The most significant challenge the maximum power point tracking (MPPT) algorithms face is reaching the global MPPT under partial shading conditions (PSC). In this case, the power-voltage (P-V) and current-voltage (I-V) characteristic curves of the photovoltaic (PV) system produce many maximum power points, which is the reason that the algorithms often trapped in local maximum power point (LMPP). The Perturbation and Observation (P&O) algorithm is simple and effective for finding MPP on a mono-extreme graph. Under PSC with many extremes, it is necessary to limit the range of potential GMPP so that this algorithm can avoid the LMPP trap. This paper introduces a global MPPT (GMPPT) method based on the combination of modified P&O and simulates the behavior of the I-V curve under PSC. In particular, the starting parameters of the algorithm are adjusted in the potential GMPP region to exploit simplicity and efficiency to increase performance and convergence speed. The proposed method determines the area where the GMPP is potentially found more accurately, and this contribution is crucial for increasing the efficiency of the strings of PV arrays under partial shading conditions. By comparing results with other algorithms, such as Modified P&O, Adaptive Jaya (Ajaya), and Jaya, the proposed algorithm has verified its outstanding advantages in MPPT performance and search time under the same test conditions.

This paper presents a technique to quickly determine the potential maximum power point (MPP) of a photovoltaic system using the duty cycle ( D ) of the DC/DC converter. The proposed method depends on estimating the open‐circuit voltage ( V _oc ) and short‐circuit current ( I _sc ) values under different operating conditions. It is an adapted version derived from the conventional Perturb and Observe (P&O) algorithm, designed to restrict the search area for the MPP. The working conditions, including temperature variations, irradiance, or both, are simulated in the PSIM environment and experimented with a simulator with parameters satisfied according to the proposed operating conditions. The research objective of this study also involves operating under partially shaded conditions. When comparing the results of this research with those obtained from the traditional P&O method and the Variable Step Size Perturb and Observe (VSSP&O) approach, it becomes evident that the proposed solution excels in terms of both convergence speed and performance, particularly when operating under partial shade conditions. In addition, this study's methods of determining I _sc , V _oc , and D _mp can be widely applied in low‐voltage and power systems.

Under uniform operating conditions, the power–voltage (P-V) and current–voltage (I-V) curves of a photovoltaic (PV) system have only one maximum point, which facilitates the operation of maximum power point tracking (MPPT) algorithms. In practice, the PV systems often operate under heterogeneous environments due to partial shading conditions (PSCs). The P-V and I-V curves exhibit multiple extremes, and distinguishing between the global maximum power point (GMPP) and local maximum power point (LMPP) is a major challenge for algorithms aiming to improve performance and convergence speed. This paper presents a global maximum power point tracking (GMPPT) method based on simulating the behavior of the I-V curve of a PV system under the influence of PSCs. With only one initial parameter selected, the proposed solution quickly determines the LMPPs based on the characteristics of the PV type and the shading condition during operation. This work helps to limit the potential GMPP region to reduce the search time and improve efficiency by using a simple algorithm and a small tuning step size. The experimental results demonstrate that the proposed method provides superior MPPT performance and significantly reduces search time due to improved GMPP detection accuracy combined with small step sizes.

The maximum power point tracking (MPPT) solutions improve power generation efficiency, quickly stabilizing the output waveform of photovoltaic (PV) systems under variable operating conditions. Along with new algorithms, improved and adjusted methods to exploit energy from PV systems are increasingly being researched and proposed. However, the proposed solutions based on the traditional algorithms and their improvements have poor performance, while the advanced algorithms or hybrid methods bring high performance but need to be simplified, and the response speed is higher. Moreover, a suitable PV configuration makes choosing a simple but highly efficient algorithm, especially in low-power PV system applications such as rooftop solar power, traffic lights, and moving vehicles…where the number of PV panels is insufficient to implement flexible configurations. This paper proposes a modified version of the Perturb and Observe (MPO) algorithm to improve MPPT performance and increase convergence speed in the parallel structure of PV panels. The Short-Circuit Current (I_sc) and Open-Circuit Voltage (V_oc) are calculated directly at specific operating conditions to quickly determine the potential maximum power point (MPP) that will reduce power interruptions and increase power generation efficiency compared to periodic updates. Therefore, the proposed solution converges faster, with higher efficiency, and the output signal in static and dynamic MPPT situations is more stable. The results show that the highest efficiency in simulation and experiment is 99.99% and 99.93%, respectively, while the convergence speed is 0.01 s and 0.03 s, respectively. They are better than the traditional Perturb and Observe (P&O) algorithm, the Variable Step Size Perturb and Observe (VSSP&O) method, and the Particle Swarm Optimization (PSO) technique under the same operating conditions. In addition, its performance and convergence speed are also compared with the latest introduced algorithms. The results show that it is valuable and reliable for parallel PV configuration.

Partial shading is the cause of the reduction of the output power of the photovoltaic (PV) system due to changes in its P-V characteristic curve. Global Maximum Power Point Tracking (GMPPT) in more complex and multiple peaks conditions is the biggest challenge for current MPPT techniques to improve the performance of the system. This article introduces an improved method based on the traditional Particle Swarm optimization (I-PSO) algorithm to increase the convergence speed in a constantly changing and complex environment. The study not only considers the influence of the best location of the individual and the swarm but also focuses on the experience of the neighboring individuals with a better position to avoid the local extreme trap. In addition to that, a boost converter uses to simulate the proposed algorithm applying PSIM software. The simulating results with those previously under the same operating conditions showed the superiority of the proposed approach in improving the efficiency of the photovoltaic system.

Using the solid-state technique that involves great temperature, we created multiple phosphors Sr4La(PO4)3O:Ce3+, Tb3+, Mn2+ (abbreviated as SLPO:Ce, Tb, Mn). We then examined their heat consistency, luminescence, as well as energy shift from Ce3+ to Tb3+ and Mn2+. We can acquire a considerable boost for the faint emission in green generated by Tb3+ as well as the emission in red generated by Mn2+ via adding the sensitizer Ce3+ ions. Through modifying the proportion between Ce3+ and Tb3+ along with the proportion between Ce3+ and Mn2+, it is possible to adjust the chroma of emission. We acquired white illumination which had color coordinates determined as (0.3326, 0.3298) for the testing phosphor Sr4La(PO4)3O: 0.12Ce3+, 0.3Mn2+. Such result displays the promising efficiency of phosphors Sr4La(PO4)3O:Ce3+, Tb3+, Mn2+ for the WLED devices.