Deterministic and Robust Volt-var Control Methods of Power System Based on Convex Deep Learning  

作　　者：Qing Ma Changhong Deng 

机构地区：[1]the School of Electrical Engineering and Automation,Wuhan University,Wuhan,China

出　　处：《Journal of Modern Power Systems and Clean Energy》2024年第3期719-729,共11页现代电力系统与清洁能源学报（英文）

摘　　要：Volt-var control(VVC)is essentially a non-convex optimization problem due to the non-convexity of power flow(PF)constraints,resulting in the difficulty in obtaining the optimum without convexity conversion.The existing second-order cone method for the convexity conversion often leads to a sharp increase in PF constraints and optimization variables,which in turn increases the optimization difficulty or even leads to optimization failure.This paper first proposes a deterministic VVC method based on convex deep learning power flow(DLPF).This method uses the input convex neural network(ICNN)to establish a single convex mapping between state parameters and node voltage to complete the convexity conversion while the optimization variables only correspond to reactive power equipment,which can ensure the global optimum with extremely fast computation speed.To cope with the impact brought by the uncertainty of distributed energy and omit the additional worst scenario search of traditional robust VVC,this paper proposes robust VVC method based on convex deep learning interval power flow(DLIPF),which continues to adopt ICNN to establish another convex mapping between state parameters and node voltage interval.Combining DLIPF with DLPF,this method decreases the modeling and optimization difficulty of robust VVC significantly.Test results on 30-bus,118-bus,and 200-bus systems prove the correctness and rapidity of the proposed methods.

关 键 词：Volt-var control convexity conversion convex deep learning power flow 

分 类 号：TM73[电气工程—电力系统及自动化] TP18[自动化与计算机技术—控制理论与控制工程]