Development of A Hybrid Method to Control the Grid-Connected PV Converter

Roozitalab, Farzad; Jarrahi, Mohammad Amin; Arefi, Mohammad Mehdi; Javadi, Mohammad Sadegh; Anvari-Moghadam, Amjad; Catalào, João P. S.

doi:10.1109/eeeic/icpseurope49358.2020.9160667

Cited by 2 publications

(1 citation statement)

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“…The presented method in the previous literature can be divided into traditional and advanced control systems. The traditional control system could be mentioned for the linear control systems such as proportional-integral (PI) [3][4][5][6] and quadratic control [7], a non-linear control system such as hysteresis voltage control [8], multi-loop controller [9], repetitive controller [10], deadbeat-based control system [11] and sliding mode-based controller [12]. There are several major drawbacks that restricted traditional control systems including (i) causing resonance due to the variable frequency switching, (ii) slow dynamic response due to modulators, (iii) sensitivity to the modulator, (iv) poor performance in presence of interruptions, and (v) being dependent on the physical model.…”

Section: Introductionmentioning

confidence: 99%

Deep learning based model predictive control of active filter inverter as interface for photovoltaic generation

Rasoulian,

Saghafi,

Abbasian

et al. 2023

IET Renewable Power Gen

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By increasing the photovoltaic (PV) systems capacity worldwide, the requirement for a fast, reliable, and efficient control system is becoming more crucial. To this end, model predictive control (MPC) is known as one of the potential solutions. Although MPC is an easily implemented control system, it needs a high computational complexity due to the dependency on solving an iterative optimization problem. To overcome this problem, this study develops an artificial intelligence‐based on one‐dimensional convolutional neural network (1D‐CNN) based MPCs. While 1D‐CNN benefits from the inherent strong feature extraction/selection capability and lower computational complexity than other deep methods, it still cannot properly track the dynamic changes due to fixed weights during the training process. Thus, this paper integrates the dynamic weighting training process and proposed dynamic weighing 1D‐CNN for the implementation of intelligent MPC for the PVs. The numerical results based on different load types show an efficient performance of the proposed system and verify the superiority of the proposed method in comparison with the conventional MPC and several state‐of‐the‐arts shallow and deep based MPC for the PVs in terms of the total harmonic distortion (THD) and frequency switching.

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