Maximum Power Point Tracking Research Articles

Performance evaluation of fuel cell based on a DC-DC boost converter through optimized MPPT controller

The current electric vehicle domain is increasingly focused on fuel cell technologies due to its flexibility, steady supply of power, low atmospheric pollution, increased startups, and rapid responses. Fuel cells exhibit nonlinear power versus current characteristics, making it challenging to extract maximum peak power from the fuel stack. To address this, this work introduces an adaptive Coati Optimization algorithm combined with a Tilt-integral-derivative (TID) controller (TID-ACOA) to find the maximum power point (MPP) of the fuel stack systems, ensuring maximum power extraction. The proposed MPPT controller is compared with other MPPT controller, including PI, TID, and TID-COA. Comprehensive evaluations are conducted on tracking current, voltage, maximum power extraction, MPPT controller efficiency, converter voltage settling time, and oscillations. The fuel stack’s low output voltages are enhanced using a boost DC-DC converter, and the entire fuel stack-fed boost converter systems is modeled using MATLAB/Simulink. Simulation result show that the TID-ACOA MPPT controller achieves higher MPP tracking efficiency compared to conventional controllers.

Maximum power point tracking control of wind turbines based on speed hysteresis loop to reduce drive-train loads

By adding compensation torque based on the optimal torque, the torque compensation control method expands the unbalanced torque at varying wind speeds, thereby improving the maximum power point tracking (MPPT) performance of wind turbines (WT). However, while improving the wind energy capture efficiency, such methods will lead to drastic fluctuations of generator torque, resulting in significantly increased drive-train loads. To solve this problem, based on the amplitude-frequency characteristics analysis of the WT system transfer function, it is found rotor speed information can not only reflect the main trend of wind speed changes, but also has the characteristics of not being easily affected by the high-frequency component of wind speed. Therefore, setting compensation torque according to it can effectively alleviate the above phenomenon. On this basis, the MPPT control of WT based on speed hysteresis loop to reduce drive-train loads is proposed. The speed information is used to replace the torque information to set the compensation torque amplitude term, and the speed hysteresis loop is introduced to improve the design of the compensation torque symbol term, which can significantly decreased drive-train loads without compromising on power capture. Finally, the effectiveness of the proposed method is verified by the experiments.

Current-Sensorless Method for Photovoltaic System Using Capacitor Charging Characteristics

The installed capacity of photovoltaic (PV) systems has increased significantly over the past few decades, and related technologies have advanced significantly. The electrical characteristics of a PV system change nonlinearly based on irradiation and temperature, and the I–V characteristic curve, expressed in terms of the voltage and current, is used to verify these characteristics. The maximum power point tracking (MPPT) control method was applied to maximize the performance of the PV system. Voltage and current sensors are used to control the I–V characteristic curve and MPPT; however, current sensors have various disadvantages in terms of price and system configuration. Therefore, this study presents a method for calculating the current of a PV system using the charging characteristics of a capacitor. The method presented in this paper analyzes the I–V characteristic curve’s qualities through simulations and experiments under normal, shaded, and mismatched conditions of the PV module.

A novel interleaved BIFRED-Zeta with intelligent MPPT for PV applications

ABSTRACT The current energy perspective postulates profound presence of Renewable Energy Sources (RESs), resulting in need for optimal DC-DC converter design to assure efficient power conversion and minimise losses. By minimising energy losses and improving power quality, an optimal converter design helps ensure a stable and reliable power supply from RESs, which is essential for meeting the growing demand for clean energy. Therefore, this research introduces an innovative Interleaved BIFRED-Zeta converter configuration, designed to meet all criteria associated with an optimal converter design. The interleaved architecture of converter allows the reduction of current ripples and voltage stress along with the improvement of efficiency. Furthermore, the absence of transformer in converter structure enables it to be simple, cost effective and less bulky. The Interleaved Boost Integrated Flyback Rectifier Energy Storage DC-DC (BIFRED)-Zeta converter duty cycle is adjusted continuously using Cascaded Artificial Neural Network (ANN) Maximum Power Point Tracking (MPPT) to further maximise Photovoltaics (PV) system power output. The capability of suggested converter design is demonstrated by the results obtained using simulation and laboratory prototype implementation. Thereby, a peak efficiency of 97.6% is estimated to be achieved using suggested converter design.

