Maximum Power Point Research Articles

A frequency control strategy with DC microgrid systems for EV stations

ABSTRACT Fluctuations in power supply and demand lead to frequency deviations, which have detrimental effects on the operation of electrical devices and equipment. Therefore, the implementation of effective frequency control mechanisms is essential for enhancing the strength and dependability of microgrid systems. Research aims to develop a frequency control mechanism for an electrical grid system that integrates both wind and photovoltaic (PV) energy sources. The proposed system utilises a Doubly Fed Induction Generator (DFIG) system with frequency controller for regulating frequency and balancing source and demand of power in the grid. To optimise the solar power output, a Luo converter is implemented, which offers high efficiency with improved conversion of power. Additionally, an adaptive neuro fuzzy inference system (ANFIS) maximum power point tracking (MPPT) controller is engaged to ensure optimal regulation of photovoltaic system. DC link obtained from the PV system is then utilised for powering DC loads and charging electric vehicles (EVs) effectively. In order to accomplish this, a bidirectional converter is used, which permits power flow in both ways and facilitates effective EV charging from the microgrid system. The validation of research is conducted on MATLAB software, and the results proves greater converter efficiency of 94.93%, with reduced total harmonic distortion (THD) of 1.24%.

Photovoltaic to electrolysis off-grid green hydrogen production with DC–DC conversion

Green hydrogen (H2), being the product of water electrolysis powered by renewable energy sources, is expected to be an energetic vector of major importance toward a more sustainable energy mix. In this context, photovoltaic (PV) -based H2 production is a key element, where power electronics technologies are critical to enable its development. In off-grid applications, designing DC–DC power electronics converters with high efficiency and high power density is really important since it can impact significantly the global performance of the H2 production system. In this work, a two-stage DC–DC power conversion system composed by an unregulated DCX converter and a regulated partial power converter is considered to increase the H2 production system efficiency. The DCX converter works in open-loop at resonant frequency with a high DC–DC conversion ratio. A partial power converter (PPC), which regulates only a fraction of the total power between its input and output terminals, represents a improvement in PV-to-H2 direct conversion, particularly since the PV panels do not require regulation from zero to nominal voltage, and in this work, is introduced for regulation of the DCX-based system, by optimizing the PV produced power through a maximum power point tracking (MPPT) algorithm along with the current control of the electrolyzer. A comparison with state-of-the-art converters show an important improvement of the proposed DCX with PPC regulated system. Compared to the solution with classical interleaved-buck pre-regulator, significant improvements in terms of efficiency for the whole range of operation are obtained, up to 2.5% higher with the proposed system. Experimental evaluation of the proposed PPC and DCX two stage converter for PV-powered electrolysis system is provided, validating its feasibility and interest for off-grid green hydrogen production application.

Experimental validation of effective zebra optimization algorithm-based MPPT under partial shading conditions in photovoltaic systems

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.

Optimization of Hydrogen Production System Performance Using Photovoltaic/Thermal-Coupled PEM

A proton exchange membrane electrolyzer can effectively utilize the electricity generated by intermittent solar power. Different methods of generating electricity may have different efficiencies and hydrogen production rates. Two coupled systems, namely, PV/T- and CPV/T-coupling PEMEC, respectively, are presented and compared in this study. A maximum power point tracking algorithm for the photovoltaic system is employed, and simulations are conducted based on the solar irradiation intensity and ambient temperature of a specific location on a particular day. The simulation results indicate that the hydrogen production is relatively high between 11:00 and 16:00, with a peak between 12:00 and 13:00. The maximum hydrogen production rate is 99.11 g/s and 29.02 g/s for the CPV/T-PEM and PV/T-PEM systems. The maximum energy efficiency of hydrogen production in CPV/T-PEM and PV/T-PEM systems is 66.7% and 70.6%. Under conditions of high solar irradiation intensity and ambient temperature, the system demonstrates higher total efficiency and greater hydrogen production. The CPV/T-PEM system achieves a maximum hydrogen production rate of 2240.41 kg/d, with a standard coal saving rate of 15.5 tons/day and a CO2 reduction rate of 38.0 tons/day. Compared to the PV/T-PEM system, the CPV/T-PEM system exhibits a higher hydrogen production rate. These findings provide valuable insights into the engineering application of photovoltaic/thermal-coupled hydrogen production technology and contribute to the advancement of this field.

