Maximum Power Point Tracking System Research Articles

Evaluating Outdoor Performance of PV Modules Using an Innovative Explicit One-Diode Model

Due to its simplicity, the one-diode model is commonly used for modeling the operation of photovoltaic (PV) modules at standard test conditions (STC). However, its inherent implicit nature often presents challenges in modeling PV energy production. In this paper, the innovative explicit one-diode model developed by us over time is adapted for estimating PV power production under real weather conditions. Simple yet accurate equations for calculating the energy output of a PV generator equipped with a maximum power point tracking (MPPT) system are proposed. The model’s performance is assessed under various normal and harsh operating conditions against measured data collected from the experimental setup located at the Solar Platform at West University of Timisoara, Romania. As an application of the new equation for maximum power, this paper presents a case study where the energy loss in the absence of an MPPT system is evaluated based on atmospheric and sky conditions.

Approaches for Maximizing Maximum Power Point Tracking and Evaluating Operational Constraints in Photovoltaic Systems

Abstract: Optimization techniques have broad utility in multiple engineering fields, including activities such as modeling, system identification, parameter estimation, forecasting, and control of intricate systems. This innovation emphasizes the use of optimization approaches to control the performance of Photo-voltaic (PV) array systems. The main difficulty lies in the nonlinear correlation between current and voltage (I-V) characteristics of PV arrays, resulting in variations in the location of the Maximum Power Point under varied climatic circumstances. It is crucial to track this Maximum electricity Point efficiently in order to collect the highest amount of electricity possible from the photovoltaic devices. Maximum Power Point Tracking is a tracking procedure that optimizes power generation by dynamically altering the operating voltage to coincide with the MPP. In order to tackle this difficulty, the paper suggests a unique two-stage Maximum Power Point Tracking technique. The initial phase applies the Weightless Swarm Algorithm to evaluate the Maximum Power Point in an offline manner. The subsequent phase employs an online Adaptive Perturb & Observe technique to continuously track the MPP in real-time. In addition, an Approximate Single Diode Model is created to quickly assess the output power. In order to verify the suggested method, computer simulations are performed utilizing a Maximum Power Point Tracking system that is combined with a Single-Ended PrimaryInductor Converter

Design and development of energy management system for solar electric vehicle with dynamic power split approach

ABSTRACT The integration of on-roof solar photovoltaic (PV) systems in electric vehicles (EVs) represents a groundbreaking advancement in the evolution of sustainable transportation. The objective of the research is to design and validate an integrated system for on-roof solar PV integration in EVs, optimising energy utilisation and reducing reliance on grid electricity. This paper explores the implementation of an Adaptive Maximum Power Point Tracking (MPPT) system, which maintains the output of the PV system at a constant level, leading to a significant improvement of approximately 10.20% in energy output compared to conventional MPPT systems. Additionally, a dynamic power split approach (DPAS) is employed to intelligently manage the allocation of power between the solar PV system and the vehicle battery, ensuring continuous fulfilment of energy demand while simultaneously recharging the battery. The proposed system reduces 65% of energy demand from grid and hence, reliance on grid. Furthermore, the dynamic power split approach enable substantial reductions in battery capacity requirements, with a potential decrease of 25–35% observed. These advancements not only enhance the performance and efficiency of EVs but also contribute to cost savings and the transition towards a cleaner and more sustainable transportation ecosystem.

PV Wind Battery Based DC Microgrid with Neural Network MPPT

This Research presents a novel approach to enhancing the performance and efficiency of a PV-wind-battery-based DC microgrid through the integration of a neural network maximum power point tracking (MPPT) system. The proposed system aims to optimize energy harvesting from photovoltaic (PV) panels and wind turbines while efficiently managing energy storage in batteries. By employing neural network algorithms, the MPPT system adapts to varying environmental conditions and load demands, thereby maximizing energy extraction and system stability. Two important RE (Renewable Energy) power sources—photovoltaic cells and wind energy systems—are examined in this technical study under a range of meteorological conditions. Initially, a state-of-the-art intelligent controller system was developed to help monitor the peak power point. For RES, an MPPT (maximum power point tracking) controller is necessary since weather patterns are unpredictable. The purpose of this work is to provide novel ways for power generation using solar and wind energy that are based on MPPT and IDNN (enhanced deep neural network). The integration of a hybrid PV/WES system into microgrids, or MGs, has the potential to lower THD values and enhance power quality. The simulation findings validated that the suggested IDNN system outperforms the current approach in various operating scenarios, as seen by decreased mean square error (MSE) rates, total harmonic distortion (THD), and computational complexity.