Enhanced charge carrier transport and defects mitigation of passivation layer for efficient perovskite solar cells

Surface passivation has been developed as an effective strategy to reduce trap-state density and suppress non-radiation recombination process in perovskite solar cells. However, passivation agents usually own poor conductivity and hold negative impact on the charge carrier transport in device. Here, we report a binary and synergistical post-treatment method by blending 4-tert-butyl-benzylammonium iodide with phenylpropylammonium iodide and spin-coating on perovskite surface to form passivation layer. The binary and synergistical post-treated films show enhanced crystallinity and improved molecular packing as well as better energy band alignment, benefiting for the hole extraction and transfer. Moreover, the surface defects are further passivated compared with unary passivation. Based on the strategy, a record-certified quasi-steady power conversion efficiency of 26.0% perovskite solar cells is achieved. The devices could maintain 81% of initial efficiency after 450 h maximum power point tracking.

A Surface-Reconstructed Bilayer Heterojunction Enables Efficient and Stable Inverted Perovskite Solar Cells.

The efficiency of perovskite photovoltaics remains distant from their theoretical limits, primarily due to high photovoltage losses. Here a strategy is reported to minimize voltage losses by reconstructing the perovskite surface into a bilayer heterojunction (BLH) structure. Unlike conventional low-dimensional capping layers, typically constrained to a few nanometers to prevent low fill factors, this methodology facilitates a more comprehensive reaction with surface defects, allowing a more substantial capping layer (≈50 nanometers) without compromising charge transport integrity. Time-resolved microwave conductivity analysis indicates a significant reduction in trap density at the top region of the perovskite film, showing an order of magnitude lower than that of the pristine sample. Incorporating this BLH in inverted cells results in a remarkably low photovoltage deficit of 325mV, leading to a power conversion efficiency (PCE) of 26.1% (25.72% certified). The encapsulated device maintains 94% of its original efficiency after 1200h of maximum power point tracking under one sun illumination at 65°C.

A novel hybrid GMPPT method for PV systems to mitigate power oscillations and partial shading detection

The Maximum Power Point Tracking (MPPT) algorithm plays a crucial role in maximizing the power output of a PV system. While uniform shading conditions (USCs) present challenges related to dynamic changes in irradiance, partial shading conditions (PSCs) introduce the complexity of multiple local maxima and rapidly changing shading patterns. Conventional algorithms often struggle to effectively track the global maximum power point (GMPP) during PSCs leading to power losses and oscillations around the MPP. In contrast, advanced algorithms are more complex and require additional computational time causing the algorithm to unnecessarily scan the entire PV curve during USCs, thereby extending the tracking time. This paper proposes a hybrid approach that combines the Modified Perturb and Observe (MP&O) and Modified Coot Optimization Algorithm (MCOA) methods termed as MP&O-MCOA as a robust real time MPPT algorithm. The MP&O method with trapezoidal rule and adaptive step size is employed under USCs, while the MCOA with only one tuning parameter and fewer random numbers is utilized during PSCs to rapidly identify varying conditions. Experimental validation using a boost converter has demonstrated the effectiveness of the proposed method, achieving the fastest average tracking times of 0.24 s under USCs and 0.73 s under PSCs and highest average tracking accuracy above 99 % for all operating conditions tested. The proposed method has proven to outperform other existing MPPT methods under various weather conditions due to its ability to distinguish between USCs and PSCs at exceptional speed and accuracy.

