MPPT Efficiency Research Articles

A novel Beluga Whale Optimization for maximum power tracking in photovoltaic systems under shading and non-shading conditions

Partial shading poses a significant challenge for PV systems as it introduces multiple peaks in the panel output characteristics, making the power extraction process more complex. Efficiently locating the global maximum power from such a complex, non-linear curve is crucial for optimal system performance. Conventional MPPT techniques often fail to achieve this, resulting in reduced efficiency. Moreover, many metaheuristic algorithms struggle to balance tracking accuracy, convergence time, oscillations, and computational complexity. Therefore, this manuscript proposes a novel natural-inspired Beluga Whale Optimization (BWO) technique that effectively track the global maximum power without compromising its performance. This iteration-based algorithm integrates effective exploration through twin equations, incorporating the influence of both the best and neighboring position with levy flight function to enhance effective exploitation, and additional particle updation using the whale fall process ensures simultaneous diverse searching phenomenon in complex search spaces resulting in smooth and faster convergence. The algorithm was extensively examined under USC and five PSCs on a 250 W panel using MATLAB 2021a Simulink platform. The effectiveness of the technique was benchmarked against state-of-the-art MPPT techniques such as PO, CSO, PSO, and GWO. The rating-based approach confirms that BWO outperformed these techniques with an average MPPT efficiency of 99.98 %, a tracking time of 0.3195 s, transient state oscillations of 10.85 %, and steady-state oscillations of just 0.000453 % within 4–5 iterations, and other related aspects. Finally, the hardware demonstration using Solar Array Simulator (SAS) validated the performance of the proposed algorithm in the real-time environment, guaranteed the suitability towards practical applications.

An Incremental Step Sensing MPPT Based SI-SIDO Energy Harvester With >99% Peak MPPT Efficiency for an Input Power Range of 30 μW to 33 mW

An Incremental Step Sensing MPPT Based SI-SIDO Energy Harvester With >99% Peak MPPT Efficiency for an Input Power Range of 30 μW to 33 mW

Performance evaluation of a novel self-tuning particle swarm optimization algorithm-based maximum power point tracker for porton exchange membrane fuel cells under different operating conditions

The power generated by a proton exchange membrane fuel cell (PEMFC) is heavily impacted by the change in membrane water content (MWC) and cell temperature. Since PEMFC stacks exhibit nonlinear characteristics, it is crucial to employ a controller that can accurately track the maximum power point (MPP) and extract the most efficient power from the fuel cell (FC) stack. This article introduces a novel MPP tracking technique, based on a self-tuning particle swarm optimization (ST-PSO) algorithm, to maximize power output from PEMFC under different operational conditions. The performance of the ST-PSO algorithm is evaluated through numerical simulations and compared to four well-known metaheuristic algorithms. The results indicate that the proposed ST-PSO-based MPPT technique surpasses the other metaheuristic methods in terms of extracting the maximum power, achieving fast-tracking, and minimizing power fluctuations in various operating conditions. It attained an MPPT efficiency, consistently exceeding 99.602 % and 99.545 %, while also achieving rapid tracking times of no more than 0.366 s and 0.297 s for the two tested scenarios. Moreover, the ST-PSO controller exhibits robustness and consistent tracking of the MPP. Experimental validation of the ST-PSO controller confirms its robustness and superiority over the other tested algorithm, achieving the highest MPPT efficiency of approximately 98.94 % with a rapid tracking time of 2.0 s. Additionally, it demonstrates the lowest power fluctuations of about 2.26 %, providing a stable power output.

