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This study presents an adaptive perturb and observe (P&O)-fuzzy control maximum power point tracking (MPPT) for photovoltaic (PV) boost dc–dc converter. P&O is known as a very simple MPPT algorithm and used widely. Fuzzy logic is also simple to be developed and provides fast response. The proposed technique combines both of their advantages. It should improve MPPT performance especially with existing of noise. For evaluation and comparison analysis, conventional P&O and fuzzy logic control algorithms have been developed too. All the algorithms were simulated in MATLAB-Simulink, respectively, together with PV module of Kyocera KD210GH-2PU connected to PV boost dc–dc converter. For hardware implementation, the proposed adaptive P&O-fuzzy control MPPT was programmed in TMS320F28335 digital signal processing board. The other two conventional MPPT methods were also programmed for comparison purpose. Performance assessment covers overshoot, time response, maximum power ratio, oscillation and stability as described further in this study. From the results and analysis, the adaptive P&O-fuzzy control MPPT shows the best performance with fast time response, less overshoot and more stable operation. It has high maximum power ratio as compared to the other two conventional MPPT algorithms especially with existing of noise in the system at low irradiance.

We study how to achieve the ultimate power in the simplest, yet non-trivial, model of a thermal machine, namely a two-level quantum system coupled to two thermal baths. Without making any prior assumption on the protocol, via optimal control we show that, regardless of the microscopic details and of the operating mode of the thermal machine, the maximum power is universally achieved by a fast Otto-cycle like structure in which the controls are rapidly switched between two extremal values. A closed formula for the maximum power is derived, and finite-speed effects are discussed. We also analyze the associated efficiency at maximum power showing that, contrary to universal results derived in the slow-driving regime, it can approach Carnot's efficiency, no other universal bounds being allowed.

This study introduces a novel approach for analyzing photovoltaic (PV) systems that employ block lookup tables for speedy and efficient simulation. It introduces an innovative method for tracking the Global Maximum Power Point (GMPP) by utilizing Zebra Optimization Algorithm (ZOA). The suggested method was carefully evaluated under difficult Partial Shading Conditions (PSCs) and Dynamic Shading Conditions (DSCs) to determine its global and local search capability. ZOA’s performance was examined in four scenarios and compared to four existing MPPT algorithms: Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), Flower Pollination Algorithm (FPA), and Whale Optimization Algorithm (WOA). ZOA surpassed its competitors with an average tracking time of 0.875 s and a tracking efficiency of 99.95% in PSCs. In comparison, ZOA increased tracking efficiency by up to 2%, increased resilience under varied circumstances, and produced a faster convergence speed—approaching the maximum Power Point 10–15% faster than the other algorithms. Furthermore, ZOA significantly decreased operating point variations. The algorithm’s overall performance was tested using an experimental setup with a DSPACE board and a PV emulator. These findings demonstrate that ZOA is a highly efficient and dependable MPPT solution for PV systems, especially in severe PSCs.

Currently, the critical challenge in solar photovoltaic (PV) systems is to make them energy efficient. One of the key factors that can reduce the PV system power output is partial shading conditions (PSCs). The reduction in power output not only depends on a shaded region but also depends on the pattern of shading and physical position of shaded modules in the array. Due to PSCs, mismatch losses are induced between the shaded modules which can cause several peaks in the output power-voltage (P-V) characteristics. The series-parallel (SP), total-cross-tied (TCT), bridge-link (BL), honey-comb (HC), and triple-tied (TT) configurations are considered as conventional configurations, which are severely affected by PSCs and generate more mismatch power losses along with a greater number of local peaks. To reduce the effect of PSCs, hybrid PV array configurations, such as series-parallel: total-cross-tied (SP-TCT), bridge-link: total-cross-tied (BL-TCT), honey-comb: total-cross-tied (HC-TCT) and bridge-link: honey-comb (BL-HC) are proposed. This paper briefly discusses the modeling, simulation and performance evaluation of hybrid and conventional 7 × 7 PV array configurations during different PSCs in a Matlab/Simulink environment. The performance of hybrid and conventional PV configurations are evaluated and compared in terms of global maximum power (GMP), voltage and currents at GMP, open and short circuit voltage and currents, mismatch power loss (MPL), fill factor, efficiency, and a number of local maximum power peaks (LMPPs).

The use of photovoltaic systems for clean electrical energy has increased. However, due to their low efficiency, researchers have looked for ways to increase their effectiveness and improve their efficiency. The Maximum Power Point Tracking (MPPT) inverters allow us to maximize the extraction of as much energy as possible from PV panels, and they require algorithms to extract the Maximum Power Point (MPP). Several intelligent algorithms show acceptable performance; however, few consider using Artificial Neural Networks (ANN). These have the advantage of giving a fast and accurate tracking of the MPP. The controller effectiveness depends on the algorithm used in the hidden layer and how well the neural network has been trained. Articles over the last six years were studied. A review of different papers, reports, and other documents using ANN for MPPT control is presented. The algorithms are based on ANN or in a hybrid combination with FL or a metaheuristic algorithm. ANN MPPT algorithms deliver an average performance of 98% in uniform conditions, exhibit a faster convergence speed, and have fewer oscillations around the MPP, according to this research.

