE J P, et al. Short-term load prediction based on combined model of long short-term memory network and light gradient boosting machine[J]. Automation of Electric Power Systems, 2021, 45(4): 91-97.
[11] SUN G, JIANG C, WANG X, et al. Short-term building load forecast based on a data-mining feature selection and LSTM-RNN method[J]. IEEJ Transactions on Electrical and Electronic Engineering, 2020, 15(7): 1002-1010.
[12] WANG X, DUAN Z, LIU L, et al. Multi-timescale load forecast of large power customers based on online data recovery and time series neural networks[J]. Journal of Circuits, Systems and Computers, 2022, 31(5): 2250088.
[13] 刘裕舸. 基于LSTM时间序列模型的电力变压器温度预测方法研究[J]. 红水河, 2022, 41(3): 75-80.
LIU Y G. Research on temperature prediction method of power transformer based on LSTM time series model[J]. Hongshui River, 2022, 41(3): 75-80.
[14] 陆继翔, 张琪培, 杨志宏, 等. 基于CNN-LSTM混合神经网络模型的短期负荷预测方法[J]. 电力系统自动化, 2019, 43(8): 131-137.
LU J X, ZHANG Q P, YANG Z H, et al. Short-term load forecasting method based on CNN-LSTM hybrid neural network model[J]. Automation of Electric Power Systems, 2019, 43(8): 131-137.
[15] 王侃, 王孟洋, 刘鑫, 等. 融合自注意力机制与CNN-BiGRU的事件检测[J]. 西安电子科技大学学报, 2022, 49(5): 181-188.
WANG K, WANG M Y, LIU X, et al. Event detection by combining self-attention and CNN-BiGRU[J]. Journal of Xidian University, 2022, 49(5): 181-188.
[16] 蔡铭, 李响. 基于自适应选择的多策略粒子群算法[J]. 计算机仿真, 2021, 38(3): 239-244.
CAI M, LI X. Multi-strategy particle swarm optimization based on adaptive selection[J]. Computer Simulation, 2021, 38(3): 239-244.
[17] 李彤, 尹志宏, 张文斌. 快速傅里叶变换在交直流漏电识别中的应用研究[J]. 软件导刊, 2021, 20(10): 202-206.
LI T, YIN Z H, ZHANG W B. Research and application of fast Fourier transform in AC/DC leakage identification[J]. Software Guide, 2021, 20(10): 202-206.