Optimized Energy Management System for Wind Lens-Enhanced PMSG Utilizing Zeta Converter and Advanced MPPT Control Strategies

This paper presents the design and analysis of an efficient energy management system for a wind lens integrated with a permanent magnet synchronous generator (PMSG) and a zeta converter. The wind lens, a ring-shaped structure encircling the rotor, enhances the turbine’s capability to capture wind energy by increasing the wind influx through the turbine. In the contemporary wind energy sector, PMSGs are extensively employed due to their superior performance characteristics. This study integrates a 1 kW PMSG system with a wind lens to optimize power extraction from the wind energy conversion system (WECS) under varying wind speeds. A comparative analysis of different control strategies for maximum power point tracking (MPPT) is conducted, including the incremental conductance (INC) method and the perturb and observe (P&O) method. The performance of the MPPT controller integrated with the wind lens-based PMSG system is assessed based on output DC voltage and power delivered to the load. To evaluate the overall effectiveness of these control strategies, both steady-state voltage and dynamic response under diverse wind conditions are examined. The system is modeled and simulated using the MATLAB R2023a/Simulink 9.1 software, and the simulation results are validated to demonstrate the efficacy of the proposed energy management system.

Optimized Nonlinear PID Control for Maximum Power Point Tracking in PV Systems Using Particle Swarm Optimization

This paper proposes a high-performing, hybrid method for Maximum Power Point Tracking (MPPT) in photovoltaic (PV) systems. The approach is based on an intelligent Nonlinear Discrete Proportional–Integral–Derivative (N-DPID) controller with the Perturb and Observe (P&O) method. The feedback gains derived are optimized by a metaheuristic algorithm called Particle Swarm Optimization (PSO). The proposed methods appear to present adequate solutions to overcome the drawbacks of existing methods despite various weather conditions considered in the analysis, providing a robust solution for dynamic environmental conditions. The results showed better performance and accuracy compared to those encountered in the literature. We also recall that this technique provides a systematic design procedure in the search for the MPPT in photovoltaic (PV) systems that has not yet been documented in the literature to the best of our knowledge.

Comparative analysis of direct coupling and MPPT control in standalone PV systems for solar energy optimization to meet sustainable building energy demands

Solar energy, a prominent renewable source, has reached an installed capacity of 71.78 GW in India. This research explored the load demands of the computer center at an engineering college in Tanjore, Tamil Nadu, India. The computer center at the engineering college has an annual energy requirement of 260,552 kWh/Year. Consequently, the research focused on the planning and implementation of a standalone photovoltaic (SAPV) system, assessing it against the institution's total annual energy consumption. The performance ratio and losses of the SAPV system with both direct coupling and an MPPT charge controller was compared. This comparative analysis aimed to evaluate the efficacy of two solar photovoltaic control methods—SAPV direct coupling and Maximum Power Point Tracking control—in optimizing energy harvesting from solar panels.

ANN-based Maximum Power Point Tracking Technique for PV Power Management under Variable Conditions

Due to the increasing energy demand, traditional fossil fuels are gradually decaying day by day as analyzed by many researchers. Fossil fuels are not sufficient to fulfil the requirement of energy demand and it also produces greenhouse gas emissions. In this regard, worldwide research is going on related to renewable energy sources (RESs) like solar photovoltaic (SPV), wind turbines, fuel cells etc. The source of SPV is plentiful and environment friendly which converts solar radiation to non-linear electrical power. This power is not suitable for a stable system. Therefore, the maximum power point tracking (MPPT) controller is required to find the optimum maximum power point (MPP) to the load. The MPPT technology regulates the duty-cycle in favour of the DC-DC converter to continuously obtain maximum power from the SPV arrays. In the past few decades, the learning of MPPT techniques has made substantial progress in the RESs. This research article analyzes the performance of various MPPT techniques in the proposed SPV framework. The main investigation is to assess different MPPT techniques to optimize power from the SPV framework. The artificial neural network (ANN)-MPPT method has been observed to be more effective in output power production and transient response about the MPP than conventional perturb and observe (P&O)-MPPT and fuzzy logic controller (FLC)-MPPT technology.