Maximum Power Point Tracking of Photovoltaic Generation System Using Improved Quantum-Behavior Particle Swarm Optimization.

This study introduces an improved quantum-behavior particle swarm optimization (IQPSO), tailored for the task of maximum power point tracking (MPPT) within photovoltaic generation systems (PGSs). The power stage of the MPPT system comprises a series of buck-boost converters, while the control stage contains a microprocessor executing the biomimetic algorithm. Leveraging the series buck-boost converter, the MPPT system achieves optimal operation at the maximum power point under both ideal ambient conditions and partial shade conditions (PSCs). The proposed IQPSO addresses the premature convergence issue of QPSO, enhancing tracking accuracy and reducing tracking time by estimating the maximum power point and adjusting the probability distribution. Employing exponential decay, IQPSO facilitates a reduction in tracking time, consequently enhancing convergence efficiency and search capability. Through single-peak experiments, multi-peak experiments, irradiance-changing experiments, and full-day experiments, it is demonstrated that the tracking accuracy and tracking time of IQPSO outperform existing biomimetic algorithms, such as the QPSO, firefly algorithm (FA), and PSO.

Robust power control for PV and battery systems: integrating sliding mode MPPT with dual buck converters

This paper presents a comprehensive exploration of an integrated Buck-Boost converter and Sliding Mode Control (SMC) Maximum Power Point Tracking (MPPT) system for optimizing photovoltaic energy conversion. The study focuses on enhancing solar energy extraction efficiency, regulating output currents, and ensuring effective battery utilization. Through a systematic analysis of converter component sizing and operational modes, the paper delves into the intricacies of the Buck-Boost converter. The unique contribution lies in the innovative integration of SMC with the traditional Perturb and Observe (P&O) algorithm, providing robust and adaptive MPPT under varying environmental conditions. Additionally, the paper introduces a battery management system with three distinct modes, namely, Charging, Direct, and Discharging, offering intelligent control over critical scenarios. Simulation results underscore the robustness of the proposed system under diverse conditions, demonstrating its effectiveness in managing power distribution based on battery charge levels, even in scenarios of insufficient solar power. Overall, this research significantly contributes to advancing the understanding of PV/battery systems and offers a practical, sustainable solution for optimizing energy production, distribution, and storage, marking a substantial stride towards a more efficient and sustainable energy future.

PROGRESSIVE STEP MAXIMUM POWER TRACKER FOR CASCADED INVERTER INDUCTION MOTOR DRIVE

This paper presents the idea of an isolated photovoltaic (PV) based cascaded two-level inverter (CTLI), driving squirrel cage induction motor (SCIM). The connected non-linear load approximately represents a centrifugal pump. The control system of this drive is based on a motor control system along with a progressive step perturb and observe (PSPnO) maximum power point tracking (MPPT) technique for the PV sources towards better utilization of the source. This PSPnO MPPT system intends to operate the SCIM so that the energy developed by PV is utilized entirely through variation of the dc-link voltages of the CTLI under both uncertain variances of non-linear load torque and unpredictable solar irradiance. This PSPnO MPPT algorithm has been engaged in the control scheme for generating reference dc-link voltage. This motor control system is designed to operate in a rotor-flux-oriented rotating reference frame. As for generating CTLI’s switching pulses, the space vector pulse width modulation (SVPWM) technique has been used for its good dynamic response. The whole system is designed to be deployable on a low-computation device. The simulations conducted by Simulink and the results are furnished considering various operational conditions.