Partial shading conditions (PSC) in photovoltaic (PV) systems degrade energy harvest by generating multi-peak power-voltage (P–V) curves, trapping conventional maximum power point tracking (MPPT) algorithms at local maxima. This paper presents a Multi-Peak to Single-Peak Conversion (MSMPPT) framework that enables conventional algorithms like Perturb & Observe (P&O) and Incremental Conductance (INC) to reliably track the global maximum power point (GMPP) under PSC without structural modifications. The framework operates via two stages: dynamic estimation of optimal voltage boundaries to shrink the GMPP search space to under 10% of the original P–V range, and active voltage regulation to enforce operation within this zone, effectively transforming the multi-peak curve into a single-peak profile. The proposed MSMPP-P&O and MSMPP-INC algorithms achieve 50% faster tracking (64 ms vs. 122 ms for P&O) and near-perfect steady-state efficiency under static shading, reducing power losses below 2%. In dynamic shading scenarios with abrupt irradiance shifts, MSMPPT maintains robustness with less than 1.5 W net loss, outperforming conventional methods that incur over 30 W of power losses. By eliminating oscillations and hotspot risks through voltage regulation, the framework retains algorithmic simplicity while enhancing performance under complex shading scenarios. Validated across benchmark shading profiles, MSMPPT demonstrates fidelity without requiring additional hardware or complex optimizers. This innovation bridges the gap between conventional MPPT simplicity and partial shading resilience, offering a cost-effective, scalable solution to boost PV system reliability in shading environments.

In the recent past, the solar photovoltaic (PV) system has emerged as the most promising source of alternative energy. This solar PV system suffers from an unavoidable phenomenon due to the fluctuating environmental conditions. It has nonlinearity in I-V curves, which reduces the output efficiency. Hence, the optimum maximum power point (MPP) extraction of the PV system is difficult to achieve. Therefore, for maximizing the power output of PV systems, a maximum power point tracking (MPPT) mechanism, which is a control algorithm that can constantly track the MPP during operation, is required. However, choosing a suitable MPPT technique might be confusing because each method has its own set of advantages and disadvantages. Hence, a proper review of these methods is essential. In this paper, a state-of-the-art review on various MPPT techniques based on their classifications, such as offline, online, and hybrid techniques under uniform and nonuniform irradiances, is presented. In comparison to offline and online MPPT methods, intelligent MPPT techniques have better tracking accuracy and tracking efficiency with less steady state oscillations. Unlike online and offline techniques, intelligent methods track the global MPP under partial shade conditions. This review paper will be a useful resource for researchers, as well as practicing engineers, to pave the way for additional research and development in the MPPT field.

In order to improve maximum power point tracking (MPPT) performance, a variable and adaptive perturb and observe (P&O) method with current predictive control is proposed. This is applied in three-phase three-level neutral-point clamped (NPC) photovoltaic (PV) generation systems. To control the active power and the reactive power independently, the decoupled power control combined with a space vector modulation block is adopted for three-phase NPC inverters in PV generation systems. To balance the neutral-point voltage of the three-phase NPC grid-connected inverter, a proportional and integral control by adjusting the dwell time of small voltage vectors is used. A three-phase NPC inverter rated at 12 kVA was established. The performance of the proposed method was tested and compared with the fixed perturbation MPPT algorithm under different conditions. Experimental results confirm the feasibility and advantages of the proposed method.

In this paper, a hybrid optimization controller that combines a genetic algorithm (GA) and ant colony optimization (ACO) called GA-ACO algorithm is proposed. It is applied to a photovoltaic module array (PVMA) to carry out maximum power point tracking (MPPT). This way, under the condition that the PVMA is partially shaded and that multiple peaks are produced in the power-voltage (P-V) characteristic curve, the system can still operate at the global maximum power point (GMPP). This solves the problem seen in general traditional MPPT controllers where the PVMA works at the local maximum power point (LMPP). The improved MPPT controller that combines GA and ACO uses the slope of the P-V characteristic curve at the PVMA work point to dynamically adjust the iteration parameters of ACO. The simulation results prove that the improved GA-ACO MPPT controller is able to quickly track GMPP when the output P-V characteristic curve of PVMA shows the phenomenon of multiple peaks. Comparing the time required for tracking to MPP with different MPPT approaches for the PVMA under five different shading levels, it was observed that the improved GA-ACO algorithm requires 19.5~35.9% (average 29.2%) fewer iterations to complete tracking than the mentioned GA-ACO algorithm. Compared with the ACO algorithm, it requires 74.9~79.7% (average 78.2%) fewer iterations, and 75.0~92.5% (average 81.0%) fewer than the conventional P&O method. Therefore, it is proved that by selecting properly adjusted values of the Pheromone evaporation rate and the Gaussian standard deviation of the proposed GA-ACO algorithm based on the slope scope of the P-V characteristic curves, a better response performance of MPPT is obtained.

In this paper, an improved artificial bee colony (I-ABC) algorithm for the maximum power point tracking (MPPT) of a photovoltaic module array (PVMA) is presented. Even though the P-V output characteristic curve with multi-peak was generated due to any damages or shading discovered on the PVMA, the I-ABC algorithm could get rid of stuck on tracking the local maximum power point (LMPP), but quickly and stably track the global maximum power point (GMPP), thereby improving the power generation efficiency. This proposed I-ABC algorithm could search for the higher power point of a PVMA by a small bee colony, determine the next tracking direction through the perturb and observe (P&O) method, and keep tracking until the GMPP is obtained. This method could prevent tracking the GMPP for too long due to applying a small bee colony. First, in this study, the photovoltaic modules produced by Sunworld Co., Ltd. were used and were configured as a PVMA with four series and three parallel connections under different numbers of shaded modules and different shading ratios, so that corresponding P-V output characteristic curves with multi-peak values were generated. Then, the GMPP was tracked by the proposed MPPT method. The simulation and experimental results showed that the proposed method performed better both in dynamic response and steady-state performance than the traditional artificial bee colony (ABC) algorithm. According to the experimental results, it showed that the tracking accuracy for the GMPP based on the proposed MPPT with 100 iterations under 5 different shading ratios was about 100%; on the other hand, that of the traditional ABC algorithm was 70%, and that of the P&O method was lower at about 30%.