تصميم متحكم المنطق الضبابي لتتبع أقصى نقطة قدرة لنظام الطاقة الكهروضوئية

This research paper presents the method of fuzzy logic system to optimize the energy extraction in a photovoltaic (PV) power system. The maximum power of a (PV) module varies due to changing temperature, solar radiation, and load. To maximize efficiency, PV systems use a maximum power point tracker (MPPT) to constantly extract the highest power that can be produced by a solar panel and then deliver it to the load. The general structure of an (MPPT) system contains a (DC-DC) converter (an electronic device that converts a source of direct current DC from one voltage level to another) and a controller. The (MPPT) finds and maintains operations at the maximum power point using a tracking algorithm during variations in weather conditions. Because of the nonlinear behavior of (PV) module current-voltage characteristics and the nonlinearity of (DC-DC) converters due to switching, conventional controllers are unable to provide the best response, especially when dealing with wide parameter variations and line transients. The goal of this work is to design andimplement a maximum power point tracker that uses a fuzzy logic control algorithm. Fuzzy logic naturally provides a superior controller for this type of nonlinear application. This method also benefits from the artificial intelligence approach for overcoming the complexity in modeling nonlinear systems. In order to succeed in this work, an (MPPT) system consisting of a (PV) module, a (DC-DC) converter, and a fuzzy logic controller is designed and simulated in Simulink. Analyses of buck- boost converter characteristics are carried out to find the most suitable topology for the PV system used. An integrated model of the PV module with the identified converter is simulated in MATLAB to derive the expert knowledge needed to formulate and tune the fuzzy logic controller. The simulation results show that, the fuzzy logic controller is able to obtain the desired outcomes which are actual power generation of solar panels at different irradiance and temperature. Keywords: Fuzzy logic, PV, MPPT, DC-DC converter, MATLAB programming

Implementation of the MPPT Particle Swarm Optimization Algorithm on a Hybrid PV-TEG Solar Panel with a DC-DC Buck Converter

Abstract Solar panels necessitate power control to locate the optimal working point amidst quickly changing voltages and diverse weather circumstances. This enables the system to adapt and sustain optimal performance in real-time. In order to address this issue, the optimization methodology known as Maximum Power Point Tracking (MPPT) is employed using an algorithmic artificial intelligence (AI) method. Particle Swarm Optimization (PSO) is a swarm intelligence technique that has effectively tackled diverse optimization issues in intricate systems. The DC-DC Buck Converter, which incorporates Maximum Power Point Tracking (MPPT), serves as an interface between the load and the photovoltaic (PV) system to control the output voltage of the system. A comparison is made between the performance of the Maximum Power Point Tracking (MPPT) technique using the Particle Swarm Optimization (PSO) algorithm and the hill climbing (HC) algorithm developed through MATLAB/SIMULINK simulations. The findings indicate that the PSO algorithm exhibits superior performance in terms of tracking time, output power, and stability, with little fluctuations or noise, as compared to the HC method. The tracking time is 0.02 seconds and 2.52 seconds, respectively. The power at constant irradiation is 14.49 Watts and 14.43 Watts. The power at irradiation variation is 6.98 Watt and 6.96 Watt. The PSO algorithm achieved a remarkable accuracy of 99.76% in this investigation, surpassing that of HC. This enhancement makes the system more effective in acquiring maximum power values.

Improved voltage scanning algorithm based MPPT algorithm for PV systems under partial shading conduction

Maximum Power Point Tracking (MPPT) algorithms are crucial for maximizing power extraction from photovoltaic (PV) systems. Traditional MPPT methods often exhibit suboptimal performance under partial shading conditions. Hence, advanced MPPT algorithms have been developed to enhance efficiency in such scenarios. The voltage scanning-based MPPT algorithm is notable for its superior performance under partial shading, characterized by high tracking speed and efficiency. This study introduces a novel enhancement to this method—namely, a voltage skipping algorithm designed to further improve tracking speed. Unlike conventional scanning techniques, this approach dynamically calculates skipping voltages during operation, eliminating the need for prior knowledge of PV panel characteristics. Performance evaluation was conducted using a MATLAB/Simulink model of a PV system comprising 4 series panels and a boost converter, subjected to simulations under 5 distinct partial shading scenarios. Comparative analyses with established optimization algorithms such as particle swarm optimization, cuckoo search algorithm, and grey wolf optimization highlight the proposed method's effectiveness in terms of tracking speed and maximum power output. The proposed algorithm has worked with high efficiency of 99.28 %, 99.61 %, 99.13 %, 99.16 % and 99.58 % in 5 different scenarios, respectively. By using the proposed method, a significant superiority has been achieved over other methods, with tracking speeds of 0.26s, 0.22s, 0.2s, 0.22s and 0.26s, respectively.