Performance Evaluation of Small Wind Turbines Under Variable Winds of Cities: Case Study Applied to an Ayanz Wind Turbine with Screw Blades

Small wind turbines placed at city locations are affected by variable-speed winds that frequently change direction. Architectural constructions, buildings of different heights and abrupt orography of Cities make the winds that occur at City locations more variable than in flat lands or at sea. However, the performance of Small-wind turbines under this type of variable wind has not been deeply studied in the specialised literature. Therefore, this article analyses the behaviour of small wind turbines under variable and gusty winds of cities, also considering three types of power electronics conversion configurations: the generally used Maximum Power Point Tracking (MPPT) configuration, the simple only-rectifier configuration and an intermediate configuration in terms of complexity called pseudo-MPPT. This general-purpose analysis is applied to a specific type of wind turbine, i.e., the Ayanz wind turbine with screw blades, which presents adequate characteristics for city locations such as; safety, reduced visual and acoustic impacts and bird casualties avoidance. Thus, a wide simulation and experimental tests-based analysis are carried out, identifying the main factors affecting the maximisation of energy production of small wind turbines in general and the Ayanz turbine in particular. It is concluded that the mechanical inertia of the wind turbine, often not even considered in the energy production analysis, is a key factor that can produce decrements of up to 25% in energy production. Then, it was also found that electric factors related to the power electronics conversion system can strongly influence energy production. Thus, it is found that an adequate design of a simple pseudo-MPPT power conversion system could extract even 5% more energy than more complex MPPT configurations, especially in quickly varying winds of cities.

Performance of wind turbine system based on a brushless doubly fed reluctance generator and a six pulse bridge cycloconverter

In this paper, a variable speed wind energy conversion system (WECS) made with a six pulse bridge cycloconverter and a brushless doubly fed reluctance generator (BDFRG) is investigated. The novelty of the proposed work is to use a brushless doubly fed reluctance generator and a six pulses cycloconverter for working in high power and for reducing the cost, also the proposed work use a maximum power point tracking algorithm to capture the optimal power available in the wind in order to increase the efficiency of the system. The power control of this system is applied, to capture the maximum power from the wind by using a maximum power point tracking (MPPT) control technique, to achieve the independent control of the active and reactive powers exchanged between the wind generator and the grid, also to test the possibility to operate in two quadrant modes (subsynchronous and supersynchronous modes). The dynamic modeling equations that describe the suggested system are presented here. The results of the simulation are shown in order to illustrate the performance and efficiency of this system.

PTAA‐infiltrated thin‐walled carbon nanotube electrode with hidden encapsulation for perovskite solar cells

AbstractIn perovskite solar cells (PSCs), expensive gold or silver metal has traditionally been utilized as the rear electrode for highly efficient performance. In this context, carbon nanotube (CNT) electrodes have been considered promising rear electrodes because of their excellent electrical conductivity, mechanical strength, and chemical stability in PSCs. Despite these favorable characteristics, concerns have been raised about the power conversion efficiency (PCE) and stability of PSCs based on CNTs due to the porosity of CNT electrodes. In this study, we employed both poly(triarylamine) (PTAA) infiltration and rear electrode hidden encapsulation approaches to address issues related to the porosity of thin‐walled carbon nanotube (TWCNT) electrodes to achieve high efficiency and stability. The infiltration of low‐molecular‐weight PTAA into the TWCNT electrode reduced electrode porosity while simultaneously improving the interfacial contact of the TWCNT layer with the perovskite layer. Furthermore, a novel encapsulation design was employed to prevent air and water exposure of the TWCNT electrode, which significantly enhanced device stability. PSCs with TWCNT rear electrodes developed on the basis of these strategies have the best PCE of 19.5% and show long‐term stability, retaining 96% and 74% of the initial PCE after 225 h at maximum power point tracking under AM 1.5G illumination and 916 h at 85°C/85% relative humidity, respectively.image

