Open AccessBiologically inspired adaptive intelligent secondary control for MGs under cyber imperfections
Author(s) -
Jafari Mohammad,
Ghasemkhani Amir,
Sarfi Vahid,
Livani Hanif,
Yang Lei,
Xu Hao
Publication year - 2019
Publication title -
iet cyber‐physical systems: theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.308
H-Index - 7
ISSN - 2398-3396
DOI - 10.1049/iet-cps.2018.5003
Subject(s) - control theory (sociology) , pid controller , reinforcement learning , vulnerability (computing) , controller (irrigation) , control engineering , matlab , computer science , stability (learning theory) , control (management) , engineering , artificial intelligence , machine learning , computer security , temperature control , agronomy , biology , operating system
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.