摘要
随着广域量测技术的发展,提前辨识暂态稳定性并采取预防控制措施对电力系统的安全和稳定有着重要意义,而对电力系统的时空轨迹预测则是其中的关键。传统的无系统模型电网时空轨迹预测方法虽然不依赖于系统模型,计算速度较快,但是在预测过程中并没有考虑到电网的空间拓扑关系,另外,在现代复杂电网的大数据环境下,其预测精度相比于采用深度学习的方法仍有待提高。因此,提出了基于双层栈式长短期记忆和近邻节点拓扑关系的电网时空轨迹预测模型。它采用栈式长短期记忆的神经网络结构,并将所预测发电机节点的近邻一阶节点和二阶节点特征引入到了模型中。实验数据表明,支持向量回归法、循环神经网络方法、单层长短期记忆神经网络方法、基于双层栈式长短期记忆的电网时空轨迹预测方法在测试集上的预测均方根误差逐渐递减,预测精度逐渐增加,而在一阶节点和二阶节点分别引入电网时空轨迹预测的情况下,随着引入邻近节点的增加,预测精度也逐渐增加。相比于传统的电网时空轨迹预测方法,基于双层栈式长短期记忆和近邻节点拓扑关系的电网时空轨迹预测模型能更好地表征暂态场景下电网时空轨迹的变化,更精确地实现电网时空轨迹的预测。
With the development of wide area measurement technology,it is important to identify transient stability in advance and take preventive control measures for safety and stability of power systems,and the spatio-temporal trajectory prediction of power systems is key.Although the traditional method of space-time trajectory prediction of power grid without system model does not depend on system model and its calculation speed is faster,the spatial topology of power grid cannot be considered in the process of prediction.In addition,in the big data environment of modern complex power grid,prediction accuracy with system model still needs to be improved compared with the method of using deep learning.Therefore,this paper proposed a space-time trajectory prediction model based on two-layer staclong short term memory and neighborhovd relationswhips.It adopts stacked long short term memory neural network,and introduces the characteristics of the first and second order nodes of the generator to be predicted into the model.The experimental results show that the root mean square error of the prediction decreases gradually for the prediction accuracy increases gradually with the support vector regression method,the recurrent neural network method,the single-layer long shortterm memory neural network method and space-time trajectory prediction method based on the double layers stacked long short term memory on the test set.When first-order node and second-order node are respectively introduced into space-time trajectory prediction of power grid,the prediction accuracy increases as the introduction of adjacent nodes increases.Compared with the traditional method of power grid spatio-temporal trajectory prediction,the model based on double layer stacked long short term memory and the topological relationship of neighboring nodes can better characterize the change of power grid space-time trajectory under transient scenarios,and achieve the prediction of power grid spatio-temporal trajectory more accurately.
作者
杨佳宁
黄向生
李宗翰
荣灿
刘道伟
YANG Jia-ning;HUANG Xiang-sheng;LI Zong-han;RONG Can;LIU Dao-wei(Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China;China Electric Power Research Institute,Beijing 100190,China)
出处
《计算机科学》
CSCD
北大核心
2019年第S11期23-27,32,共6页
Computer Science
基金
国家自然基金面上项目(61573356)资助
关键词
电力系统
栈式长短期记忆神经网络
暂态稳定
时空轨迹预测
空间拓扑信息.
Powersystem
Stacked long short term memory neural network
Transient stability
Spatio-temporal trajectory prediction
Spatial topological information