| 摘要: |
| 在现代互联大电网背景下，研究了多区域强鲁棒性的智能发电控制策略。在Q学习的架构下，将深度神经网络的预测机制作为强化学习的动作选择机制，提出了一种具有强鲁棒性的深度Q学习算法，设计了基于该算法的智能发电控制器。针对智能电网下的智能发电控制问题，在多智能体系统的框架下采用所提深度Q学习算法进行控制，并与传统的PID、Q学习和Q(λ)算法进行对比。在IEEE标准2区域和以南方电网4区域为背景的仿真模型(采用了23 328种不同模型参数)中进行数值仿真，仿真结果验证了所提深度Q学习算法的可行性和有效性，也验证了所设计控制器的强鲁棒性。 |
| 关键词: 深度Q学习 智能发电控制 强鲁棒性 深度神经网络 多智能体系统 |
| DOI：10.16081/j.issn.1006-6047.2018.05.002 |
| 分类号:TM761+.2 |
| 基金项目:国家重点基础研究发展计划(973计划)项目(2013CB228205)；国家自然科学基金资助项目(51477055) |
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| Design of strong robust smart generation controller based on deep Q learning |
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YIN Linfei, YU Tao
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School of Electric Power, South China University of Technology, Guangzhou 510640, China
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| Abstract: |
| Under the background of modern interconnected large power grid, a smart generation control strategy with strong robustness in multi-areas is studied. In the framework of Q learning, taking the prediction mechanism of the deep neural network as the action selector of Q learning, a DQL(Deep Q Learning) algorithm with strong robustness is proposed, and on this basis, a smart generation controller is designed. The proposed DQL algorithm in the multi-agent system is applied for smart generation control in the smart interconnected power grid, and is compared with the traditional PID algorithm, Q learning algorithm and Q(λ) learning algorithm. The simulative results of IEEE standard two-area model and the four-area model based on China Southern Power Grid with 23 328 different parameters verify the feasibility and effectiveness of the proposed DQL algorithm and the strong robustness of the designed controller. |
| Key words: deep Q learning smart generation control strong robustness deep neural network multi-agent systems |
