Multi-objective optimal power flow calculation based on multi-step Q(λ) learning algorithm

被引：4

作者：

Yu T. ^{[1
]}

Hu X.-B. ^{[1
]}

Liu J. ^{[1
]}

机构：

[1] School of Electric Power, South China University of Technology, Guangzhou 510640, Guangdong

来源：

Huanan Ligong Daxue Xuebao/Journal of South China University of Technology (Natural Science) | 2010年 / 38卷 / 10期

关键词：

Electric power system; Multi-objective optimization; Optimal power flow; Q(lambda; ) learning algorithm; Reinforcement learning;

D O I：

10.3969/j.issn.1000-565X.2010.10.026

中图分类号：

学科分类号：

摘要：

As the conventional optimization algorithms of power flow cannot meet the requirements of real-time scheduling of power system with complex and nonlinear descriptional multi-objective optimal power flow (OPF), this paper presents a multi-step Q(λ) learning algorithm based on the semi-Markov decision process. This algorithm, independent of any accurate model, converts the constraints, actions and targets of the optimal power flow to the status, actions and rewards of the algorithm, and dynamically finds the optimal action by continuous fault testing, retrospecting and iteration. By comparing comparison of the proposed algorithm with other algorithms in several IEEE standard examples, it is found that the Q(λ) learning algorithm is feasible and effective in dealing with multi-objective OPF problems.

引用

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页码：139 / 145

页数：6

共 17 条

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