控制理论与应用2018，Vol.35Issue(2)：169-183,15.DOI:10.7641/CTA.2017.70239
基于深度自适应动态规划的孤岛主动配电网发电控制与优化一体化算法
Deep adaptive dynamic programming based integration algorithm for generation control and optimization of islanded active distribution network
摘要
Abstract
With the development of many distributed generations (DGs) (e.g. wind power and photovoltaic power, biomass energy,energy storage device and plug in hybrid electric vehicle(PHEV)),the traditional methods for generation control of isolated island power grids cannot meet the requirements of frequency stability.This paper proposes deep adap-tive dynamic programming(DADP)algorithm to solve this problem.Replacing neural network(NN)in adaptive dynamic programming(ADP)algorithm by the deep neural network(DNN)in the field of machine learning(ML),and adding deep model forecast neural network in,the proposed DADP algorithm is designed. Generation commands,which are achieved by the algorithms of both generation control and generation command dispatch in the traditional way, are obtained by the proposed DADP algorithm. Finally,to verify the robustness of the proposed DADP algorithm,many simulations for isolated island micro-gird are simulated,for instance,the normal isolated island situation,plug and play situation,commu-nication failure situation,all-day disturbance situation,time-varying topology situation and systemic internal parameters varying situation,and the duration of all these simulations is configured as long as 25 years. The effectiveness,feasibility and strong robustness of the proposed DADP algorithm are verified by the simulation results.关键词
主动配电网/发电控制/优化控制/深度学习/自适应动态规划Key words
active distribution network/generation control/optimization control/deep learning/adaptive dynamic programming(ADP)分类
信息技术与安全科学引用本文复制引用
殷林飞,余涛,张泽宇,张孝顺..基于深度自适应动态规划的孤岛主动配电网发电控制与优化一体化算法[J].控制理论与应用,2018,35(2):169-183,15.基金项目
国家自然科学基金项目(51177051,51477055),国家重点基础研究"973"项目(2013CB228205)资助.Supported by the National Natural Science Foundation of China(51177051,51477055)and the National Key Basic Research Program of China(“973”Program)(2013CB228205). （51177051,51477055）
