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Maximum power point tracking (MPPT) is required in PV power systems for the highest solar energy harvest. This article proposes a self-tuning scheme to improve the MPPT performance in terms of high accuracy and speed. The scheme adopts the reinforcement learning (RL) and Beta parameter for the highest MPPT performance. The tracking speed and accuracy are significantly improved since the RL algorithm is enhanced for high convergence speed, meanwhile, the guiding variable $\beta$ is introduced to constrain the exploration space. Simulation and experimental test are applied to validate the superior performance of the proposed solution following the EN50530 dynamic test procedure. [ABSTRACT FROM AUTHOR]

Dynamic performance of maximum power point tracking (MPPT) algorithms is important to ensure high-power output under practical operating conditions. In this article, after reviewing three dynamic test procedures, including stepped operation procedure, day-by-day operation procedure, and EN50530 dynamic test procedure, three typical MPPT algorithms such as the fixed-step-size perturb and observe (P&O), variable-step-size incremental conductance, and hybrid-step-size beta method are evaluated experimentally under the EN50530 dynamic test procedure. Two dynamic EN50530 test sequences are adopted for the performance evaluation to cover different irradiance changing conditions. The PV model for EN50530 dynamic test sequences is built, and the effects of wrong-step changes by using three MPPT algorithms are analyzed systematically. The experimental comparison of three MPPT algorithms in terms of the tracking routines, accumulated energy, and tracking efficiency is presented. The research shows that the 0.5% fixed-step-size P&O may fail to track the MPP due to the tracking drift, whereas the beta algorithm exhibits the highest tracking efficiency under both dynamic sequences. The average tracking efficiency improvement of the beta algorithm compared with other two algorithms are experimentally measured as $\text{24.2}\%$ and $\text{18.8}\%$ , respectively. [ABSTRACT FROM AUTHOR]

Power reserve control (PRC) without energy storage becomes essential for modern photovoltaic (PV) power plants to meet the increased ancillary service requirements such as grid frequency support. Conventional PRC strategies show obvious limitations to estimate the available maximum power point ($P_{\text{avai}}$), such as additional hardware requirements, implementation difficulty, and slow estimation speed. Originated from the linear characteristic of PV curves in the constant current region and a Lambert- $W$ function for voltage linear reference, $P_{\text{avai}}$ is estimated separately in separate steps rather directly measured or estimated. The proposed strategy does not requires any additional hardware such as the irradiance and temperature sensors, which realizes sensorless control with reduced cost. Furthermore, cumbersome procedures of curve fitting with sophisticated operating points sampling and key parameters determination in the real-time $P_{\text{avai}}$ estimation by using the conventional PRC methods can be also eliminated. In this strategy, the operating point with a curtailed power level is allocated at the left-hand side of the MPP, which guarantees the stability of PV systems under varying conditions. The developed strategy exhibits fast speed to estimate $P_{\text{avai}}$ , high robustness, and good compatibility with existing PV systems. Simulation and experimental results under various scenarios are provided to validate the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]