Maximum Power Point Tracking Research Articles

Power and efficiency enhancement of solar photovoltaic power plants through grouped string voltage balancing approach

Solar photovoltaic (PV) power plants’ performance is severely impacted by multi-level irradiances or partial shading, leading to power losses and voltage instability. Also, partial shading adds further complexity to the maximum power point tracking algorithms by introducing numerous peaks in the power curves, resulting in additional losses. Numerous solutions are presented to deal with shading losses, and dynamic reconfiguration is the most effective; however, higher switch count and complex architecture make it impractical in real-world implementation. Hence, this study proposes a low-complexity architecture based on the grouped string voltage balancing approach. This approach utilizes a voltage balancing converter connected to groups of strings to enhance the power output of the array of PV plants, maintain overall system voltage stability, and eliminate the possibility of multiple peaks formation in the power curves. The effectiveness of the proposed approach is tested under numerous static and dynamic partial shadings and analyzed using power curves, power output, losses, efficiencies, and voltage stability. The validation is done by comparing the proposed approach with conventional and advanced architectures for a 32.5 kW system. The results show that the proposed method requires a 50 % reduced switch count than existing techniques, achieves 99.54 % efficiency, and maintains an average voltage stability of 0.01.

Enhanced MPPT in Permanent Magnet Direct-Drive Wind Power Systems via Improved Sliding Mode Control

Addressing the challenges of significant speed overshoot, stability issues, and system oscillations associated with the sliding mode control (SMC) strategy in maximum power point tracking (MPPT) for permanent magnet synchronous wind power systems, this paper introduces a fuzzy sliding mode control (FSMC) method employing an innovative exponential convergence law. By incorporating a velocity adjustment function into the traditional exponential convergence law, a novel convergence law was designed to mitigate oscillations during the sliding phase and expedite the convergence rate. Additionally, a fuzzy controller was developed to implement a fuzzy adaptive SMC strategy, optimizing the MPPT for permanent magnet synchronous wind power generation systems. Simulation results indicated that this approach offered a faster response and superior interference rejection capabilities, compared to conventional and modified SMC strategies. The improved FSMC strategy demonstrated a swift, dynamic response and excellent steady-state performance, improving the efficiency of MPPT, thus confirming the effectiveness of the proposed method.

Extending Shelf-life of Two-step Method Precursor Solutions through Targeted Regulation for Highly Efficient and Reproducible Perovskite Solar Cells.

Precursor solution aging process can cause significant influence on the photovoltaic performance of perovskite solar cells (PVSCs). Notably, we first observe that the aging phenomenon is more severe in the precursor of two-step sequential method compared to that in one-step method due to that the protic solvent isopropanol facilitates amine-cation side reaction and iodide ions oxidation. Herein, we report a novel approach for selectively stabilizing both organic amine salt and lead iodide (PbI2) precursor solutions in two-step method. The introduction of benzene-1,3-dithiol into organic amine salt solution can mitigate amine-cation side reactions due to the formation of an acidic and reducing environment. Simultaneously, decamethylferrocene (FcMe10/FcMe+10) pair can act as a redox shuttle in PbI2 solution to concurrently oxidize Pb0 and reduce I2 in cyclic manner. Consequently, the PVSCs device fabricated from ameliorative precursor solutions demonstrates superior power conversion efficiency of 25.31%, retaining 95% of its efficiency after 21 days of solution aging. Moreover, the unencapsulated devices maintain 85% of primitive efficiency for 1500 h at maximum power point tracking under continuous illumination. This work establishes a fundamental guidance and scientific direction for the stabilization of two-step perovskite precursor solutions.

