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| Content Provider | IET Digital Library |
|---|---|
| Author | Jafari, Mohammad Ghasemkhani, Amir Sarfi, Vahid Livani, Hanif Yang, Lei Xu, Hao |
| Abstract | In this study, the authors investigate the secondary control of microgrids (MGs) in the presence of cyber imperfections such as delay and/or noise, and system disturbances. The existence of cyber imperfections and disturbance could bring in system uncertainty that will seriously degrade the effectiveness of most existing secondary control such as proportional–integral–derivative (PID), etc. To tackle these issues, a biologically-inspired reinforcement learning technique has been proposed which adjusts its parameters to the perturbed system setpoints generated by the cyber imperfections and system disturbances. The learning capability and low computational complexity of the proposed controller make it a promising approach to take cyber imperfections and system disturbances into account, where traditional control methodologies are not suitable due to their vulnerability to the cyber imperfections. First, an emotional learning-based secondary control structure is proposed, where the impacts of cyber imperfection and disturbance have been captured efficiently. Then, the real-time update laws are developed for generating the proper emotional signals (ESs) to stabilize the frequency and voltage. Ultimately, using the generated ESs, the secondary control of MGs is achieved. The Lyapunov analysis has been provided to prove the stability of the proposed design. Moreover, MATLAB/Simulink-based simulations demonstrate the effectiveness of the proposed algorithm. |
| Starting Page | 341 |
| Ending Page | 352 |
| Page Count | 12 |
| Volume Number | 4 |
| e-ISSN | 23983396 |
| Issue Number | Issue 4, Dec (2019) |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-cps/4/4 |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-cps.2018.5003 |
| Journal | IET Cyber-Physical Systems: Theory & Applications |
| Publisher | The Institution of Engineering and Technology |
| Publisher Date | 2019-06-24 |
| Access Restriction | Open |
| Rights License | Creative Commons Attribution-Non Commercial-No Derivs License (http://creativecommons.org/licenses/by-nc-nd/3.0/) |
| Subject Keyword | Adaptive Control Biologically Inspired Adaptive Intelligent Secondary Control Biologically Inspired Reinforcement Learning Technique Control of Electric Power System Control System Analysis Control System Synthesis Cyber Imperfection Distributed Power Generation Emotional Learning-based Secondary Control Structure Emotional Signal Knowledge Engineering Technique Learning in AI Low Computational Complexity Lyapunov Analysis Lyapunov Method MATLAB/Simulink-based Simulations MGs Neurocontrol Neurocontrollers Perturbed System Set Point Generation Power Generation Control Power System Control Proportional–integral–derivative Self Adjusting Control System Stability Stability in Control Theory Synthesis Method System Disturbances Three-term Control Uncertain System Uncertainty System |
| Content Type | Text |
| Resource Type | Article |
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