Emulating emotional quotient – an augmented BELBIC based strategy for harmonic recession in photovoltaic microgrids

Abstract

Abstract For the harmonic suppression in photovoltaic microgrids, two interdependent control loops: primary current control loop and secondary voltage control loop is necessary. In this paper, an intelligent secondary controller depicting brain emotional learning based intelligent controller (BELBIC) is suggested. The primitive nature of mammalian brain to learn using emotional quotient is the underlying theory of BELBIC. The original BELBIC is augmented with state awareness knowledge and an upgraded a-BELBIC is proposed in this paper. The proposed controller acquires intelligence through emotional learning and gives promising results in state detection and harmonics deduction. It is also intended to resolve harmonic surge caused by redundant control actions, without compromising on the dynamic response of system. Additionally, it also remains unaffected by insignificant variations during the steady state and reduces the overwork of the primary current controller due to weather deviations. It is completely model-free and is proved to handle computational complexity, system ambiguity and non-linearity. It is compared with PI and NN controller to prove its excellence in harmonic recession and combating subharmonics Moreover, the primary controller administering current control in the inner loop is carried out by the finite control set based model predictive control (MPC). Comprehensively, the validation of robust primary controller and proposed intelligent secondary controller for diversified test cases is assessed using simulations performed in MATLAB/Simulink mathematical engine and tested for hardware-in-loop efficacy using dSPACE DS1104 RTI. The proposed a-BELBIC is observed to reduce the settling time by 18.75%, overshoot by 17.05% and the execution time by 75% in comparison to NN Controller.