Design and implementation of Type-3 intuitionistic fuzzy logic MPPT controller for PV solar system: Comparative study

The performance of a photovoltaic (PV) solar system is affected by partial shading conditions (PSC) and environmental conditions, such as solar irradiance and ambient temperature, which vary throughout the day. This results in variations in the maximum power point (MPP) on the solar PV output characteristic curve. Therefore, various classical MPP tracking (MPPT) techniques have been used to track the MPP and extract maximum power from PV systems. However, these techniques have drawbacks such as lower stability, increased oscillation around the steady state, and slower convergence to the MPP. To overcome this problem, the newly proposed interval Type-3 intuitionistic fuzzy logic (T3IFL) controller has been proposed. The T3IFL MPPT controller combines the uncertainty of Type-3 fuzzy logic (T3FL) controller with intuitionistic concepts. The T3IFL controller is more accurate and offers faster convergence to the MPP under changing climatic and steady-state conditions than classical techniques and T3FL controller. The T3IFL algorithm provides better performance with excellent MPP tracking by controlling the duty cycle of the DC-DC buck converter. Four cases studied were investigated: uniform radiation conditions, a step change in solar radiation with constant temperature, replacing the battery load with the ohmic load with constant radiation and temperature, and partial shading conditions. Experimental validation of the T3IFL was performed on a DC-DC buck converter using real-time hardware-in-the-loop (HIL). Finally, the simulation and experimental results with comparative studies verified the accuracy of the proposed method in tracking the desired value and disturbance/uncertainty attenuation with better response.

Chemically‐Modified 2D Covalent Organic Framework as an HTL Dopant for High‐Performance, Stable, and Sustainable Perovskite Solar Cells and Modules

AbstractDespite significant advances in perovskite solar cells (PeSCs), the operational instability and susceptibility to Pb leakage of PeSCs severely limit their widespread application. To address these issues, this study investigates the effect of doping the Spiro‐OMeTAD hole‐transporting layer (HTL) with a chemically‐modified 2D conjugated covalent organic framework (Tp‐Azo‐COF) on the photovoltaic performance and stability of PeSCs. Enriched with abundant carbonyl (C═O) groups and azo (N═N) nodes, Tp‐Azo‐COF has excellent chelation and adsorption capabilities, and experimental results confirm that Tp‐Azo‐COF effectively decreases Pb leakage and Li‐ion migration, improving the environmental safety and operational stability of PeSCs. The optimized PeSCs (0.12 cm2) exhibit an efficiency of 24.25%, a new benchmark for COF‐modified devices, and maintain robust performance in large‐area modules (18 cm2) with an efficiency of 21.96%. Under accelerated aging tests, including continuous light irradiation at maximum power point tracking for 980 h, the module demonstrated exceptional durability, with near‐100% efficiency retention. The COF doping strategy developed in this study significantly enhances operational stability and minimizes Pb leakage in PeSCs, paving the way for the sustainable, large‐scale deployment of perovskite photovoltaics.

