Neural network based predictive Automatic Generation Control

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...

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Bibliographic Details
Main Authors: Dingguo Chen, Lu Wang
Format: Conference Proceeding
Language:English
Subjects:
Automatic generation control
Automatic Generation Control (AGC)
Control Performance Standards (CPS)
Frequency control
Generators
Neural Controller
Power system stability
Predictive Control
Renewable Energy Resources
Time-frequency analysis
Transient analysis
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Summary: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.
ISSN:1944-9933
DOI:10.1109/PESGM.2016.7741573