Enhanced MPPT Based on Zebra Optimization Algorithm for Control of a Partially Shaded Photovoltaic System

Objective: The objective of this paper is to adapt the Zebra Optimization Algorithm (ZOA) to track the Global Maximum Power Point (GMPP) of a PV system when subjected to partial shading conditions. Theoretical Framework: Photovoltaic (PV) systems are increasingly being used due to their clean nature and low maintenance cost. However, solar cells exhibit a nonlinear relationship between current and voltage, which necessitates the use of Maximum Power Point Tracking (MPPT) algorithms to ensure the system operates at its full potential. Bypass diodes are integrated into PV modules to protect solar cells from damage during partial shading, but this leads to a distorted multi-peak Power-Voltage (P-V) curve. This distortion presents a challenge for algorithms to distinguish between Global Maximum Power Point (GMPP) and Local Maximum Power Points (LMPPs). Method: The Zebra Optimization Algorithm (ZOA) is applied to track the GMPP of a PV system under partial shading conditions. Simulations were conducted using the MATLAB/Simulink platform to evaluate the algorithm’s performance. The focus was on ensuring fast and accurate tracking of the GMPPT under rapid changes in solar irradiance levels. Results and Discussion: Simulation results demonstrated that the ZOA-based MPPT algorithm effectively tracked the GMPPT of the PV system, even under rapid variations in solar irradiance. The algorithm proved its accuracy and speed in distinguishing the GMPP from LMPPs, addressing the challenges posed by the distorted P-V curve. Research Implications: The successful application of the ZOA for tracking the GMPP in partial shading conditions offers a significant improvement for PV systems. This can lead to enhanced efficiency in energy production and more reliable performance in real-world scenarios, where shading and other environmental factors frequently affect solar panels. Originality/Value: This study introduces the adaptation of the Zebra Optimization Algorithm (ZOA) in MPPT for PV systems, addressing the issue of distinguishing between GMPP and LMPPs under partial shading. The approach demonstrates both innovation and practical value in improving the efficiency of PV systems in challenging environmental conditions.

Design and Analysis of a Triple-Input Three-Level PV Inverter with Minimized Number of MPPT Controllers

Photovoltaic (PV) energy has been a preferable choice with the rise in global energy demand, as it is a sustainable, efficient, and cost-effective source of energy. Optimizing the power generation is necessary to fully utilize the PV system. Harvesting more power uses cascading of impedance source converters taking input from low-voltage PV arrays which requires multiple maximum power point tracking (MPPT) controllers. To solve this problem, a three-level inverter topology with a proposed PV arrangement, offering higher voltage boosting and a smaller size with a lower cost suitable for low-voltage panels, is designed in this article. The design criteria for parameters are discussed with the help of the small signal analysis. In this paper, three PV arrays are used to harvest maximum energy, which require only one MPPT controller and employ an extended perturb and observe (P&O) algorithm, being faster, highly efficient, and reducing the computational burden of the controller. Moreover, a three maximum power points tracker algorithm, which perturbs one parameter and observes six variables, is designed for the selected converter topology. Finally, the designed 1.1 kVA grid-connected PV system was simulated in MATLAB (R2023a) which shows that the MPPT algorithm offers better dynamics and is highly efficient with a conversion efficiency of 99.2% during uniform irradiance and 97% efficiency during variable irradiance conditions.

