Ant colony optimization algorithm and fuzzy logic for switched reluctance generator control

Rabyi Tarik; Brouri Adil; Rabyi Tarik; Brouri Adil

doi:10.3934/energy.2022045

AIMS Energy

2022, Volume 10, Issue 5: 987-1004. doi: 10.3934/energy.2022045

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Research article

Ant colony optimization algorithm and fuzzy logic for switched reluctance generator control

Rabyi Tarik ^,,
Brouri Adil

L2MC laboratory, SECNDCM teams, ENSAM, Moulay Ismail University, 50000 Meknes, Morocco

Received: 10 February 2022 Revised: 25 June 2022 Accepted: 25 July 2022 Published: 02 September 2022

This article discusses two methods to control the output voltage of switched reluctance generators (SRGs) used in wind generator systems. To reduce the ripple of the SRG output voltage, a closed-loop voltage control technique has been designed. In the first method, a proportional-integral (PI) controller is used. The parameters of the PI controller are tuned based on the voltage variation. The SRG is generally characterized by strong nonlinearities. However, finding appropriate values for the PI controller is not an easy task. To overcome this problem and simplify the process of tuning the PI controller parameters, a solution based on the ant colony optimization algorithm (ACO) was developed. To settle the PI parameters, several cost functions are used in the implementation of the ACO algorithm. To control the SRG output voltage, a second method was developed based on the fuzzy logic controller. Unlike several previous works, the proposed methods, ACO and fuzzy logic control, are easy to implement and can solve numerous optimization problems. To check the best approach, a comparison between the two methods was performed. Finally, to show the effectiveness of this study, we present examples of simulations that entail the use of a three-phase SRG with a 12/8 structure and SIMULINK tools.
- switched reluctance generator,
- PI controller,
- wind generator systems,
- ant colony optimization,
- fuzzy logic control
Citation: Rabyi Tarik, Brouri Adil. Ant colony optimization algorithm and fuzzy logic for switched reluctance generator control[J]. AIMS Energy, 2022, 10(5): 987-1004. doi: 10.3934/energy.2022045

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Abstract

This article discusses two methods to control the output voltage of switched reluctance generators (SRGs) used in wind generator systems. To reduce the ripple of the SRG output voltage, a closed-loop voltage control technique has been designed. In the first method, a proportional-integral (PI) controller is used. The parameters of the PI controller are tuned based on the voltage variation. The SRG is generally characterized by strong nonlinearities. However, finding appropriate values for the PI controller is not an easy task. To overcome this problem and simplify the process of tuning the PI controller parameters, a solution based on the ant colony optimization algorithm (ACO) was developed. To settle the PI parameters, several cost functions are used in the implementation of the ACO algorithm. To control the SRG output voltage, a second method was developed based on the fuzzy logic controller. Unlike several previous works, the proposed methods, ACO and fuzzy logic control, are easy to implement and can solve numerous optimization problems. To check the best approach, a comparison between the two methods was performed. Finally, to show the effectiveness of this study, we present examples of simulations that entail the use of a three-phase SRG with a 12/8 structure and SIMULINK tools.

References

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