Solar PV system with maximum power tracking

The paper investigates the state-of-the-art architecture of photovoltaic systems (PVS) by evaluating the performance of the developed maximum power point tracking (MPPT) algorithm of fuzzy particle swarm optimization (FPSO) for temperate continental latitudes. The material of the paper gives an evaluation of traditional MPPT algorithms in relation to the state-of-the-art FPSO algorithm. The study summarizes the problems of On-Grid PV systems and analyzes methods of solving them by combining them with hydrogen technologies. The paper summaries the experience of observations of climatic factors and solar resources on the example of the Russian Federation. Thus, in 2023, the average winter temperature exceeded the norm by 4.7 °C, and the average summer temperature exceeded the norm by 4.9 °C compared to 2022. The paper analyses the level of insolation, which increased by 0.03 kWh/m2 by regions of Russia for the period 2022–2023. The experimental part of the work was carried out at the solar power plant (SPP) «Kalmykskaya» with coordinates 53.422832 north latitude and 55.266895 east longitude. The watt-volt field characteristic of photovoltaic modules (PVM) connected to the inverter was obtained experimentally. When compared, the correlation coefficient between the experimental power (P) and voltage (U) of the panels was found to be higher than that of the ideal panels, 0.933 versus 0.914. The correlation coefficient between the ideal P(U) function and the experimental one is (−0.475). The data for calculating the coefficient of performance (COP) of the implemented MPPT algorithm was also obtained experimentally, which was about 98.7%. The reliability of the data used in the calculations was confirmed by two independent means of measurement, the difference of the obtained results was less than 1%. In the last part of the experiment of this study, the dependence of insolation in a given geographical point on the generated PV field power was evaluated. The correlation coefficient was 0.47, while the inverter output voltage was maintained in the nominal range of (600 ± 20%)V. Thus, the authors of the study experimentally proved the efficiency of using MPPT on FPSO in temperate continental climate with duration of sunshine T = 1850 h per year. The effectiveness of the FPSO algorithm under the conditions of inverter distance from the common point of the DC switching cabinet (DСCC) has been confirmed. The effectiveness of the MPPT algorithm based on FPSO under conditions of partial shading, increased cloud cover and increased air temperature is concluded. Using the description of the current architecture of On-Grid SPP, the authors draw attention to the impossibility of operation of such systems without the presence of voltage in the reference network. In addition, the impossibility of the system operation at the value of PVM power over 1500 kW and at the voltage of panels less than 900 V is noted. To modernize the existing PVS architecture, for the first time, the use of a DCCC and an inverter with implemented MPPT on the FPSO in combination with a hydrogen (H2) production unit and nickel-hydrogen (Ni–H2) batteries is proposed. The researchers propose that when the PVM field voltage is below 900 V and when the PVM field power exceeds 1500 kW, energy can be diverted to H2 generation or to charge Ni–H2 batteries via a DCCC controller. Such an architecture will improve the continuity and efficiency of the PVS, reduce the carbon footprint, and allow the PVS to be used as an industrial uninterruptible power supply (UPS). The authors see the lack of standardization of implemented projects based on the ESG principle as the main problem of alternative energy development.

Magnetron sputtered nickel oxide with suppressed interfacial defect states for efficient inverted perovskite solar cells

Widely used spin-coated nickle oxide (NiOx) based perovskite solar cells often suffer from severe interfacial reactions between the NiOx and adjacent perovskite layers due to surface defect states, which inherently impair device performance in a long-term view, even with surface molecule passivation. In this study, we developed high-quality magnetron-sputtered NiOx thin films through detailed process optimization, and compared systematically sputtered and spin-coated NiOx thin film surfaces from materials to devices. These sputtered NiOx films exhibit improved crystallinity, smoother surfaces, and significantly reduced Ni3+ or Ni vacancies compared to their spin-coated counterparts. Consequently, the interface between the perovskite and sputtered NiOx film shows a substantially reduced density of defect states. Perovskite solar cells (PSCs) fabricated with our optimally sputtered NiOx films achieved a high power conversion efficiency (PCE) of up to 19.93% and demonstrated enhanced stability, maintaining 86.2% efficiency during 500 h of maximum power point tracking under one standard sun illumination. Moreover, with the surface modification using (4-(2,7-dibromo-9,9-dimethylacridin-10(9H)-yl)butyl)phosphonic acid (DMAcPA), the device PCE was further promoted to 23.07%, which is the highest value reported for sputtered NiOx based PSCs so far.

An enhanced maximum power point tracking and voltage control for proton exchange membrane fuel cell using predictive model control techniques

Proton Exchange Membrane Fuel Cells serve as sustainable devices for converting renewable energy sources into electricity, offering advantages such as rapid startup, high power density, and operation at low temperatures. They are highly efficient and environmentally friendly energy sources, yet their non-linear output characteristics under variable conditions present challenges in maximizing power output. Furthermore, they generates direct current, while many electrical devices rely on alternating current. This research addresses the necessity of enhancing the performance and efficiency through the development of an advanced maximum power point tracking technique and voltage control strategy. A modified finite-control-set model predictive control technique is proposed for both maximum power point tracking and voltage control. Specifically, a finite-control-set model predictive control technique approach is employed to modulate the switching signal for both the DC-DC boost converter and the DC-AC inverter. The DC-DC boost converter step up the fuel cell output voltage to the desired level, ensuring it reaches the maximum power point, and a DC-AC inverter to convert the direct current voltage to a pure sinusoidal alternating waveform. The investigation demonstrates the effectiveness of the proposed method in achieving its objectives. The proposed maximum power point tracking technique accuracy achieved the maximum power with a rate of 97 % with excellent respond time within 7 ms. For alternating current power, only less than 1.5 % of total harmonic distortion is recorded. The study evaluates the control scheme under robust operating conditions, demonstrating its effectiveness in optimizing PEMFC output and providing high-quality AC voltage.