Bottom Contact Engineering for Ambient Fabrication of >25% Durable Perovskite Solar Cells.

The bottom contact in perovskite solar cells (PSCs) is easy to cause deep trap states and severe instability issues, especially under maximum power point tracking (MPPT). In this study, sodium gluconate (SG) is employed to disperse tin oxide (SnO2) nanoparticles (NPs) and regulate the interface contact at the buried interface. The SG-SnO2 electron transfer layer (ETL) enabled the deposition of pinhole-free perovskite films in ambient air and improved interface contact by bridging effect. SG-SnO2 PSCs achieved an impressive power conversion efficiency (PCE) of 25.34% (certified as 25.17%) with a high open-circuit voltage (VOC) exceeding 1.19V. The VOC loss is less than 0.34V relative to the 1.53eV bandgap, and the fill factor (FF) loss is only 2.02% due to the improved contact. The SG-SnO2 PSCs retained around 90% of their initial PCEs after 1000h operation (T90 = 1000h), higher than T80 = 1000h for the control SnO2 PSC. Microstructure analysis revealed that light-induced degradation primarily occurred at the buried holes and grain boundaries and highlighted the importance of bottom-contact engineering.

Achieving Off-Grid Refrigeration in Remote Areas: A Solar-Powered Vapor Compression Refrigerator Prototype with PCM Integration

The availability of vaccines, medicines, and perishable goods in remote or off-grid areas remains a formidable challenge. Integrating solar photovoltaic (PV) systems with refrigeration technology has emerged as a promising solution to address this critical need. This paper presents an autonomous solar-powered refrigerator prototype for off-grid refrigeration in remote areas utilising renewable energy. The system comprises a 160 W photovoltaic module, a 12/24 V DC compressor refrigerator, a lead-acid battery, and a Maximum Power Point Tracking (MPPT) controller. Its main feature is complete autonomy from the electricity grid, thanks to its standalone configuration. An experimental campaign evaluated the system's behaviour in the laboratory for 24 hours at different set-point temperatures. A water-based Phase Change Material (PCM) was implemented to improve its autonomy in severe outdoor conditions. A further experimental campaign emulated the functioning of the prototype while managing the temperature of a sample solution whose melting temperature was equal to – 21°C to ensure its liquid state during the tests. The target range for preserving the sample was defined at ± 1°C. Several real conditions have been considered, such as higher cooling loads realised through 21 litres of additional thermal mass in the refrigerator and pick and place conditions for simulating the opening and closing of the door. The results demonstrate that the solar refrigerator prototype achieves complete autonomy from the electricity grid, paving the way for solutions for preserving perishable goods such as medicines and food in unelectrified areas.

African vulture optimized RNN algorithm maximum power point tracking (MPPT) controller for photovoltaic (PV) system

African vulture optimized RNN algorithm maximum power point tracking (MPPT) controller for photovoltaic (PV) system

Integrated RES With Intelligent MPPT For Efficient Power Generation & Wireless Transmission for PV Applications

