TY - JOUR
T1 - Model-based fault-tolerant control to guarantee the performance of a hybrid wind-diesel power system in a microgrid configuration
AU - Vargas-Martínez, Adriana
AU - Avila, Luis Ismael Minchala
AU - Zhang, Youmin
AU - Garza-Castañón, Luis Eduardo
AU - Ortiz, Eduardo Robinson Calle
PY - 2013
Y1 - 2013
N2 - This paper presents a comparison of two different adaptive control schemes for improving the performance of a hybrid wind-diesel power system in an islanded microgrid configuration against the baseline controller, IEEE type 1 automatic voltage regulator (AVR). The first scheme uses a model reference adaptive controller (MRAC) with a proportional-integral-derivative (PID) controller tuned by a genetic algorithm (GA) to control the speed of the diesel engine (DE) for regulating the frequency of the power system and uses a classical MRAC for controlling the voltage amplitude of the synchronous machine (SM). The second scheme uses a MRAC with a PID controller tuned by a GA to control the speed of the DE, and a MRAC with an artificial neural network (ANN) and a PID controller tuned by a GA for controlling the voltage amplitude of the SM. Different operating conditions of the microgrid and fault scenarios in the diesel engine generator (DEG) were tested: 1) decrease in the performance of the diesel engine actuator (40% and 80%), 2) sudden connection of 0.5 MW load, and 3) a 3-phase fault with duration of 0.5 seconds. Dynamic models of the microgrid components are presented in detail and the proposed microgrid and its faulttolerant control (FTC) are implemented and tested in the Simpower Systems of MATLAB/Simulink® simulation environment. The simulation results showed that the use of ANNs in combination with model-based adaptive controllers improves the FTC system performance in comparison with the baseline controller.
AB - This paper presents a comparison of two different adaptive control schemes for improving the performance of a hybrid wind-diesel power system in an islanded microgrid configuration against the baseline controller, IEEE type 1 automatic voltage regulator (AVR). The first scheme uses a model reference adaptive controller (MRAC) with a proportional-integral-derivative (PID) controller tuned by a genetic algorithm (GA) to control the speed of the diesel engine (DE) for regulating the frequency of the power system and uses a classical MRAC for controlling the voltage amplitude of the synchronous machine (SM). The second scheme uses a MRAC with a PID controller tuned by a GA to control the speed of the DE, and a MRAC with an artificial neural network (ANN) and a PID controller tuned by a GA for controlling the voltage amplitude of the SM. Different operating conditions of the microgrid and fault scenarios in the diesel engine generator (DEG) were tested: 1) decrease in the performance of the diesel engine actuator (40% and 80%), 2) sudden connection of 0.5 MW load, and 3) a 3-phase fault with duration of 0.5 seconds. Dynamic models of the microgrid components are presented in detail and the proposed microgrid and its faulttolerant control (FTC) are implemented and tested in the Simpower Systems of MATLAB/Simulink® simulation environment. The simulation results showed that the use of ANNs in combination with model-based adaptive controllers improves the FTC system performance in comparison with the baseline controller.
KW - Artificial intelligence
KW - Distributed generation
KW - Fault-tolerant control
KW - Microgrids
KW - Model-based control
UR - http://www.scopus.com/inward/record.url?scp=84896893329&partnerID=8YFLogxK
U2 - 10.1016/j.procs.2013.06.094
DO - 10.1016/j.procs.2013.06.094
M3 - Artículo de la conferencia
AN - SCOPUS:84896893329
SN - 1877-0509
VL - 19
SP - 712
EP - 719
JO - Procedia Computer Science
JF - Procedia Computer Science
T2 - 4th International Conference on Ambient Systems, Networks and Technologies, ANT 2013 and the 3rd International Conference on Sustainable Energy Information Technology, SEIT 2013
Y2 - 25 June 2013 through 28 June 2013
ER -