Extraction of maximum power from PV system based on horse herd optimization MPPT technique under various weather conditions

This paper introduces a novel approach that extracts the maximum power from photovoltaic (PV) system utilizing the Horse Herd Optimization (HHO) algorithm under different weather conditions, including fast changes in solar radiation (FCSR) and partial shading conditions (PSCs). The HHO algorithm is a technique for optimization that mimics the movement behavior exhibited by horses within a herd. The proposed MPPT controller has been tested and compared with other recognized metaheuristic algorithms, including the Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Flower Pollination (FP), Deterministic PSO (DPSO), and Cuckoo Search (CS). According to the simulation results, the proposed HHO-based MPPT method is found to outperform other considered metaheuristic methods in terms of the maximum power extraction, fast-tracking, and settling times under various weather conditions. Additionally, the suggested MPPT controller is robust and can continuously track the MPP under the FCSR. The performance of the proposed HHO controller is validated experimentally on a real PV system. It is demonstrated that the proposed HHO algorithm is robust and capable of outperforming the other counterpart metaheuristic algorithms as well as offering the highest MPPT efficiency of about 98.81 % with a rapid tracking time of 2.2 s. Furthermore, it has the lowest power oscillations of about 1.91 %, ensuring stable and consistent power output.

Hybrid Artificial Rabbit Optimization and Perturb & Observe MPPT for Grid-Connected PV System

The Perturb & Observe for MPPT algorithm is frequently utilized in solar systems. A faces significant challenges in accurately tracking peak power under changing temperature and irradiance objectives. An essay suggests a fresh idea for hybrid MPPT based on an algorithm that combines the advantages of Artificial Rabbit Optimization (ARO) as well as P&O algorithms. A proposed algorithm overcomes the limitations of traditional P&O algorithms by capturing multiple peak power points, enabling it to adapt to rapidly changing conditions. Comparative analysis with the standard P&O technique demonstrates the suggested algorithm’s advantage in terms of accuracy, efficiency, as well as reduced near-steady-state oscillations. In suggested model achieves an MPPT efficiency of approximately 96% and an overall system efficiency of around 99%. Furthermore, it exhibits a settling time of 0.1 s and a rise time of approximately 0.05 s. These impressive performance metrics highlight the potential of the hybrid ARO and considerably improve the P&O MPPT-based algorithm performance for PV systems by varying the conditions of environments.

A New High-Performance Photovoltaic Emulator Suitable for Simulating and Validating Maximum Power Point Tracking Controllers

Photovoltaic (PV) research is rapidly growing, and the need for controlled environments to validate new MPPT controllers is becoming increasingly important. Currently, researchers face several challenges in testing MPPT algorithms due to the unpredictable nature of solar PV power generation. In this paper, we propose a new photovoltaic emulator (PVE) that could replace solar panels and ensure a highly controllable environment suitable for testing photovoltaic (PV) systems. In this PVE, the complex nonlinear equations of the PV cell/module are fast computed and resolved by a new linearization technique which involves the systematic breakdown of the current-voltage ( I - V ) curve of the PV into twelve linear segments. Based on input environmental conditions, an artificial neural network (ANN) was constructed to assist the linearization process by predicting the current-voltage boundary coordinates of these segments. Using simple linear equations, with the segment boundary coordinates, a reference voltage was generated for the PVE. A nonlinear backstepping controller was designed to exploit the reference voltage and stabilize the power conversion stage (PCS). The PVE was optimized using particle swarm optimization (PSO). Several tests have shown that the proposed nonlinear controller provides better dynamic and robust performance than the PI controller, the most reputable and recurrent control method in the area of PVE. The PVE was coupled with a recently proposed integral backstepping MPPT controller and analyzed under several dynamic conditions, including the MPPT test specified by EN 50530. It was found that the accuracy of the proposed PVE measured by its relative error is less than 0.5%, with an MPPT efficiency of greater than 99.5%. The attractive results achieved by this PVE make it especially suitable for simulating and validating MPPT controllers.

