Neural network predictive control for smoothing of solar power fluctuations with battery energy storage

2021 ◽  
Vol 42 ◽  
pp. 103014
Author(s):  
Miswar A. Syed ◽  
Muhammad Khalid
Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2285 ◽  
Author(s):  
Yantao Liao ◽  
Jun You ◽  
Jun Yang ◽  
Zuo Wang ◽  
Long Jin

Although the traditional model predictive control (MPC) can theoretically provide AC current and circulating current control for modular multilevel converters (MMCs) in battery energy storage grid-connected systems, it suffers from stability problems due to the power quality of the power grid and model parameter mismatches. A two discrete-time disturbance observers (DOBs)-based MPC strategy is investigated in this paper to solve this problem. The first DOB is used to improve the AC current quality and the second enhances the stability of the circulating current control. The distortion and fluctuation of grid voltage and inductance parameter variation are considered as lump disturbances in the discrete modeling of a MMC. Based on the proposed method, the output prediction is compensated by disturbance estimation to correct the AC current and circulating current errors, which eventually achieve the expected tracking performance. Moreover, the DOBs have a quite low computational cost with minimum order and optimal performance properties. Since the designed DOBs work in parallel with the MPC, the control effect is improved greatly under harmonics, 3-phase unbalance, voltage sag, inductance parameter mismatches and power reversal conditions. Simulation results confirm the validity of the proposed scheme.


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