Analysis and control of grid-interactive PV-fed BLDC water pumping system with optimized MPPT for DC-DC converter

In this study, a novel water pumping module fed by grid interactive Photo-Voltaic with a bidirectional Power Flow Control was proposed. In addition to improving the pumping system’s reliability, a water pump is powered by a brushless DC motor drive. This method enables the pump to work at its maximum capacity for the entirety of that day, regardless of the weather. The entire system becomes more reliable as a result of the motor’s increased use of photovoltaic (PV) generated power for pumping applications. Maximum Power Point Tracking (MPPT) controller incorporating Machine Learning algorithm drives bridgeless greater static gain DCDC converter to achieve higher power generation point and increment PV efficiency. The PV array’s operation would be managed using the ML back propagation technology to capture the most electricity under any ecological circumstance. A BLDC motor is fed by a Voltage Source Inverter (VSI) that includes a DC bus controlled in both directions by a unit vector template (UVT) approach incorporated in a single-phase voltage source converter (VSC). Additionally, utilizing a PI controller to manage the DC capacitor voltage in the UVT controller at a particular level is not appropriate for the increased PQ capabilities. However, due to tuning problems with the current controller, this controller is unpopular. The aforementioned problems are resolved by employing a unique intelligent-based fuzzy logic controller that achieves good performance features. In this technique, the function of a PV array at its Maximum Power Point (MPP), as well as power quality enhancements and a decrease in Total Harmonic Distortion (THD) of the grid are accomplished. The proposed PI controller attains a significant voltage THD of 3.736. The PI controller, on the other hand, managed to achieve a load voltage THD of 2.629%. The ANFIS method, whose value is 1.739%, is discovered to have a lower THD than all remotes with improved features, it lessens abrupt swings while maintaining steady DC-link voltage.

Advancing wind energy conversion: smart maximum power point based on M5-Pruned algorithm for enhanced wind energy production

Advancing wind energy conversion: smart maximum power point based on M5-Pruned algorithm for enhanced wind energy production

Photovoltaic Maximum Power Point Tracking Technology Based on Power Prediction Algorithm Combined with Variable Step Length Disturbance Observation Method

Under partial shading conditions, the system operating sequence of photovoltaic cells does not fall on a single characteristic curve, and the traditional maximum power point tracking (MPPT) control algorithm is prone to causing the system output power to oscillate around the maximum power point. In the case of multiple peaks, the traditional MPPT algorithm is prone to being trapped in a local optimum solution. This paper proposes an MPPT control algorithm based on the WOA-RF prediction algorithm combined with a variable step disturbance observation method. By establishing a photovoltaic system simulation model, the improved algorithm is verified through simulation. The improved MPPT control algorithm can adaptively adjust the step size under different conditions to ensure that the system can quickly and accurately track the maximum power point. At the same time, by optimizing the algorithm logic and parameter settings, it can effectively avoid problems such as local optimum solutions and instability in the system, and improve the system convergence speed and tracking accuracy.

Enhancing efficiency through surface passivation of carbon-based perovskite solar cells

Perovskite solar cells (PSCs) have made significant strides in power conversion efficiency (PCE), but their commercialization remains limited by stability issues. Additionally, the high cost of electrodes like gold necessitates the exploration of more affordable alternatives such as carbon (graphene). In this study, we present an approach that combines material dimensionality control and interfacial passivation using post-device treatment with phenethylammonium iodide (PEAI), an organic halide salt, to enhance the efficiency of carbon-based PSCs. Effective defect passivation is key to further improving the PCE and open-circuit voltage (VOC) of PSCs. Our results show that PEAI successfully passivates defects on the perovskite surface, significantly reducing non-radiative recombination. As a result, we achieved carbon-based PSCs with an impressive efficiency of 19.3%, demonstrating excellent stability under maximum power point tracking (MPPT) for over 900 h.

