自动化学报2018，Vol.44Issue(5)：901-914,14.DOI:10.16383/j.aas.2018.c170487
基于GAN技术的自能源混合建模与参数辨识方法
We-energy Hybrid Modeling and Parameter Identification With GAN Technology
摘要
Abstract
As a sub-unit of the energy internet, we-energy (WE) aims at realizing bi-directional power transformation and flexible conversion between various types of energies. As the operating characteristics of WE have large difference under different working conditions, existing methods can not accurately identify its parameters. In order to solve this problem,a data-mechanism hybrid driving method based on generative adversarial networks(GAN)is proposed. In order to switch the WE model under different operating conditions,fuzzy theory is used to achieve fuzzy classification of training data of the GAN model by expertise. A modified GAN model containing policy gradient feedback is applied in training model,therefore solving the issue of discrete output sequence of WE.Simulation results validate that the proposed model is of high identification accuracy and has better generalization performance, and can effectively fit the state variation of each node of the whole system under different operation modes.关键词
能源互联网/自能源/生成式对抗网络/生成式模型/对抗学习/零和博弈Key words
Energy internet/we-energy (WE)/generative adversarial networks (GAN)/generative model/adversarial learning/zero-sum game引用本文复制引用
孙秋野,胡旌伟,杨凌霄,张化光..基于GAN技术的自能源混合建模与参数辨识方法[J].自动化学报,2018,44(5):901-914,14.基金项目
国家自然科学基金重点项目(61433004),国家自然科学基金(61573094),中央高校基础科研业务费(N140402001)资助 Supported by the Key Program of National Natural Sci-ence Foundation of China(61433004),National Natural Science Foundation of China(61573094),and The Central University Based Research Fees(N140402001) （61433004）