Hybrid feedback control of PMSG-based WECS with multilevel flying-capacitor inverter enhanced by fractional order extremum seeking

This paper proposes an original hybrid control strategy for a three-phase multilevel flying capacitor inverter (FCI) used in wind energy conversion system (WECS). A state feedback law, computed using a Lyapunov function and an invariance principle, is designed to manage grid current control and capacitor voltage balancing, guaranteeing the asymptotic stability of the closed-loop system. Additionally, a fractional-order extremum seeking control (FOESC) algorithm for Maximum Power Point Tracking (MPPT) is introduced, which optimizes power capture by adjusting turbine speed from the DC side without requiring a detailed system model (model-free approach). The use of fractional-order calculus leads to improved convergence and more seamless performance while preserving the robust characteristics of the system. The proposed control strategies are validated through simulation, demonstrating improved transient response and stability under varying wind conditions and parameter uncertainties. These results highlight the potential of advanced control algorithms to enhance the efficiency and reliability of wind energy systems, contributing to global efforts to achieve sustainable energy goals.

Isomeric Dimer Acceptors for Stable Organic Solar Cells with over 19 % Efficiency.

The strategy of isomerization is known for its simple yet effective role in optimizing molecular configuration and enhancing the power conversion efficiency (PCE) of organic solar cells (OSCs). However, the impact of isomerization on the design of dimer acceptors has been rarely investigated, and the relationship between the chemical structure and optoelectronic property remains unclear. In this study, we designed and synthesized two dimer acceptor isomers named D-TPh and D-TN, which differ in the positional arrangement of their end capping groups. Compared to D-TN, D-TPh exhibited enhanced backbone planarity, elevated lowest unoccupied molecular orbital energy level, and more ordered molecular stacking. Consequently, the OSC device based on PM6 : D-TPh achieved a PCE of 19.05 %, higher than that (PCE=18.42 %) of the device based on PM6 : D-TN. Large-area PM6 : D-TPh devices (1 cm2) yielded a PCE of 18.00 %. More importantly, the extrapolated T80 lifetime of the PM6 : D-TPh device is over 2800 h with MPP tracking under continuous one-sun illumination. These results suggest that isomerization strategy is an effective way to optimize the molecular configuration of dimer acceptors for the fabrication of high-efficiency and stable OSCs.

Sand cat swarm optimization based maximum power point tracking technique for photovoltaic system under partial shading conditions

Maximum power point tracking (MPPT) plays a crucial role in photovoltaic systems (PVS). In partial shading conditions (PSCs), the P-V characteristic curves of PVS exhibit multiple peaks. Traditional MPPT algorithms, like perturbation and observation (P&O), may fall into local maximum power points (LMPP) and fail to identify the global maximum power point (GMPP). To address the drawbacks of conventional optimal search algorithms, this paper introduces a bio-inspired approach named Sand Cat Swarm Optimization (SCSO) for maximizing the power output of individual PVS. The SCSO can mitigate the adverse effects of partial shadows on PVS performance by precisely identifying the GMPP. In comparison to other bio-inspired algorithms, SCSO exhibits lower complexity and higher efficiency by utilizing only one optimization parameter. SCSO’s performance is evaluated in four scenarios: uniform irradiance, complex partial shading conditions, step-varying stochastic irradiance, and gradual irradiance. A comparative analysis is conducted with Particle Swarm Optimization (PSO), Cuckoo Search Algorithm (CSA), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Moth-Flame Optimization (MFO), Crow Search Algorithm (CSA), Slap-Swarm Optimization (SSA) and P&O, focusing on factors such as high efficiency, accuracy, convergence time, and implementation simplicity. Simulation results demonstrate that, on average, the tracking time improves by 19.91%, achieving an efficiency of over 98% while maximizing energy yield. Simultaneously, experimental results indicate that the SCSO is capable of tracking to a larger power output in a shorter time, with an average tracking efficiency improvement of 3.16%.