Super twisting sliding mode-type 2 fuzzy MPPT control of solar PV system with parameter optimization under variable irradiance conditions

The need for renewable energy sources is increasing while the use of non-environmentally energy sources is decreasing day by day. At this point, solar energy which is a great natural energy source comes to the fore. In this study, MPPT control is performed based on sliding mode control as a robust control method. A super-twisting sliding mode controller has been developed and type 2 fuzzy set has been adapted to the system to reduce the chattering problem. The developed MPPT control algorithm is applied to a solar PV system and tested under variable irradiance conditions. In addition, the parameters of super twisting sliding mode and type 2 fuzzy set are optimized. The efficiency of the proposed MPPT algorithm is presented with tables and graphics that contains sliding mode MPPT, super twisting sliding mode MPPT and super twisting sliding mode-type 2 fuzzy MPPT methods. The results show the MPPT efficiency of proposed system with a robust structure.

MPPT Design for PV-Powered WPT System with Irregular Pulse Density Modulation

Electric vehicles (EVs) are well-known as environmentally friendly systems. Therefore, the demand for EVs is rapidly increasing every day, but for charging batteries of EVs eco-friendly electrical energy sources are needed. To use a clean energy source, this article proposes a wireless power transfer (WPT) system energized by photovoltaic panels. In this study, pulse density modulation technique controlled with incremental conductance algorithm is preferred in the WPT system for both transferring power from primary to secondary windings and for tracking maximum power point (MPP). The proposed system was tested with an experimental prototype and the results were presented. As a result, both 99.22% MPPT efficiency was obtained and power was transferred from primary to secondary with 90% efficiency.

Design of Improved Incremental Conductance with Fast Intelligent (FI) Based MPPT Technique for Solar PV System

Currently, the solar PV power extraction technology is undergoing significant improvement. Towards this, the paper proposed the design for a photovoltaic (PV) array and the output performance of a photovoltaic system under the influence of irradiance. To achieve this, the design for improved incremental and conductance fast tracking INC -FI based MPPT technique for solar PV system has been presented. The purpose of employing the improved INC -FI technique is to improve the efficiency of the system. The accuracy and performance of the proposed INC -FI method was increased due to its better tracking capability by utilizing variable ΔD for tracking the MPP in comparison to the conventional INC method at variable temperature while keeping the irradiance constant. Further, the results of the proposed method were compared with the conventional method where the INC -FI based technique outperforms the conventional INC method in terms of better accuracy. For the irradiance with 800w/m2, the achieved MPPT efficiency was 58.21 for conventional method and 80.53 for the improved technique. It was also noted that the tracking efficiency of the conventional method was 84.39 as compared to 99.92 for the proposed INC -FI technique in terms of MPPT efficiency at the irradiance of 1000w/m2. Furthermore, the improved method delivered fast tracking ability of the MPPT system with a time of less than 10 s(approx.). The MATLAB Simulink platform was utilized for designing the proposed technique. In future, the proposed INC based technique would be implemented on hardware for better outcomes and validation.

A 1.4mW to 119mW, Wide Output Power Range Energy Harvesting System With 2-D Fast MPPT Based on HC for 1k to 50k Illuminated Solar Cell

A photovoltaic (PV) energy harvesting system with 2-Dimensional Fast Maximum Power Point Tracking (2-D FMPPT), based on the hill climbing (HC) MPPT technique is proposed. The 3-step MPPT modes for the energy harvesting system are employed to obtain KVOC that is independent of environmental effects such as irradiance and radiated temperature. The system consists of a dynamic peak transducer power sensor (DPTPS), Coarse (1st) FMPPT mode, Ton Increment/Decrement Mode, Fine (2nd) mode, MPPT logics, and a controller. The system can handle a voltage range of 0.5 V to 2.4 V and a power range of 1.4 mW to 119 mW. A peak MPPT efficiency of 99.4% and over 96.1% MPPT efficiency in 1 k to 50 k illuminance are achieved. The proposed system is fabricated on 0.18- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu m$ </tex-math></inline-formula> CMOS process and occupies an active area of 1.4 mm2.

