摘要
准确预测架空输电线路动态载流量是保障线路安全增容的关键。针对传统预测模型因依赖人工经验选择模型超参数,难以有效降低线路动态载流量波动性而导致的预测精度不佳问题,本研究创新性提出一种基于SSA-VMD-LSTM的预测方法。该方法深度融合了SSA的全局优化能力、VMD的多尺度数据分解特性以及LSTM的时序建模优势,构建了一个层次化的人工智能预测模型。首先,利用SSA的强大搜索能力对VMD超参数进行迭代寻优,获取最优超参数;随后,采用VMD对线路动态载流量进行多尺度分解,得到一系列中心频率不同但局部平稳的分量;在此基础上,对多个分量分别建立LSTM进行预测;最后,将分量预测结果叠加得到最终预测结果。实验结果表明,与多个传统预测模型相比,所提方法的预测精度至少提升4.78%,充分验证了该方法在动态载流量预测中的有效性和优越性。
Accurately predicting the Dynamic Line Rating of overhead transmission lines is crucial for ensuring safe line capacity expansion.Traditional prediction models,which rely on manual experience for selecting hyperparameters,often struggle to effectively reduce the volatility of DLR,leading to suboptimal prediction accuracy.To address this issue,this study innovatively proposes an SSA-VMD-LSTM-based prediction method.This approach deeply integrates the global optimization capability of the Sparrow Search Algorithm,the multi-scale data decomposition characteristics of Variational Mode Decomposition,and the temporal modeling advantages of Long Short-Term Memory networks,constructing a hierarchical artificial intelligence prediction model.First,the powerful search ability of SSA is employed to iteratively optimize the hyperparameters of VMD,obtaining the optimal hyperparameters.Subsequently,VMD is used to decompose the DLR data into multiple scales,yielding a series of components with different central frequencies but local stationarity.On this basis,separate LSTM models are established to predict each component.Finally,the prediction results of all components are aggregated to produce the final prediction.Experimental results demonstrate that,compared to several traditional prediction models,the proposed method achieves at least a 4.78%improvement in prediction accuracy,fully validating its effectiveness and superiority in DLR prediction.
作者
王帅
申杰文
徐彬
朱振东
Wang Shuai;Shen Jiewen;Xu Bin;Zhu Zhendong(College of Electrical Engineering and New Energy,China Three Gorges University,Yichang 443002,China)
出处
《电子测量技术》
北大核心
2025年第19期115-125,共11页
Electronic Measurement Technology
基金
中国南方电网有限责任公司科技项目(030166KK52222001)资助。
关键词
架空输电线路
动态载流量
SSA
VMD
超参数寻优
overhead transmission lines
dynamic line rating
SSA
VMD
hyperparameter optimization
