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).

This study paper presents a comprehensive review of virtual inertia (VI)-based inverters in modern power systems. The transition from the synchronous generator (SG)-based conventional power generation to converter-based renewable energy sources (RES) deteriorates the frequency stability of the power system due to the intermittency of wind and photovoltaic (PV) generation. Unlike conventional power generation, the lack of rotational inertia becomes the main challenge to interface RES with the electrical grid via power electronic converters. In the past several years, researchers have addressed this issue by emulating the behavior of SG mathematically via pulse width modulation (PWM) controller linked to conventional inverter systems. These systems are technically known as VI-based inverters, which consist of virtual synchronous machine (VSM), virtual synchronous generator (VSG), and synchronverter. This paper provides an extensive insight into the latest development, application, challenges, and prospect of VI application, which is crucial for the transition to low-carbon power system.

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In recent years, the domination of power electronics-interfaced renewable energy source (RES) such as solar photovoltaic (PV) system causes grid frequency instability issue. This paper proposes a new machine learning (ML)-based virtual inertia (VI) synthetization in synchronverter topology to integrate the solar PV system and the power grid with high-frequency stability. The proposed ML-based VI is synthetized by amalgamating the action and critic network to decouple active and reactive power control. Therefore, the proposed synchronverter exhibits decoupled control and flexible moment of inertia (J) changes that lead to high stability and fast transient response as compared to the conventional proportional-integral (PI) and fuzzy logic (FL)-based synchronverters. Various case studies in MATLAB/ Simulink simulation have been carried out, and the results proved the feasibility and effectiveness of the proposed ML-based synchronverter. Through the proposed control strategy, the maximum frequency deviation from the nominal value, settling time to reach quasi-steady-state frequency and steady-state error has been reduced by 0.1Hz, 35% and 27% respectively. INDEX TERMS Synchronverter, virtual inertia, power quality, frequency stability, grid-connected solar photovoltaic system.