Performance Evaluation of Single-Phase Boost-Type Cascaded H-Bridge Inverter in the Applications of Grid-Tied Photovoltaic Systems

In this paper, a new cascaded-H-bridge (CHB) inverter is proposed to enhance the input voltage of a renewable energy source to a desired level without any necessity of a dc-dc converter. The proposed topology consists of a pair of inductor and capacitor that serves as an input impedance network for the two full bridge inverters to boost the source voltage. An interleaved control scheme is proposed for the grid-tied inverter that independently supervises the duty cycle and the modulation index. The maximum power point tracking system commands the duty cycle whereas the dc-link voltage regulator maintains a constant voltage across the capacitors irrespective of the disturbances in the system. The proposed control scheme is primarily investigated in the MATLAB/SIMULINK platform. Different test cases are analysed to explore the performance of the proposed inverter in the applications of photovoltaic systems. An experimental prototype is designed in the laboratory to validate the simulation results.

The Performance of a Solar Panel with Maximum Power Point Tracking (MPPT) System

Due to the rotation of the earth and its spherical shape the availability of sunlight at a certain place is not same. At normal sky condition at a place sunlight is minimum at morning that gradually increase upto 2-3 pm then again tends to minimum. Usually, solar panels are fixed type and can’t absorb the sunlight whole day properly. In that sense, to increase the efficiency of a solar panel with its necessary accessories has been developed where maximum power point tracking (MPPT) system adopted. In this system the panel can easily move to get the maximum sunlight. The electric charges are then store in a battery that are being supplied to the electric appliances. The experimental work has been done at the roof-top of science faculty building of Chittagong University. This experimental study shows that the efficiency of the solar panel starts increasing from morning 9 am with efficiency of 16.64% and found maximum with efficiencies at 12 noon (24.92%); 1 pm (25.94%); and at 2 pm (24.68%) then again starts decreasing with the values of (22.3%) & (18.7%) at 3 and 4 pm respectively. Another, It has also been found that the peak value of average efficiency on July is (24.44%). The achieved efficiencies are more than the efficiency of (15.7%) studied on May-2017 with a fixed-type solar panel at the same place [M. A. Haque et al. 2018].

Numerical Simulation and Mathematical Analysis of Meta Heuristic MPPT System for Solar Photovoltaic Applications Under Non-Linear Operational Conditions

As a sustainable energy source, the use of solar photovoltaic (PV) systems has significantly increased. Environmental elements, especially partial shadowing circumstances, have a considerable impact on how well solar PV systems function. In such cases, the various local maxima in the power-voltage curve make it difficult for conventional Maximum Power Point Tracking (MPPT) methods to maintain peak efficiency. Heuristic Maximum Power Point Tracking (MPPT) system for solar photovoltaic (PV) applications, employing the cuckoo search algorithm. The primary objective of the research is to enhance the efficiency and reliability of solar PV systems by optimizing the MPPT process, which is crucial for maximizing energy extraction under varying environmental conditions. The model is used to simulate the performance of the system under various environmental conditions, such as changes in temperature and irradiance levels. The simulation results are then statistically analyzed to evaluate the effectiveness of the cuckoo search algorithm in tracking the maximum power point accurately and rapidly. The algorithm demonstrates a robust ability to converge to the maximum power point efficiently, thereby enhancing the overall energy yield of the solar PV system.. The performance of the hybrid PSO-CSA MPPT algorithm in contrast to traditional MPPT techniques is assessed through simulations and tests under various shading circumstances. The findings show that the hybrid strategy regularly outperforms conventional methods by enhancing the total energy production of partially shadowed solar PV installations through faster convergence, less oscillations, and greater tracking accuracy. The proposed hybrid algorithm also demonstrates stability and flexibility in real-world settings, making it a potential option for boosting the dependability and efficiency of solar PV systems when shade is present. This study advances the field of renewable energy and prepares the path for the use of sophisticated optimization methods to the problems of solar PV power generation under changing environmental circumstances.

Towards sustainable water pumping systems: Integration of particle swarm optimization and direct torque control PSO-DTC

This research paper focuses on the optimization of solar water pumping systems (SWPS) by combining the particle swarm optimization (PSO) technique on the generator photovoltaic (GPV) side and direct torque control (DTC) on the pump motor side. The integration of a maximum power point tracking system (MPPT-PSO) represents a significant advance, enabling maximum power to be extracted from the GPV whatever the weather conditions. The main objective is to improve the energy efficiency of the SWPS system by maximizing the electrical power dedicated to the pumping system. At the same time, the application of DTC to the pump motor aims to optimize the use of the energy generated by the GPV. This intelligent approach adjusts the motor power according to the power extracted from the GPV, thus avoiding unsuitable profiles for the pumping system. This results in better power quality, longer system life and improved tracking speed, which in turn improves the pump's rotational speed, which in turn improves the flow rate of the water pumped. The results of the study confirm that the combination of PSO-DTC approaches improves the overall performance of the SWPS. These improvements persist under different climatic conditions.

