摘要
由于复杂大电网关键输电断面的功率调整工作量大、重复性高,计算速度难以满足在线辅助决策需求。基于深度学习理论,提出了一种对断面功率调整数据进行特征自学习进而实现大电网断面功率快速自动调整的方法。首先,采用反向等量配对法模拟人工调整操作,构建深度学习所需的海量数据集。然后,在机组灵敏度、调整量等约束条件下筛选出复杂大电网中参与功率调整的有效机组集合。在此基础上,以决定系数为指标构建最优回归模型准确预测出机组出力调整值,从而实现了断面功率的快速自动调整。最后,以中国某实际区域大电网的省间断面功率调整为例对所提方法进行验证。仿真结果表明,最优模型的决定系数和断面功率调整成功率均较为理想,大大缩短了断面功率调整时间,且调整效率不受系统运行方式和断面实际功率与目标功率差值的影响。
Due to the heavy workload and high repeatability of power adjustment for key transmission cross-sections of complex bulk power grids, the computational speed is difficult to meet the requirements of online assistant decision-making. Based on the deep learning theory, a method is proposed to perform feature self-learning on the cross-section power adjustment data to realize the fast automatic adjustment of the cross-section power of bulk power grids. First, the reverse equivalent matching method is used to simulate the manual adjustment operation, and the massive data set required by deep learning is constructed. Then, under the constraints of unit sensitivity and adjustment amount, the effective set of units participating in the power adjustment in the complex bulk power grid is selected. On this basis, an optimal regression model is built with the determination coefficient as the index to accurately predict the output value of the adjusting unit, thereby realizing the fast automatic adjustment of the cross-section power.Finally, the proposed method is validated by taking the inter-provincial cross-section power adjustment in a practical regional bulk power grid in China an example. Simulation results show that the determination coefficient of the optimal model and the success rate of the cross-section power adjustment are both relatively ideal, which greatly shortens the cross-section power adjustment time, and the adjustment efficiency is not affected by the system operation mode and the difference between the actual cross-section power and the target power.
作者
龚承霄
李岩松
刘君
黄彦浩
陈兴雷
文晶
GONG Chengxiao;LI Yansong;LIU Jun;HUANG Yanhao;CHEN Xinglei;WEN Jing(School of Electrical and Electronic Engineering,North China Electric Power University,Beijing 102206,China;China Electric Power Research Institute Co.,Ltd.,Beijing 100192,China)
出处
《电力系统自动化》
EI
CSCD
北大核心
2023年第2期181-190,共10页
Automation of Electric Power Systems
基金
国家自然科学基金资助项目(U1866602)。
关键词
大电网
断面功率
反向等量配对法
自动调整
深度学习
bulk power grid
cross-section power
reverse equivalent matching method
automatic adjustment
deep learning
