Search

Conventional global maximum power point tracking (GMPPT) algorithms show limited tracking speed and accuracy due to the unnecessary peak search considering the complexity of partial shading conditions (PSCs). This paper proposes a novel power incremental (PI) algorithm for photovoltaic GMPPT and partial shading detection with the aid of the reference voltage line (RVL), representing the voltage of possible peaks based on the derived analytical expressions. The proposed analytical solution can detect the occurrence of PSCs easily and never overlook GMPP. Furthermore, both the tracking speed and the efficiency can be improved due to the quick allocation of GMPP and the reduced search area. The proposed RVL-PI-GMPPT algorithm is straightforward without sophisticated searching iterations, which can reduce the computational burden. Combined with the low-cost feature without additional temperature or irradiance sensors, the proposed algorithm is very suitable for the practical engineering design. Both the simulations and experiments under various PSC patterns validated the effectiveness of the proposed algorithm. Conducted fair comparisons with other popular GMPPT methods, the average percentage change of tracking time by implementing the proposed RVL-PI-GMPPT algorithm under three-peaks PSC patterns is $\bf 108.9\%$ compared with the PSO method and $\bf 209.9\%$ compared with the 0.8 $V_{oc}$ model method, respectively. [ABSTRACT FROM AUTHOR]

In this article, a low complexity finite-control-set model predictive control (FCS-MPC) based on the discrete space vector modulation (DSVM) is proposed for T-type three-phase three-level (3P-3L) converters. Different from the conventional FCS-MPC, 48 virtual voltage vectors (VVs) of the converter are constructed by real VVs based on the DSVM. Thus, the performance of 3P-3L converters is significantly improved and the peak amplitude of high-order harmonics concentrates at the sampling frequency. Furthermore, two-stage FCS-MPC based on virtual VVs is proposed to reduce the computation burden. Its first stage selects one of six virtual VVs that minimizes the current tracking error. Then, these candidate VVs located in the same sector as the optimal virtual VV selected in the first stage are evaluated in the second-stage optimization. Thus, the computational efficiency has been greatly improved. To verify the validity of the proposed control method and show its superiority over the conventional FCS-MPC, experimental results are presented. [ABSTRACT FROM AUTHOR]

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]

Differential power processing (DPP) is regarded as a promising architecture in solving mismatching issues among photovoltaic (PV) submodules. Although conventional total-minimum-power-point (TMPP)-based real-time optimization algorithm by using the distributed submodule-level maximum power point tracking and simultaneously the centralized total-minimum-power tracking shows effectiveness in maximizing the power yield. However, uneven power stress among DPP converters, large oscillations, high additional cost for communication among DPP converters, and complicated implementation hinder the practical application. This article proposed a low-complexity power balancing point-based optimization algorithm to reduce the system cost and size, improve the system efficiency, and realize the standardized modular design for DPP converters. Furthermore, simple submodule-level voltage equalization control is implemented to eliminate expensive communication and relieve the control complexity while guaranteeing high maximum-power-point efficiency. The proposed algorithm can reduce the power rating of DPP converters compared with conventional TMPP-based control, which is beneficial to the improvement of system cost, reliability, and lifetime. Both simulation and experimental results under various scenarios are provided to validate the advantages of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Passing cloud results in rapid changes of irradiance. The intermittency of photovoltaic (PV) power output has drawn serious concern especially for utility-scale PV system. Consequently, power ramp-rate control (PRRC) has been introduced to avoid significant PV power fluctuations. PRRC is usually implemented either by curtailing active power output or implementing energy storage system (ESS). However, current active power curtailment cannot deal with the irradiance ramp-down fluctuations, and the high cost of the ESS is still hindering its extensive application. In this paper, two innovative PRRC strategies are presented, which utilize the short-term forecasting. The first solution does not require any ESS, during the power ramp-down event, the PV generation will be curtailed before the actual shading occurs. The second solution requires only one-quarter of the energy capacity of the conventional ESS control strategy. To provide the PV generation forecasts, a dynamic model based on spatio-temporal theory is formulated. The effectiveness of the proposed forecasting model and control strategies have been verified through experiment and case studies. [ABSTRACT FROM AUTHOR]

Model predictive control (MPC) suffers from high computational burden and cumbersome tuning of weighting factors especially for three-level three-phase inverters. Here, a fast finite-switching-state MPC (FSS-MPC) algorithm without weighting factors is proposed. First, the deadbeat control is used to construct the voltage vector reference. Then, the T-type three-level three-phase inverter topology is selected and voltage vectors are carefully tuned in order to minimize the neutral-point (NP) voltage fluctuation. The voltage vectors that are far from the desired voltage vector reference and go against balancing the NP voltage will not participate in the cost function optimization. Thus, only 3 effective voltage vectors instead of totally 27 vectors are required in the implementation of the proposed FSS-MPC algorithm, which saves computation time up to 53.3%. Furthermore, the proposed algorithm makes the tuning of the weighting factor unnecessary, which simplifies the practical implementation and improves the portability of the algorithm. Finally, an experimental prototype was established and main results including the steady-state and dynamic performance were presented to validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]