Artificial neural network based automatic generation control scheme for deregulated electricity market

2010 ◽  
Author(s):  
Sandeep Bhongade ◽  
H.O. Gupta ◽  
Barjeev Tyagi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Electricity Market ◽  
Automatic Generation ◽  
Automatic Generation Control ◽  
Deregulated Electricity Market ◽  
Control Scheme ◽  
Artificial Neural ◽  
Generation Control

scholarly journals Improved model predictive load frequency control of interconnected power system with synchronized automatic generation control loops

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Abdullahi Bala Kunya ◽  
Mehmet Argin ◽  
Yusuf Jibril ◽  
Yusuf Abubakar Shaaban
Keyword(s):  
Frequency Control ◽  
Cross Coupling ◽  
Absolute Error ◽  
Automatic Generation ◽  
Load Frequency Control ◽  
Automatic Generation Control ◽  
Control Vector ◽  
Interconnected Power System ◽  
Control Scheme ◽  
Generation Control

Abstract Background Automatic generation control (AGC) of multi-area interconnected power system (IPS) is often designed with negligible cross-coupling between the load frequency control (LFC) and automatic voltage regulation (AVR) loops. This is because the AVR loop is considerably faster than that of LFC. However, with the introduction of slow optimal control action on the AVR, positive damping effect can be achieved on the LFC loop thereby improving the frequency control. In this paper, LFC synchronized with AVR in three-area IPS is proposed. Model predictive controller (MPC) configured in a dense distributed pattern, due to its online set-point tacking is used as the supplementary controller. The dynamics of the IPS subjected to multi-area step and random load disturbances are studied. The efficacy of the developed scheme is ascertained by simulating the disturbed system in MATLAB/Simulink. Results Based on the comparative analysis on the system responses, it is established that by cross-coupling the LFC loop with AVR, reductions of 66.45% and 59.09% in the frequency and tie-line power maximum deviations respectively are observed, while the respective settling times are found to be reduced by 29.68% and 22.77% when compared with the uncoordinated control scheme. In addition, the standard deviation and variance of the integral time absolute error of the system’s responses have reduced by 23.21% and 20.83% respectively compared to those obtained in a similar study. Conclusions The reduction in the maximum deviations and settling times in the system states indicates that introducing the voltage control via AVR loop has improved the frequency control significantly. While the lower standard deviation and variance of the integral time absolute error signify improvement in the robustness of the developed algorithm. However, this improvement is at the detriment of the controller size and computational complexity. In the uncoordinated control scheme, the control vector is one-dimensional, while in the coordinated scheme, the control vector is two-dimensional for each CA.

Automatic Generation of 3D Facial Image Using Artificial Neural Network

2014 ◽  
Vol 97 (3) ◽  
pp. 52-59
Author(s):  
Takuma Yamamoto ◽  
Koosuke Hattori ◽  
Ryo Taguchi ◽  
Masahiro Hoguro ◽  
Taizo Umezaki
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Automatic Generation ◽  
Facial Image ◽  
Artificial Neural
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