Data-Driven Switch Fault Diagnosis for DC/DC Boost Converters in Photovoltaic Applications

Switch semiconductors are essential components in power electronics stages for photovoltaic (PV) systems, which are prone to failures caused mainly by thermal stress, thereby affecting PV energy production. Specifically, dc/dc boost power converters are commonly used as maximum power point tracking (MPPT) systems. Several works have been reported so far in the literature for switch fault detection and isolation (FDI) purposes in boost power converters. However, most of them require additional sensors and circuitry to those included in any PV system, which generate extra costs on the overall system. Hence, this work presents an efficient and low-cost switch fault diagnosis proposal, which requires only the most common measurements in PV systems, namely, PV current and voltage. This work departs from a data-driven FDI methodology for diagnosing switch open- and short-circuit faults in dc/dc boost converters. The experimental results have been evaluated against sudden irradiance changes and switch faults in a test bench of 350 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\rm {W}$</tex-math></inline-formula> operating under a closed-loop nonlinear control action for MPPT purposes by using the dSpace 1104 board.

Comparison of Solar P&amp;O and FLC-based MPPT Controllers &amp; Analysis under Dynamic Conditions

Increase in electricity generation is caused due to population increase, which leads to the depletion of fossil fuels, and increased pollution. This leads to focusing on alternate renewable energy, mainly solar photovoltaic generation, due to the abundant availability. The maximum power generated by a PV module depends on the temperature and irradiance because the P-V and V-I natures are non-linear. Various DC-DC boost converters are used along with the MPPT techniques because the conversion efficiency of the PV system is low [1][2]. In this paper, comparative analysis between Perturb and Observe (P&amp;O) and Fuzzy Logic-based Maximum Power Point Tracking (MPPT) systems along with modified SEPIC are done using MATLAB/ SIMULINK software. Simulations are done at different irradiations to observe its tracking speed towards MPP. From the obtained output (simulation), it is observed that the Fuzzy Logic Converter (FLC)-based MPPT controllers have good dynamic performance, reduced oscillation, high tracking speed, maximum power, etc...[3].

Battery, Super Capacitor Based Hybrid Energy Storage with PV for Islanded DC Microgrid

Cogitating the present national as well as global context, renewable energy sources are becoming increasingly popular as a source of electricity, where connecting to the utility grid is either impossible or excessively costly. Several trends that have emerged as electrical distribution technology in the twenty-first century will change the requirements for energy transmission. Green power generating methods are receiving more attention due to the world's deteriorating environmental conditions, growing prices, the limited nature of fossil fuels, and rising energy demand. The increasing depletion of fossil fuels and the escalating need for energy has made renewable energy sources a global topic of discussion. The incredible advancement of renewable energy has allowed us to address environmental problems. However, the production from these sources could be more predictable which requires an appropriate energy storage system. The main aim of this research paper is to study the hybrid energy storage with solar photovoltaic for islanded DC Microgrid. This research study simulates, analyzes, and presents an islanded mode DC Microgrid with solar PV, battery, and super capacitor through the use of MATLAB Simulink. The MPPT (Maximum Power Point Tracking) system uses a two-stage DC converter. The regulated Battery Energy Storage System (BESS), coupled through a bidirectional converter, helps to maintain a steady dc bus voltage. The transient and steady-state characteristics of the storage systems are examined. This study demonstrates how quickly hybrid energy storage can adapt to sudden changes in the load.