A Novel Strategy for Multitype Fault Diagnosis in Photovoltaic Systems Using Multiple Regression Analysis and Support Vector Machines

This study focuses on analyzing common fault types in photovoltaic (PV) modules, employing fault diagnosis methods based on machine learning technology to enhance the accuracy and efficiency of diagnosing faults in solar power systems. Initially, we collected relevant data from the solar power system and used data analysis techniques to identify system faults, designing a human-machine monitoring interface for practical application. Furthermore, the experimental results proved that the system could accurately identify eight major types of faults, including solar panel output circuits, energy storage batteries, maximum power point tracking (MPPT) controllers, inverters, dust accumulation, loosening of mounting rack screws, damage to the mounting rack foundation, and deformation of the mounting rack structure. Particularly in the detection of dust accumulation, we developed a new method of estimating power generation from multiple regression analysis (MRA), which closely aligns the estimated power output with the actual power output, highlighting the significant impact of dust accumulation on the efficiency of solar power systems. Next, by integrating voltmeters and support vector machines (SVM) into the solar PV array modules, we are able to quickly and accurately measure and locate short-circuit and open-circuit faults in bypass diodes. Ultimately, the proposed PV fault diagnosis strategy includes diagnostics for dust accumulation and mounting frame faults, making it particularly suitable for areas with severe air pollution and frequent earthquakes, providing a comprehensive fault diagnosis solution.

Design and simulation of zeta solar charge controller with artificial neural network MPPT

Fossil fuel are non-renewable energy sources with constrained reserves. This advocates renewable energy as a viable alternative for electricity generation. PV is a renewable energy source that harnesses solar energy. The current issue with PV is its low efficiency and elevated cost. Photovoltaic control devices and Maximum Power Point Tracking (MPPT) mimic human neural networks in processing multiple conditions and providing solutions from current reference data to optimise photovoltaic power. This technique is known as Artifical neural network (ANN). This method uses a zeta converter to adjust the photovoltaic voltage to supply specific loads, allowing ANN to optimize power as sunlight intensity changes. In this work, a charge controller for solar applications was designed using MPPT algorithms. This is responsible for overseeing the battery circuit and producing PWM signals to regulate zeta converter according to the battery voltage. The artificial neural network (ANN) received as inputs temperature and irradiation (G). Several simulations verified the efficiency of the suggested MPPT algorithm. The Zeta solar charge controller (ZSCC) device achieved up to 92% efficiency with low irradiance. The proposed ANN-based MPPT controller and perturb-observed Maximum Power Point Tracking (MPPT) algorithm were compared in different solar insolations. The simulation results show that MPPT-ANN can track power up to 2000 Watts for a 2000 WP photovoltaic in 0.056 seconds, with no power oscillations at the MPP.

Advanced Perturb and Observe Algorithm for Maximum Power Point Tracking in Photovoltaic Systems with Adaptive Step Size

Maximum power point tracking (MPPT) algorithms are commonly used in photovoltaic (PV) systems to optimize the power output from the solar panels. Among the various MPPT algorithms, the perturb and observe (P&O) algorithm is a popular choice due to its simplicity and effectiveness. However, the basic P&O algorithm has some limitations such as oscillations and steady-state error under rapidly changing irradiance conditions. The enhanced algorithm includes a modified perturbation step and a dynamic step size adjustment scheme. This reduces the oscillations and improves the tracking accuracy. In the dynamic step size adjustment scheme, the step size is adjusted based on the rate of change of the PV output power. This improves the tracking performance under rapidly changing irradiance conditions. In order to prove the performance of the designed control algorithm, we will test it under simple climatic conditions of fixed temperature (30°C) and variable irradiation in the form of steps (500W/m² and 2000w/m²) and see the system response. The performance of the enhanced P&O algorithm has been evaluated using MATLAB simulations.

