摘要
提出了一种新颖的基于平均报酬模型的全过程R(λ)学习互联电力系统CPS最优控制方法。该方法与电网自动发电控制(AGC)追求较高的考核时间段内的10min平均控制性能标准(CPS)指标合格率的目标相吻合,且所提出的基于平均报酬模型的R(λ)学习算法与基于折扣报酬模型的Q(λ)学习算法相比,在线学习收敛速度更快,可获得更佳的CPS指标。此外,所提出的改进的R(λ)控制器具有全过程在线学习的特点,其预学习过程被一种新型的在线"模仿学习"所代替,克服了以往强化学习控制需要另外搭建仿真模型来进行预学习收敛的严重缺陷,提高了R(λ)控制器的学习效率及其在实际电力系统中的应用性。
The R（λ）-learning algorithm is based on the average reward model.A novel optimal CPS control methodology for interconnected power systems based on the whole process R（λ）-learning algorithm is presented.The objective of the presented CPS control methodology coincides with that of AGC which pursues the high CPS compliance in every ten minutes.Moreover, the R（λ）-learning algorithm can converge faster and gain higher value of the CPS index than the Q（λ）-learning algorithm which is based on a discounted reward model.In addition,the improved controller based on the novel R（λ）-learning algorithm holds the advantage of learning on-line in the whole process and the pre-learning process of the controller is substituted by the imitation-learning process.The improved controller overcomes the serious defect of the conventional reinforcement learning controller which needs to build an accurate simulating model for converging in the pre-learning process,and it can enhance the learning efficiency and applicability in power systems.
出处
《电力系统自动化》
EI
CSCD
北大核心
2010年第21期27-33,共7页
Automation of Electric Power Systems
基金
国家自然科学基金资助项目(50807016)
广东省自然科学基金资助项目(9151064101000049)
中央高校基本科研业务费专项资金资助项目(2009ZM0251)~~