Long Short Term Memory Tracker‐Based Modified DC‐DC Converter for Power Quality Improvement in Grid‐PV Systems under Uniform and Partial Shading Environments

In previous era, coal and oil like non‐renewable resources were mostly used for power generation, thus causing a severe environmental problem as well as high economic. An intelligent maximum power point tracking (MPPT) system was proposed to overwhelm the above‐mentioned impacts, which tracks the maximum generation of photovoltaic (PV) under various atmospheric conditions. Moreover, a modified DC‐DC converter was introduced to low inrush current and switching oscillation. The proposed model working process was split into three steps: first, design the normal PV with a grid‐integrated power system. Second, create a dataset with the aid of a designed PV model, and finally, design an intelligent MPPT controller based on that dataset. The difference among the MPPT and PV voltage was fed to an optimal controller that generates an appropriate pulse signal of the modified DC‐DC converter. The proposed intelligent tracking system was analyzed under various circumstances like static, dynamic and combined shading conditions. The proposed model offers 99.5% efficiency and very low harmonics values like 0.15% current total harmonic distortion (THD) and 0.46% voltage THD. The outcome verifies that the intelligent MPPT approach performs well than that of the currently used controllers in solar power generation and conditioning systems.

Optimasi PV Array Menggunkan Maximum Power Point Tracking dengan Algoritma FireFly dan Partical Swarm Optimization kondisi Normal dan Partial Shadding

Most solar power plants are not capable of operating independently at maximum capacity, the voltage characteristics of solar energy usually follow the voltage (V) of the storage (battery) or load (load) which is directly connected to solar energy. Of course, the irradiation received by the PV module does not always receive uniform radiation so that the power produced does not always have maximum power and causes double peaks. An MPPT (Maximum Power Point Tracking) system is required to optimize PV output. However, the commonly used methods are often trapped by peaks below the optimal peak and require a long convergence time. This research compares the tracking performance and tracking time of two methods, FF (Firefly Algorithm) and PSO (Particle Swarm Optimization), to find the best performance in partial shadow conditions. The PSO algorithm has an average efficiency of 99.1043% and the FF algorithm has an average efficiency of 99.4073% in 10 trials in each of the 6 cases.

Hybrid Energy Storage with PV for Islanded DC Microgrid

Cogitating the present national as well as global context, renewable energy sources are becoming increasingly popular as a source of electricity in places, where connecting to the utility grid is either impossible or excessively costly. Several trends that have emerged as electrical distribution technology in the twenty-first century will change the requirements for energy transmission. Green power generating methods are receiving more attention due to the world's deteriorating environmental conditions, rowing prices, the limited nature of fossil fuels, and rising energy demand. The increasing depletion of fossil fuels and the escalating need for energy has made renewable energy sources a global topic of discussion. The incredible advancement of renewable energy has allowed us to address environmental problems. However, the production from these sources could be more predictable which requires an appropriate energy storage system. The main aim of this research paper is to study the hybrid energy storage with solar photovoltaic for islanded DC microgrid. This research study simulates, analyzes, and presents an islanded mode DC microgrid with solar PV, battery, and supercapacitor through the use of MATLAB Simulink. The MPPT (Maximum Power Point Tracking) system uses a twin-stage DC converter. The regulated Battery Energy Storage System (BESS), coupled through a bidirectional converter, helps to maintain a steady dc bus voltage. The transient and steady-state characteristics of the storage systems are examined. This study demonstrates how quickly hybrid energy storage can adapt to sudden changes in the load.

PV Sistemlerde Kullanılan Maksimum Güç Noktası İzleme Tekniklerinin İncelenmesi ve Değerlendirilmesi

The issue of improving the efficiency and effectiveness of PV (Photovoltaic) systems remains a concern for researchers and manufacturers who aim to make these systems cost-effective, thereby encouraging their wider adoption. To achieve this goal, increasing the efficiency of the PV generation system by implementing the Maximum Power Point Tracking (MPPT) system has been proposed. Enhancing the energy output from the PV system is considered a crucial aspect of improving efficiency, as it will lead to increased revenue. Consequently, the cost of the generated energy is reduced, approaching that of energy produced by conventional systems based on fossil fuels. This review paper discusses conventional MPPT techniques designed to extract the maximum available power from PV panels operating under uniform environmental conditions. Subsequently, it highlights why these techniques often fail to perform adequately under partial shading conditions. Following this, modern MPPT techniques explicitly designed to operate under non-uniform and partial shading conditions are analyzed.

