Load frequency control of power system based on cloud neural network adaptive inverse system

被引：0
|
作者：
Wu Z. [1 ]
Zhang W. [1 ]
Li F. [1 ]
Du C. [1 ]
机构：
[1] Key Laboratory of Industrial Computer Control Engineering of Hebei Province, College of Electrical Engineering, Yanshan University, Qinhuangdao
来源：
Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2017年 / 37卷 / 11期
关键词：
Adaptive inverse control; Cloud model; Interconnected power system; Load frequency control; Neural network;
D O I：
10.16081/j.issn.1006-6047.2017.11.014
中图分类号：
学科分类号：
摘要：
The system frequency will fluctuate sharply after the area interconnected power system suffered from wind power and load disturbance, for which, a load frequency control method for multi-area interconnected power system is proposed based on the cloud neural network adaptive inverse control system. The active power output characteristics of a single area power system is analyzed, based on which, the load frequency control model of interconnected power system considering multi-area active power output is built. The contradiction between system response and disturbance restrain is effectively solved by the adaptive inverse control. The cloud model is introduced into the adaptive inverse control system to construct the cloud neural network identifier. The identification ability of neural network is further improved by the advantages of cloud model in dealing with uncertainties such as fuzziness and randomness. Simulative results show that the proposed cloud neural network adaptive inverse control system can not only obtain good dynamic response, but also minimize the disturbance caused by wind power and load. © 2017, Electric Power Automation Equipment Press. All right reserved.
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页码：86 / 91and98
页数：9112
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