TY - GEN
T1 - Application of antlion optimizer technique in restructured automatic generation control of two-area hydro-thermal system considering governor dead band
AU - Raju, More
AU - Saikia, Lalit Chandra
AU - Sinha, Nidul
AU - Saha, Debdeep
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017
Y1 - 2017
N2 - In this article, automatic generation control of two area interconnected system in restructured scenario is addressed. In each area two generation companies (GENCOs), one thermal and other hydro are considered. Dead band nonlinearity is incorporated for governors of the thermal GENCOs. Classical controllers, integral (I), proportional-I (PI) and PI-derivative (PID) are utilized as supplementary controllers. These controller gains are optimized with nature inspired antlion optimizer (ALO) technique. Analysis demonstrates the improved performance of PID controller over I and PI controllers in terms of minimum settling time and reduced peak overshoots in various contract conditions. Sensitivity analysis explores that ALO optimized PID controller parameters found at nominal system conditions are tough enough against variations in system loading and inertia constant parameter. Further, Studies proves the superiority of ALO technique over genetic algorithm and particle swarm techniques in providing the better dynamics and lesser values of cost function in different contract conditions.
AB - In this article, automatic generation control of two area interconnected system in restructured scenario is addressed. In each area two generation companies (GENCOs), one thermal and other hydro are considered. Dead band nonlinearity is incorporated for governors of the thermal GENCOs. Classical controllers, integral (I), proportional-I (PI) and PI-derivative (PID) are utilized as supplementary controllers. These controller gains are optimized with nature inspired antlion optimizer (ALO) technique. Analysis demonstrates the improved performance of PID controller over I and PI controllers in terms of minimum settling time and reduced peak overshoots in various contract conditions. Sensitivity analysis explores that ALO optimized PID controller parameters found at nominal system conditions are tough enough against variations in system loading and inertia constant parameter. Further, Studies proves the superiority of ALO technique over genetic algorithm and particle swarm techniques in providing the better dynamics and lesser values of cost function in different contract conditions.
UR - https://www.scopus.com/pages/publications/85045897030
UR - https://www.scopus.com/pages/publications/85045897030#tab=citedBy
U2 - 10.1109/IPACT.2017.8245099
DO - 10.1109/IPACT.2017.8245099
M3 - Conference contribution
AN - SCOPUS:85045897030
T3 - 2017 Innovations in Power and Advanced Computing Technologies, i-PACT 2017
SP - 1
EP - 6
BT - 2017 Innovations in Power and Advanced Computing Technologies, i-PACT 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 Innovations in Power and Advanced Computing Technologies, i-PACT 2017
Y2 - 21 April 2017 through 22 April 2017
ER -