Improving Buried Interface Contact for Inverted Perovskite Solar Cells via Dual Modification Strategy

AbstractThe non‐wetting issue of the self‐assembled monolayer (SAM) layer can complicate subsequent perovskite deposition and impact device efficiency. This study addresses this challenge using a dual approach involving co‐self‐assembly and a buffer layer to enhance the wettability and interfacial contact of the buried perovskite film. A weakly acidic boronic acid derivative, 4‐N, N‐dimethylbenzeneboronic acid hydrochloride (4NPBA), is used to co‐self‐assemble with the regular SAM molecule on ITO and the subsequent FAI buffer layer further increased perovskite film coverage to 89%. This dual buried interface strategy—SAM‐4NPBA/FAI—results in a flat and dense perovskite interface. The optimized device demonstrates a high fill factor of 88.35%, a power conversion efficiency of 25.29%, and retains over 99% of its initial efficiency after 500 h of maximum power point testing.

A Maximum Power Point Tracking (MPPT) Strategy Based on Harris Hawk Optimization (HHO) Algorithm

To address the multi-peak characteristics of the P-U output characteristic curve when the photovoltaic array is partially shaded, a Maximum Power Point Tracking (MPPT) method combining Harris Hawk optimization, and a variable step conductance increment method is proposed in this article. Although the Harris Hawk Optimization algorithm has advantages in optimizing multi-modal P-U curves, when the lighting conditions suddenly change, the output characteristic curve will undergo drastic changes, causing the algorithm to oscillate repeatedly at the maximum power point. Moreover, due to its inherent limitations, the Harris Hawk algorithm has a low convergence accuracy, slow tracking speed, and is prone to getting trapped in a local optimum. In order to compensate for the shortcomings of the Harris Hawk Optimization algorithm, the variable step conductance increment method is switched for local exploration to improve the algorithm’s ability to escape from the local optimal solution and reduce power oscillation. Finally, the MPPT control of the proposed strategy was simulated and verified on the MATLAB/Simulink simulation (2021) platform. In the MPPT test experiment, the proposed composite algorithm was applied to simulate and verify uneven irradiance conditions.

High-efficiency MPPT strategy for PV Systems: Ripple-free precision with comprehensive simulation and experimental validation

This paper presents a newly developed maximum power point (MPP) tracking algorithm (MPPT) to boost the tracking performance of solar photovoltaic (PV) systems. By functioning PV arrays at their MPP and eliminating the ripple problem in the converter's output, the newly developed strategy can markedly improve the precision of tracking and overcome the issues faced by many existing algorithms. The proposed strategy uses an MPP voltage boundary to control the PV voltage and directly generate the required duty cycle using a mathematic expression, resulting in a high convergence speed, drift problem avoidance, and zero MPP fluctuations. To prove and validate the robustness of tracking of the suggested MPPT strategy, both simulation and experiment validations based on the MATLAB/Simulink and dSPACE DS1104 board platforms, respectively, are carried out under swift variations in solar irradiance. The effectiveness of the proposed approach is evaluated by comparing it to the InC algorithm in terms of tracking accuracy. The results from both simulations and experiments demonstrate that the suggested strategy surpasses the InC approach in multiple aspects. It exhibits a shorter response time, eliminates the ripple problem, and achieves a superior tracking efficiency of 99.60 %.

Deep predictive data representation model control for photovoltaic maximum power point tracking under partial shading conditions

As deep learning progresses in recent years, deep neural networks (DNNs) have penetrated applications in various industries. The global maximum power point (GMPP) tracking issue exists in photovoltaic (PV) systems. This study proposes a deep predictive data representation model control (PDRC)-based PV GMPP tracking method. The PDRC approach establishes a data representation control model centered on deep prediction by extracting the data features such as the current, voltage, and duty cycle of PV systems controllers on a time series basis. The proposed PDRC method can receive signals of voltage and current, then output duty cycle control signals. Thereby PDRC method can control PV output power to track GMPP efficiently. Finally, the proposed PDRC approach is evaluated with the perturbation observation method (P&O), the improved particle swarm algorithm (PSO-P&O), and the modified cuckoo algorithm (CS-P&O) to compare the PV GMPP tracking performance. In the simulation experiments, the PDRC approach increases the average tracking efficiency over the comparison algorithms by at least 7.729 % and decreases the average tracking time by at least 0.0155 s under partial shading conditions (PSCs). In the physical simulation validation, the proposed PDRC approach possesses minimal power perturbation and increases the average tracking efficiency over the comparison algorithms by at least 0.407 %.

