Neural Network Based Predictive Automatic Generation Control

被引：0

作者：

Chen, Dingguo ^{[1
]}

Wang, Lu ^{[2
]}

机构：

[1] Siemens Energy Management Div, R&D Dept, Minneapolis, MN 55449 USA

[2] Accenture, Houston, TX USA

来源：

2016 IEEE POWER AND ENERGY SOCIETY GENERAL MEETING (PESGM) | 2016年

关键词：

Automatic Generation Control (AGC); Control Performance Standards (CPS); Neural Controller; Predictive Control; Renewable Energy Resources;

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

The NERC's Control Performance Standards (CPS) represent a great flexibility in relaxing the control of generating resources and yet assuring the stability and reliability of interconnected power systems. The design enhancement of Automatic Generation Control (AGC) plays a vital role in meeting these challenges. This paper for the first time provides a mathematical formulation for AGC in the context of meeting the NERC control performance standards and integrating renewable generating assets. In addition, this paper proposes a neural network based predictive control approach for AGC. The proposed controller is capable of handling complicated nonlinear dynamics in comparison with the conventional Proportional Integral (PI) controller. Furthermore, a coordinated control policy is proposed: the neural controller is responsible to control the system generation in the relaxed manner to achieve the desired control performance.

引用

页数：5

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