The increasing global energy demand and the urgent need to shift to sustainable practices have made the integration of renewable energy sources (RESs) into distribution power systems imperative. This project's main objective is to implement rooftop photovoltaic (PV) systems as an essential part of low-voltage (LV) DC networks' building-integrated centralized generation Maximum Power Point Tracking (MPPT) using Artificial Neural Networks (ANNs) is used to maximize power extraction from photovoltaic (PV) panels and boost power generation efficiency, particularly in partially shaded circumstances. After that, the PV system's generated power is sent to a Luo converter, which effectively tracks and optimizes the power production. For wireless power transmission, the Luo converter's output is then fed into a high-frequency converter. This wireless power transmission improves the system's adaptability and scalability by facilitating smooth energy transfer without the requirement for physical connections. An isolation transformer is attached to the high-frequency converter's output in order to guarantee the system's dependability and safety. The high-frequency converter is isolated from the downstream components by the isolation transformer, which also offers protection against electrical risks. Finally, a battery made especially for use in electric vehicle (EV) applications receives the transformer's isolated output, which is also supplied to DC loads. Finally, we will use the MATLAB 2021a / Simulink program to do a number of numerical simulations in order to validate the suggested controls. The output voltage of 130v is stored in the battery if a voltage of 65 v is taken as output from the photovoltaic system.

Zynq FPGA for hardware co-simulation of Takagi-Sugeno neuro-fuzzy for MPPT algorithm incorporating sensorless wind speed estimation in grid-connected wind system

This paper presents a hardware implementation on the Field Programmable Gate Array (FPGA) Zed-Board of a Maximum Power Point Tracking (MPPT) incorporating pitch angle control system and sensorless wind speed estimation using the Takagi-Sugeno (TS) Adaptive Neuro-Fuzzy Inference System (ANFIS). The described approach is applied specifically to a grid-connected Permanent Magnet Synchronous Generator-based Variable Speed Wind Turbine (VSWT). Firstly, the paper proposes an estimator-based TS-ANFIS model for real-time wind speed estimation, addressing challenges with conventional anemometers, such as precision, and susceptibility to adverse weather conditions. The estimated wind speed guides the calculation of an optimized mechanical speed for MPPT control. Secondly, an MPPT-based TS-ANFIS controller is introduced to achieve the maximum power point, integrating pitch angle control to prevent turbine failures in high wind speeds. Finally, the paper emphasizes the hardware implementation on the FPGA Zed-Board, leveraging its parallel processing capabilities to enhance control system quality by reducing sampling periods and loop delays. Validation includes simulations in Matlab/Simulink using the Xilinx system generator and hardware co-simulation on the FPGA Zed-Board. A comparative analysis highlights the contributions and advancements of the proposed models and controllers compared to recent schemes in the field. Indeed, the proposed MPPT-based TS-ANFIS approach effectively maximizes the power extracted from the VSWT, achieving an estimated average efficiency of 99.84 %. In contrast, PID methods show average efficiencies of 92.63 %. Additionally, compared to other published works, the proposed MPPT-based TS-ANFIS method demonstrates a rapid response time of 0.001 s and lower static error at 0.02 %. Furthermore, the proposed WSE-based TS-ANFIS model exhibits superior performance and effectiveness, yielding a root mean squared error (RMSE) of 0.0085381, a determination coefficient (R2) value of 0.99985, and a Pearson correlation coefficient (r) value of 0.99996. Moreover, the proposed hardware implementation of these approaches maintains lower power usage, operating at a high frequency of 80.38 MHz and achieving a high throughput of 2572.16 Mbps.

Substrate Induced p-n Transition for Inverted Perovskite Solar Cells.

The p- or n-type property of semiconductor materials directly determine the final performance of photoelectronic devices. Generally, perovskite deposited on p-type substrate tends to be p-type, while perovskite deposited on n-type substrate tends to be n-type. Motived by this, a substrate-induced re-growth strategy is reported to induce p- to n-transition of perovskite surface in inverted perovskite solar cells (PSCs). p-type perovskite film is obtained and crystallized on p-type substrate first. Then an n-type ITO/SnO2 substrate with saturated perovskite solution is pressed onto the perovskite film and annealed to induce the secondary re-growth of perovskite surface region. As a result, p- to n-type transition happens and induces an extra junction at perovskite surface region, thus enhancing the built-in potential and promoting carrier extraction in PSCs. Resulting inverted PSCs exhibit high efficiency of over 25% with good operational stability, retaining 90% of initial efficiency after maximum power point (MPP) tracking for 800h at 65°C with ISOS-L-2 protocol.