PENGARUH KONTROLER MAXIMUM POWER POINT TRACKING (MPPT) TERHADAP EFISIENSI DAYA VERTICAL AXIS WIND TURBINE (VAWT) TIPE SAVONIUS DUA SUDU

The purpose of this research is to determine the effect of Maximum Power Point Tracking (MPPT) controller on the power efficiency of a two-bladed Savonius vertical axis wind turbine (VAWT) with a dimensional diameter of 0.9 m and a height of 1 meter, using 1 mm thick stainless steel plate blades. The MPPT controller is applied to the two-bladed Savonius wind turbine to maintain its operation at the maximum power point. The Perturb &amp; Observe type MPPT controller is used in the study to compare the power output of the Savonius wind turbine when the MPPT system is inactive and when it is active, with three variations of load using resistors of 470 ohm, 560 ohm, and 1000 ohm. The research is conducted by subjecting the turbine blades to three variations of wind thrust at speeds of 4.9 m/s, 5.8 m/s, and 6.4 m/s. The testing without using MPPT resulted in an average power of 0.199 Watt, while using MPPT resulted in an average power of 0.620 Watt. The research findings indicate an average power difference of 0.421 Watt, representing an increase of 317.713%. The average power efficiency of the turbine increased by 2.331% compared to the calculated maximum power of the two-bladed Savonius wind turbine. The average power coefficient (Cp) of the two-bladed Savonius wind turbine without MPPT and with MPPT is 0.159% and 0.509%, respectively, indicating a positive effect of MPPT as it led to an average increase in power coefficient of 0.350%.

Tuning the Composition, Crystal Structure and Morphology of Manganese(III/IV) Oxide for High-Power Storage Applications

If inherently intermittent renewable energy technologies are to offer a practical alternative to conventional heat engines, it is critically important that efficient, cost-effective and sustainable energy storage solutions are developed to decouple supply and demand profiles. A promising strategy is to pair the competitive energy densities of batteries with the superior cycling lifetimes and power densities of supercapacitors,[1] employing an appropriate maximum power point tracking (MPPT) system to optimise energy exchange between these complementary devices.[2] Mixed-valence manganese(III/IV) oxides (MnOz, where z ranges between 1.5 and 2.0) have emerged as foremost candidates for the electroactive material in supercapacitor cathodes due to their low toxicity, minimal cost,[3] ready availability (manganese appears at an average concentration of ca. 1,000 ppm in the Earth’s crust[4]), wide electrochemical stability window (ca. 1.0 V in aqueous electrolytes[5]), and high maximum theoretical specific capacitance (ca. 1,370 F g-1, based on the reduction of Mn4+ ions by a single electron[6]). Charge storage in MnOz is predominantly mediated by the pseudocapacitive intercalation of cations from the surrounding electrolyte in each discharge phase, followed by the release of these guest ions during charging.Of the various crystallographic phases of MnOz, cryptomelane (α-MnOz) and birnessite (δ-MnOz) polymorphs are deemed two of the most promising candidates for storage applications because they possess characteristically wide channels for intercalation/de-intercalation:[8,9] α-MnOz consists of ca. 4.6 Å diameter one-dimensional tunnels enclosed between corner-sharing MnO6 octahedra, whereas δ-MnOz comprises two-dimensional layers of MnO6 octahedra separated by ca. 7.0 Å. Herein, a scalable, low-cost synthesis procedure is discussed that enables the production of highly pseudocapacitive birnessite and cryptomelane electrodes with tuneable material characteristics, including particle morphology, crystallographic structure, average Mn oxidation state, and the level of pre-intercalation by Na+ and K+ ions.[10] In addition to comparing the electrochemical properties of these NaxKyMnOz products in aqueous conditions, their application within an asymmetric supercapacitor is showcased: by optimising the relative masses of a NaxKyMnOz cathode and activated carbon anode in a 100 cm2 aqueous full-cell configuration, discharge capacitances in the range 10-30 F g-1 can be achieved over a voltage span of 1.8 V at current densities as high as 10 A g-1, with Coulombic efficiency exceeding 80% up to ca. 20 A g-1. With these competitive performance characteristics, the developed NaxKyMnOz materials are a promising option for high-power supercapacitor cathodes, helping to pave the way towards more efficient and sustainable energy storage.