Next-Generation MPPT: neural network-driven optimization for superior solar performance

This paper presents a novel smart Maximum Power Point Tracking (MPPT) method based on a Neural Network Controller (NNC) to enhance solar power efficiency and mitigate significant oscillations. Traditional MPPT techniques, including perturbation and observation, fixed and variable incremental conductance methods, and fractional open circuit voltage, often suffer from slow response to sudden environmental changes (temperature and irradiation), inaccurate tracking, lack of prediction abilities, and complex structures. These drawbacks lead to power loss, reduced effectiveness of photovoltaic (PV) systems and raised broadband oscillations. To address these issues, we propose an intelligent MPPT strategy utilizing NNC. The NNC inputs are the error and derived error (E&ΔE) between the reference and actual inputs, with the duty cycle (D) as the target output. The proposed algorithm demonstrates an efficiency of approximately 0.75% compared to 0.63% for the P&O method. Simulation and experimental validation results confirm the NNC strategy's effectiveness, robustness, and efficiency, providing a thorough system analysis.

A Novel EPSO Algorithm Based on Shifted Sigmoid Function Parameters for Maximizing the Energy Yield from Photovoltaic Arrays: An Experimental Investigation

This paper presents a new method for maximizing the energy production of photovoltaic (PV) arrays, utilizing the Enhanced Particle Swarm Optimization (EPSO) algorithm. In contrast with the classical PSO, the proposed EPSO algorithm employs shifted sigmoid functions in order to adapt the acceleration coefficients and the inertia weight instead of using constant coefficients. The EPSO algorithm proves its effectiveness even in challenging either condition, such as abrupt variations in solar irradiance or instances of partial shading conditions (PSCs). The proposed EPSO-based Maximum Power Point Tracking (MPPT) controller has undergone testing and a comparative analysis alongside well-known metaheuristic algorithms, which include the PSO, Bat Algorithm (BAT), Grasshopper Optimization Algorithm (GOA), and Grey Wolf Optimizer (GWO). The results obtained through simulations consistently demonstrate that the EPSO-based MPPT method surpasses other metaheuristic approaches in terms of energy yield and the speed at which it tracks the Maximum Power Point (MPP) under PSCs. In addition, the proposed EPSO algorithm demonstrates robustness, maintaining consistent tracking of the MPP during sudden solar radiation changes. Experimental validation on a real PV system affirms these findings, demonstrating that the EPSO algorithm surpasses its counterparts by achieving a high MPPT efficiency of approximately 99.19% in a fast-tracking time of 2.4 s while maintaining minimal power oscillations of around 2.04 %.

A modified particle swarm optimization-based adaptive maximum power point tracking approach for proton exchange membrane fuel cells

Fuel cells are one of the most promising renewable energy sources, offering advantages like reliability, eco-friendliness, and low pollutant emissions, which have spurred rapid advancements in power generation technologies. However, fuel cells face significant challenges, including high initial costs, limited fuel availability, and the difficulty of maintaining operation at the maximum power point, which hinders their use in stand-alone applications. In this paper, a Modified Particle Swarm Optimization (MPSO) method is proposed for maximum power point tracking (MPPT) to optimize the power output of Proton Exchange Membrane Fuel Cells (PEMFCs). The proposed method dynamically adjusts to key operational parameters such as cell temperature, hydrogen partial pressure, and membrane water content, areas that have not been comprehensively addressed in previous research. In this paper, an MPSO algorithm-based MPPT tracking approach without a PID controller is proposed to achieve the maximum power point (MPP) of a PEMFC. Under rapid temperature fluctuations in the fuel cell, the proposed MPSO MPPT method achieved a maximum power of 1223.5 W with an average of 5.66 iterations. In comparison, the meta-heuristic particle swarm optimization (PSO) method and the conventional perturb and observe (P&O) method achieved maximum power outputs of 1218.5 W and 1213.65 W, respectively, with PSO requiring 12.33 iterations. Additionally, the proposed approach showed improvements in power efficiency by 2.47 %, 2.87 %, and 13.58 % for the Jaya algorithm. demonstrating effective MPPT tracking under different operating conditions and perturbations. The MPSO method is implemented in the Simulink/MATLAB environment and is compared with the Perturb & Observe (P&O) and Conventional PSO (CPSO) methods. The results demonstrate that the proposed MPSO approach outperforms these traditional techniques in terms of tracking speed, efficiency, and stability under varying conditions. This successful implementation lays a strong foundation for future integration into real-world PEMFC systems.