Improved Charge‐Transfer Doping in Crystalline Polymer for Efficient and Stable Perovskite Solar Cells

AbstractPerovskite solar cells (PSCs) have significant potential for next‐generation photovoltaic technology applications. However, the instability of hole transport layers (HTLs) becomes the major obstacle to long‐term operational devices, which are affected by the intrinsic thermal instability and loose structure of hole transport materials, as well as the hygroscopicity and migration of dopants. Here, poly(3‐hexylthiophene‐2,5‐diyl) (P3HT) is used as a model crystalline polymer to thoroughly investigate effective p‐type doping strategies and the underlying mechanism. According to Hard–Soft‐Acid–Base theory, the soft base P3HT is more likely to form a stable Lewis acid–base adduct with the reactive soft acid radical, resulting in a strong charge‐transfer interaction, thereby enhancing conductivity and regulating the energy band of the HTL. Meanwhile, the radical cation salt can promote pre‐nucleation to optimize the crystallization orientation of P3HT. The resulting PSCs exhibited the efficiency of 25.16%, which is the highest efficiency reported so far based on doped P3HT. In addition, the resulting devices demonstrated excellent stability, maintaining 96.5%, 96%, and 91% of their initial efficiency after aging under continuous illumination for 2028 h, at 85 °C for 1080 h, and at maximum power point (MPP) tracking under continuous 1 Sun illumination at 85 °C for 528 h, respectively.

Advancing Solar Power Efficiency: Innovations in Material Science and System Optimization for Enhanced Solar Energy Conversion

This study aims to advance solar power efficiency through innovations in material science, surface engineering, and system optimization. The research integrates experimental testing and computational modeling to assess improvements in energy conversion efficiency. The methodology includes testing photovoltaic materials such as perovskite and multi-junction cells, anti-reflective coatings, self-cleaning surfaces, and optimizing panel orientation, cooling systems, and Maximum Power Point Tracking (MPPT) algorithms. Perovskite cells demonstrated a 22% increase in efficiency, while multi-junction cells achieved up to 35% efficiency. Anti-reflective coatings and self-cleaning surfaces improved energy capture by 4% and 7%, respectively. System optimization, including dual-axis trackers and AI-enhanced MPPT algorithms, provided additional efficiency gains of up to 25% and 10%, respectively. These findings demonstrate significant advancements in solar panel performance, providing a clear path for enhancing scalability and commercial viability in diverse environmental conditions. The integration of innovative materials and system optimizations presents a promising solution for making solar energy more sustainable and cost-effective. Future research should focus on improving the durability and reducing the costs of these technologies for widespread adoption.

A Communication‐Free Master–Slave Control of Cascaded‐Type DC Microgrids With Nondispatchable Generations

ABSTRACTThis paper addresses the challenge of integrating dispatchable and nondispatchable distributed generators (DGs) in cascaded‐type direct current (DC) microgrids and proposes a communication‐free master–slave control strategy. In the proposed method, all DGs are treated as voltage sources. A master dispatchable DG is primarily responsible for maintaining a constant DC voltage at the point of common coupling (PCC). The rest slave nondispatchable DGs adjust their output power based on a front maximum power point tracking (MPPT) controller. This communication‐free approach enhances system reliability and ease of implementation. Moreover, it ensures load voltage stability, providing a high‐quality power supply for users. Additionally, it enables nondispatchable DGs to achieve MPPT‐based output power and enables power curtailment operation. Subsequently, a small‐signal stability analysis confirms the efficacy of the proposed strategy. Finally, simulation and experiment results further validate its efficacy.