Abstract
This paper presents an innovative controller inspired by human emotional intelligence and learning mechanisms called Brain Emotional Learning-Based Intelligent Controller (BELBIC), introduced and applied to enhance the operation of a wind energy conversion system based on Doubly Fed Induction Generator (DFIG). The BELBIC control approach combines emotional states and reinforcement learning concepts to optimize control actions effectively. This paper focuses on BELBIC control of the rotor side converter and grid side converter of the DFIG system. The controller's performance is systematically evaluated under various scenarios, such as constant wind speed, variable wind speed, step change in reactive power, and voltage sag, and the performance of the above scheme is compared with that of the classical PI controller. The simulation results of the DFIG system, employing a BELBIC-based control strategy, show excellent dynamic performance in regulating the output powers of the doubly fed induction generator in grid-connected mode, thereby validating the effectiveness of the proposed control approach.
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Abbreviations
- BELBIC:
-
Brain emotional learning-based intelligent
- DFIG:
-
Doubly fed induction generator
- Rew :
-
Reward function
- B1, B2 :
-
Sensory signal gain values
- \({\text{B}}3,\mathrm{ B}4,\mathrm{ B}5\) :
-
Reward signal gain values
- \(Ai\)i:
-
Amygdala output
- \(O\)i:
-
Orbitofrontal cortex output
- \(S\)i:
-
Sensory input
- \(V\)i:
-
Amygdala learning rate
- \(W\)i:
-
Orbitofrontal cortex learning rate
- \({y}_{p}\) :
-
Plant output
- e:
-
Error
- \({V}_{ds}\) :
-
D-axis stator voltage
- \({V}_{qs}\) :
-
Q-axis stator voltage
- \({V}_{dr}\) :
-
D-axis rotor voltage controller
- \({V}_{qr}\) :
-
Q-axis rotor voltage
- \({i}_{ds}\) :
-
D-axis stator current
- \({i}_{qs}\) :
-
Q-axis stator current
- \({i}_{dr}\) :
-
D-axis rotor current
- \({i}_{qr}\) :
-
Q-axis rotor current
- \({\varphi }_{ds}\) :
-
D-axis stator flux linkages
- \({\varphi }_{qs}\) :
-
Q-axis stator flux linkages
- \({\varphi }_{dr}\) :
-
D-axis rotor flux linkages
- \({\varphi }_{qr}\) :
-
Q-axis rotor flux linkages
- \({L}_{s}\) :
-
Stator inductance
- \({L}_{r}\) :
-
Rotor inductance
- \({L}_{m}\) :
-
Magnetizing inductance
- \({\omega }_{s}\) :
-
Slip speed (rad/s)
- \({\omega }_{r}\) :
-
Rotor electrical speed (rad/s)
- \({P}_{s}\) :
-
Stator active power
- \({Q}_{s}\) :
-
Stator reactive power
- \({R}_{s}\) :
-
Stator resistance
- \({R}_{r}\) :
-
Rotor resistance
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [VMR] and [KHPS]. The first draft of the manuscript was written by [VMR] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Velpula, M.R., Kona, H.P.S. An emotional control approach to grid-connected DFIG based wind turbine. Int. J. Dynam. Control 12, 3048–3063 (2024). https://doi.org/10.1007/s40435-023-01379-z
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DOI: https://doi.org/10.1007/s40435-023-01379-z