Configuration optimization of PV array for stratospheric airship under nonuniform radiation condition

The nonuniform solar irradiation on the stratospheric airship surface leads to overlooked mismatch losses in photovoltaic (PV) array. Proper configuration for PV array is crucial but frequently neglected in airship energy system research. In this paper, a mathematical model coupled with the layout of PV array on airship surface, radiation model, electrical model and energy balance model is established. The deployment of PV pack based on the distribution characteristics of solar radiation on airships surface is analyzed. Three maximum power point tracking configurations for the PV array are investigated in detail. The results indicate that mounting PV pack in axial series on stratospheric airships leads to higher power output compared to parallel-connected deployment, PV packs on stratospheric airships exhibit significant output power differences along the circumferential direction and minor differences along the axial direction, the peak of daily maximum output difference ratio is merely 0.4 % between the PV array under the distributed maximum power point tracking (DMPPT) and the partial distributed global maximum point tracking (PDMPPT) configuration, and PDMPPT configuration needs much fewer module, it emerges as the optimal choice for stratospheric airship PV array. This investigation can provide references for the further PV array configuration optimization under nonuniform radiation condition.

Research on PV array reconstruction and Full-cycle maximum power point tracking technology of space solar power station

Space solar power station is an energy system that converts solar energy into electrical energy in the space environment and then transmits it to the space platform or ground using wireless power transmission technology. To improve the power generation and system efficiency of the space solar power station, an adaptive and reconfigurable photovoltaic array with multi-configuration is proposed, which can avoid large attenuation of the output power and efficiency of the photovoltaic array when the photovoltaic modules have a fault occurs or the receive different irradiation intensity. Then, according to the orbit area and light condition of the space solar power station, the operation mode are divided in detail. Furthermore, a novel full-cycle and multi-mode GMPPT (maximum power point tracking) strategy is proposed. Compared to the single mode MPPT, the control strategy has shorter response time, faster convergence and higher tracking accuracy. Through the above research, the output power and photoelectric conversion efficiency of space solar power station can be significantly improved.

A comprehensive comparison of advanced metaheuristic photovoltaic maximum power tracking algorithms during dynamic and static environmental conditions

This study introduces a novel technique for achieving the global peak (GP) in solar photovoltaic (PV) systems under partial shadowing conditions (PSC) using the Dandelion Optimizer Algorithm (DOA), inspired by the dispersal of dandelion seeds in the wind. The proposed approach aims to enhance the power generation efficiency of PV systems across various scenarios, including dynamic uniform, dynamic PSCs, static uniform irradiances, and static PSCs. The proposed approach improves tracking efficiency, provides non-oscillatory steady-state responses, and reduces transients as well as enhancing the dynamic performance of the whole system. Simulation and hardware-in-loop (HIL) experiments demonstrate that the DOA outperforms several state-of-the-art techniques, such as hybrid grey wolf optimizer since-cosine algorithm (HGWOSCA), grasshopper optimization algorithm (GOA), dragonfly optimizer (DFO), particle swarm optimizer with gravitational search (PSOGS), PSO, cuckoo search algorithm (CSA), perturb &observe (P&O), and incremental conductance (INC), achieving average efficiencies of 99.93 %, 88.84 %, 94.48 %, 87.12 %, 88.05 %, 94.79 %, 93.82 %, 85.25 %, and 77.93 %, respectively. These results underscore the DOA's effectiveness in improving maximum power point tracking (MPPT) performance in solar arrays, particularly under challenging dynamic PSC conditions.