Performance evaluation of thermoelectric generator employing SEPIC converter and incremental conductance maximum power point tracking for electric vehicles

This article introduced a unique MPPT controller for thermoelectric generators. Our method uses a modified incremental conductance (IC) algorithm to track MPP efficiently. The proposed method is simple to install, fast-tracking, and stable. Traditionally, MPPT trades off steady-state and transition-state properties. Various designs flows are explored. The study’s common parameters are fast convergence, efficiency, and low oscillations. Use a proposed strategy that effectively handles these difficulties. The proposed methodology improves TEG MPPT efficiency and convergence times.

Modular Level Power Electronics (MLPE) Based Distributed PV System for Partial Shaded Conditions

Photovoltaic (PV) solar energy is a very promising renewable energy technology, as solar PV systems are less efficient because of climate conditions, temperature, and irradiance change. So, to resolve this problem, two PV topologies are used, i.e., centralized and distributed PV systems. The centralized technique is quicker than the distributed technique in terms of convergence speed and a faster power tracking approach. In the event of uniform irradiance, the centralized system also has the benefit of supplying superior energy, but in PS scenarios, a huge amount of energy is lost. However, the distributed approach requires current and voltage measurements at each panel, resulting in a massive data set. Nevertheless, in the event of shading circumstances, the distributed technique is highly effective because a modular level power electronics (MLPE) converter is used. While in a centralized PV system, there is only a single DC-DC converter for the whole PV system. In this research work, a DFO-based DC-DC converter is designed for modular level, with an ability to perform a rapid shutdown of the module under fire hazard conditions, troubleshooting, and monitoring of a module in a very efficient way. The robustness of the proposed MPPT DFO algorithm is tested with different techniques such as Cuckoo Search (CS), Fruit Fly Optimization (FFO), Particle swarm optimization (PSO), Incremental conductance (InC), and Perturb and observe(P&amp;O) techniques. The proposed technique shows better results in terms of MPPT efficiency, dynamic responsiveness, and harmonics. Furthermore, the result of MLPE and the centralized system is verified by using the Helioscope with different inverter companies like SMA, Tigo, Enphase, Solar edge, and Huawei. The results prove that MLPE is a better option in the case of shading region for attaining the maximum power point.

Chaos Game Optimization Algorithm for Parameters Identification of Different Models of Photovoltaic Solar Cell and Module

In order to achieve the optimum feasible efficiency, the electrical parameters of the photovoltaic solar cell and module should always be thoroughly researched. In reality, the quality of PV designs can have a significant impact on PV system dynamic modeling and optimization. PV models and calculated parameters, on the other hand, have a major effect on MPPT and production system efficiency. Because a solar cell is represented as the most significant component of a PV system, it should be precisely modeled. For determining the parameters of solar PV modules and cells, the Chaos Game Optimization (CGO) method has been presented for the Single Diode Model (SDM). A set of the measured I-V data has been considered for the studied PV design and applied to model the RTC France cell, and Photowatt-PWP201 module. The objective function in this paper is the Root Mean Square Error (RMSE) between the measured and identified datasets of the proposed algorithm. The optimal results that have been obtained by the CGO algorithm for five electrical parameters of PV cell and model have been compared with published results of various optimization algorithms mentioned in the literature on the same PV systems. The comparison proved that the CGO algorithm was superior.

HIL-Simulation을 이용한 50kW PV 시스템의 MPPT 제어 성능 검증

This paper proposes a maximum power tracking control algorithm based on the incremental conductance method with a variable step size coefficient that is not affected by the insolation of 50kW PV system. A PV module for consisting 50kW PV system is modeled as a single module considering the real-time calculation of RTBOX, and a control module is modeled to control MPPT and load balance of three-phase interleaved converter. HILS system consists of RTBOX, TMS320F28379D, and interface board. Through PLECS simulation and HILS, under the conditions of changes in insolation, performance of MPPT efficiency, tracking time to the maximum power point, and load balance of interleaved converter are verified.

