首页|Deterministic and Robust Volt-var Control Methods of Power System Based on Convex Deep Learning
Deterministic and Robust Volt-var Control Methods of Power System Based on Convex Deep Learning
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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 opti-mum 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 opti-mization failure.This paper first proposes a deterministic WC 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 extreme-ly 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 WC,this paper pro-poses robust WC method based on convex deep learning inter-val power flow(DLIPF),which continues to adopt ICNN to es-tablish another convex mapping between state parameters and node voltage interval.Combining DLIPF with DLPF,this meth-od decreases the modeling and optimization difficulty of robust WC significantly.Test results on 30-bus,118-bus,and 200-bus systems prove the correctness and rapidity of the proposed methods.
Volt-var controlconvexity conversionconvex deep learningpower flow
Qing Ma、Changhong Deng
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School of Electrical Engineering and Automation,Wuhan University,Wuhan,China
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