Artificial Intelligence Based MPPT Techniques for Solar Power System: A review

Abstract

In the last decade, artificial intelligence (AI) techniques have been extensively used for maximum power point tracking (MPPT) in the solar power system. This is because conventional MPPT techniques are incapable of tracking the global maximum power point (GMPP) under partial shading condition (PSC). The output curve of the power versus voltage for a solar panel has only one GMPP and multiple local maximum power points (MPPs). The integration of AI in MPPT is crucial to guarantee the tracking of GMPP while increasing the overall efficiency and performance of MPPT. The selection of AI-based MPPT techniques is complicated because each technique has its own merits and demerits. In general, all of the AI-based MPPT techniques exhibit fast convergence speed, less steady-state oscillation and high efficiency, compared with the conventional MPPT techniques. However, the AI-based MPPT techniques are computationally intensive and costly to realize. Overall, the hybrid MPPT is favorable in terms of the balance between performance and complexity, and it combines the advantages of conventional and AI-based MPPT techniques. In this paper, a detailed comparison of classification and performance between 6 major AI-based MPPT techniques have been made based on the review and MATLAB/Simulink simulation results. The merits, open issues and technical implementations of AI-based MPPT techniques are evaluated. We intend to provide new insights into the choice of optimal AI-based MPPT techniques. Index Terms-Maximum power point tracking (MPPT), artificial intelligence (AI), fuzzy logic control (FLC), artificial neural network (ANN), genetic algorithm (GA), swarm intelligence (SI), machine learning (ML).

Design of a Partial Resonant Inverter for solar photovoltaic applications

“…The authors in [27] surveyed various conventional and recently developed MPPT methods. The authors in [28] integrated the artificial intelligence approach in MPPT, which eventually increased the efficiency of the PV system. In the proposed system the input power is drawn from a solar PV panel, therefore a suitable MPPT technique is essential to harvest maximum power from the PV panel.…”

Design of a Hybrid Energy System with Energy Storage for Standalone DC Microgrid Application

“…The method works well to eliminate the effects of SEPIC voltage input (from the DERs) variation and step-load changes. Further, artificial neural network (ANN) and fuzzy logic controllers (they can be summed up as artificial intelligence-AI) have been employed to satisfy load requirements from a microgrid [14,15]. However, the large number of connected DERs, which sometimes impose those AI controllers to fulfil conflicting requirements, is fraught with limited communication.…”

Deep Learning-Aided Sensorless Control Approach for PV Converters in DC Nanogrids

“…Similarly, [5] introduces ANN-based method to reduce the number of implemented sensors and control power converters with lower attenuation at DC bus. Furthermore, ANNs are broadly adopted in fault diagnosis [22], failure and lifetime prediction of power devices [14], long-term performance analysis for power electronic converters [23], detecting and mitigation of cyber-attacks for DC microgrids [24], optimal energy scheduling for microgrids [25] and maximum power point tracking (MPPT) techniques for PV systems [26].…”