Maiden Application of Bacterial Foraging-Based Optimization Technique in Multiarea Automatic Generation Control

2009 ◽  
Vol 24 (2) ◽  
pp. 602-609 ◽  
Author(s):  
J. Nanda ◽  
S. Mishra ◽  
L.C. Saikia
2018 ◽  
Vol 7 (3) ◽  
pp. 1446
Author(s):  
Ahmed Jasim Sultan ◽  
Falah Noori Saeed

In This research PIDF (Proportional Integral Derivative with Filter) is suggested to control the ACE (area control error) signal of automatic generation control circuit (AGC) for two-area multi units system under deregulated conditions, each area consist of two thermal reheat units with physical GRC (generating rate constrain). The parameters of the PIDF controller are tuned using PSO (particle swarm optimization) technique. To improve the system performance, Redox Flow Batteries (RFB) is presented in one area and one of FACTS components IPFC (Inter Line Power Flow Controller) is installed in tie line. The performance of the proposed controller is assessed under different working conditions of deregulated power market. Finally, a comparison will be made on the system response when testing with varying the load conditions and system parameter through MATLAB environment 2015Rb.  


Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5867
Author(s):  
Amil Daraz ◽  
Suheel Abdullah Malik ◽  
Athar Waseem ◽  
Ahmad Taher Azar ◽  
Ihsan Ul Haq ◽  
...  

Automatic Generation Control (AGC) delivers a high quality electrical energy to energy consumers using efficient and intelligent control systems ensuring nominal operating frequency and organized tie-line power deviation. Subsequently, for the AGC analysis of a two-area interconnected hydro-gas-thermal-wind generating unit, a novel Fractional Order Integral-Tilt Derivative (FOI-TD) controller is proposed that is fine-tuned by a powerful meta-heuristic optimization technique referred as Improved-Fitness Dependent Optimizer (I-FDO) algorithm. For more realistic analysis, various constraints, such as Boiler Dynamics (BD), Time Delay (TD), Generation Rate Constraint (GRC), and Governor Dead Zone (GDZ) having non-linear features are incorporated in the specified system model. Moreover, a comparative analysis of I-FDO algorithm is performed with state-of-the-art approaches, such as FDO, teaching learning based optimization, and particle swarm optimization algorithms. Further, the proposed I-FDO tuned controller is compared with Fractional Order Tilt Integral Derivative (FOTID), PID, and Integral-Tilt Derivative (I-TD) controllers. The performance analysis demonstrates that proposed FOI-TD controller provides better performance and show strong robustness by changing system parameters and load condition in the range of  ± 50%, compared to other controllers.


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