An Effective Falcon Optimization Algorithm Based MPPT Under Partial Shaded Photovoltaic Systems

Uncertain conditions involving partial shading can be found in large-scale solar photovoltaic (PV) systems. There is a possibility that the performance of the PV system will suffer as a result of partial shading conditions (PSCs) because it creates multiple peaks in the power–voltage (P–V) characteristics. Nevertheless, for the photovoltaic system to be utilized in the most effective manner, it needs to be run at a global maximum power point (GMPP). A new strategy based on the falcon optimization algorithm (FOA) is introduced in this paper for the monitoring of GMPP. The perturb and observe (P&O) and the Particle swarm optimization (PSO) techniques have certain drawbacks that can be resolved using a new optimization method known as the FOA. These limitations include a lower convergence speed and steady-state oscillations. The tracking performance of the proposed method is evaluated and compared to that of three MPPT algorithms, namely grey wolf optimization (GWO), PSO and P&O, for a PV array that is functioning under PSCs and displaying numerous peaks. An implementation of the proposed FOA-MPPT algorithm on a PV system was carried out with the help of MATLAB/SIMULINK. Simulation tests conducted under a variety of partial shading patterns reveal that the proposed FOA outperforms all three MPPT algorithms: GWO, PSO, and P&O. Simulation results show that the MPPT efficiency of FOA in four different partial shading conditions is 99.93 %, 99.82 %, 99.80 %, and 99.81 %, Furthermore, the simulation results show that the tracking time of proposed FOA in four different partial shading conditions is 0.4 s, 0.41 s, 0.39 s, and 0.41 s, respectively. Moreover, the proposed FOA is tested using actual and measurable data from Neom, Saudi Arabia. According to the simulation results, the proposed FOA generates significantly more revenue than other compared algorithms.

MIPSIMO: A Multi-Input Piezo-Adaptive Single-Inductor Multi-Output Energy Harvester Achieving Using a Shared Inductor With an Integrated Analog Computer Achieving 95% MPPT Efficiency

This paper presents a multi-input piezo-adaptive single-inductor multi-output (MIPSIMO) architecture that harvests energy from up to three inputs while regulating up to three loads plus a battery all in a single power stage. The proposed power management unit performs load domain inductor switching to reduce switching losses by 3x and allows recycling of excess energy back to the battery for decoupled single-stage regulation, all while utilizing an adaptive ON-time PFM control method during battery utilization to free up inductor cycles as much as possible for harvesting. The proposed architecture implements a technique that performs multi-phase piezoelectric harvesting (PEH) charge flipping (CF) using the single shared inductor from the power stage together with four flying capacitors that reduce the required inductor size by 8x while achieving 90% flipping efficiency. In addition, a fully-integrated power-extraction analog computer is employed to enable real-time hill-climbing MPPT compatible with PEH sources. Fabricated in 180nm, the analog computer achieves a MPPT efficiency of 95% and the converter supports power range of 6μW to 60mW, and achieves a peak efficiency of 87%.

Enhanced Pv Solar Power System Design with An Mppt Controller as A Function of Temperature

In order to achieve optimum point tracking (MPPT) for the selection of the highest power consumption, the photovoltaic (PV) system was improved. The output power of the PV effect depends on external solar irradiation and the ambient temperature. In the existing MPPT approaches, most take only radiation fluctuations into account, with limited consideration of the consequences of temperature shifts. But the temperature coefficients (TCs), especially in applications where changes in ambient temperature are relative high, play an important role in the PV system. A correct study for the MPPT method was performed with a PV system which considers the temperature change using a variable universe fuzzy logic control (VUFLC). Taking account of the changing atmosphere temperature in photovoltaic panels, the proposed control method will regulate VUFLC contraction and expansion, removing the effect of temperature fluctuations and improving MPPT efficiency and thus achieving a quick and accurate tracking control. The proposed strategy was tested under different environmental conditions for a PV module and its control efficiency is compared with simulation and experimental results to other MPPT strategies. Compared with fossil fuels, the best alternative is to generate photovoltaic energy every day. In future, because of the above developments the thermal and nuclear power plant will probably withdraw. The cost of development and installation are rising in comparison with other renewable options. The article addresses a better configuration with an MPPT controller for a PV solar power system compared to other strategies dealing with the non-shaded, partial shading of variable sun-irradiance. This helps designers and maintainers build maintenance processes and phases.

An Accurate Time-Based MPPT Circuit With Two-Period Tracking Algorithm and Convergence Range Averaging Technique for IoT Applications

This article proposes a highly efficient Time-Based Maximum-Power-Point-Tracking (TB-MPPT) integrated-circuit. Conventional TB-MPPT circuit shows unstable status (oscillation) near the Maximum Power Point (MPP), which causes the degradation of the overall power tracking efficiency. To overcome this critical issue, the proposed circuit separates the tracking operation into two periods, and it utilizes convergence range averaging technique with an adaptive perturbation step according to each control signal to accurately extract the current power level without oscillation problem. This work is fabricated in 180 nm CMOS process to demonstrate advantages of the proposed scheme. With 0.47V 12mW Photovoltaic (PV) cell, the measurement results show 94.2% of MPPT efficiency and 91.6% of power conversion efficiency (PCE). It occupies silicon chip area of $1.69~\text {mm}^{2}$ .

A novel unified maximum power extraction framework for PMSG based WECS using chaotic particle swarm optimization derivatives

The most important issue in the use of wind energy conversion systems (WECS) is to ensure maximum power extraction for attaining increased efficiency. In this study, maximum power extraction frameworks operating the state-of-the-art optimization methods are presented for permanent magnet synchronous generator (PMSG) based WECS. These frameworks consist of a fast terminal sliding mode control (FTSMC) based MPPT controller and a hybrid MPPT approach that combines chaotic based particle swarm optimization (PSO) derivatives and optimal relation based (ORB) method. Chaotic dynamic weight PSO (CDW-PSO) and Gauss map based chaotic PSO (GM-CPSO), which are remarkable and recent optimization techniques, are utilized to achieve optimum coefficients to ensure efficient MPPT operation. After acquiring the optimum coefficients, the framework passes to ORB operation part. Moreover, the proposed frameworks track extracted power within certain limits during the operation of WECS and make transition between the best coefficients if necessary. On the other hand, FTSMC is used to track the MPPT references that are determined via hybrid MPPT algorithms. To validate the proposed frameworks, they are tested in Matlab/Simulink environment for three specific wind speed scenarios. GM-CPSO based ORB and CDW-PSO based ORB MPPT methods are compared with ORB and tip speed ratio (TSR) methods under these scenarios. Consequently, it is revealed that proposed methods contribute to MPPT efficiency by providing higher power extraction than other conventional methods.

Extended operating range of PV module-level CHB inverter

In this paper an enhanced distributed maximum power point tracking (DMPPT) algorithm, along with an improved hybrid modulation method for a photovoltaic (PV) module-level Cascaded H­Bridge (CHB) inverter, is proposed. The advantages and drawbacks of the CHB topology in distributed PV generation systems are highlighted. The main benefits are related to the higher granularity of the PV power control, which mitigates mismatch effects, thus increasing the power harvesting. Nevertheless, heavy unbalanced configurations need to be properly addressed by means of dedicated control action. The proposed control strategy allows to overcome the operating limits of PV CHB inverter, by integrating in the MPPT algorithm a limitation of the individual cell modulation index. Experimental tests carried out on a laboratory prototype of a single­phase seven­level PV CHB inverter, evidence the effectiveness of the proposed control and modulation approach in terms of extended operating range and MPPT efficiency.