Adaptive Maximum Power Point Tracking Research Articles

Smart energy harvesting: coupled inductor-based HGISB converter integration with adaptive InCond MPPT for optimal grid performance

Smart energy harvesting: coupled inductor-based HGISB converter integration with adaptive InCond MPPT for optimal grid performance

Design of adaptive hybrid MPPT controllers with universal input voltage DC–DC converter for RES’s

At present, conventional energy production is absent because of the more hazardous gases released into the environment, the high effect on human health, more cost required for maintenance, plus less usefulness for highly populated areas. So, the Renewable Energy Sources are more focused for the present automotive industry application. In this work, the Proton Exchange Membrane Fuel Stack is considered for analyzing the proposed DC–DC converter circuit. The advantages of this fuel stack are high energy density, fast functioning nature, more robustness, and more usefulness for the various water membrane conditions of the fuel stack. However, the disadvantages of the fuel stack are excessive current generation, plus more current conduction losses. So, the wide voltage supply single switch power converter is introduced in this work for optimizing the current production of the fuel stack network. The merits of this converter circuit are high stability, good reliability, low voltage appearing across the switches, plus a uniform power supply. Here, the converter switching pulses are obtained by proposing the Modified Continuous Step Change Adaptive Fuzzy Logic with Grey Wolf Optimization hybrid controller. This controller provides high maximum power extraction efficiency from the fuel stack which is equal to 99.421%. Also, this controller's Maximum Power Point Tracking time is 0.0285 s.

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.

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.

Performance Evaluation of Incremental Conductance and Adaptive HCS MPPT Algorithms for WECS

Performance Evaluation of Incremental Conductance and Adaptive HCS MPPT Algorithms for WECS

Adaptive Hybrid MPPT Using Artificial Intelligence for an Autonomous PV System

Adaptive Hybrid MPPT Using Artificial Intelligence for an Autonomous PV System

A new adaptive MPPT technique using an improved INC algorithm supported by fuzzy self-tuning controller for a grid-linked photovoltaic system.

Solar energy, a prominent renewable resource, relies on photovoltaic systems (PVS) to capture energy efficiently. The challenge lies in maximizing power generation, which fluctuates due to changing environmental conditions like irradiance and temperature. Maximum Power Point Tracking (MPPT) techniques have been developed to optimize PVS output. Among these, the incremental conductance (INC) method is widely recognized. However, adapting INC to varying environmental conditions remains a challenge. This study introduces an innovative approach to adaptive MPPT for grid-connected PVS, enhancing classical INC by integrating a PID controller updated through a fuzzy self-tuning controller (INC-FST). INC-FST dynamically regulates the boost converter signal, connecting the PVS's DC output to the grid-connected inverter. A comprehensive evaluation, comparing the proposed adaptive MPPT technique (INC-FST) with conventional MPPT methods such as INC, Perturb & Observe (P&O), and INC Fuzzy Logic (INC-FL), was conducted. Metrics assessed include current, voltage, efficiency, power, and DC bus voltage under different climate scenarios. The proposed MPPT-INC-FST algorithm demonstrated superior efficiency, achieving 99.80%, 99.76%, and 99.73% for three distinct climate scenarios. Furthermore, the comparative analysis highlighted its precision in terms of control indices, minimizing overshoot, reducing rise time, and maximizing PVS power output.

Hybrid two‐stage adaptive maximum power point tracking for stand‐alone, grid integration, and partial shaded PV system

SummaryIn a PV array, the power‐voltage characteristic (P‐V) and output power are completely dependent on atmospheric conditions. Consequently, the P‐V curve's maximum power point (MPP) likewise changes significantly. This abrupt oscillation in the MPP may be caused by the PV panel being uniformly or partially shaded by trees, buildings, raindrops, and clouds. However, in both circumstances, the maximum power point tracking (MPPT) turns into a highly nonlinear issue with a time‐bounded solution. The unpredictable weather circumstances change P‐V characteristics after every short time interval. Therefore, this research presents a hybrid two‐stage adaptive MPPT to overcome the environmental uncertainties. The first stage is MPPT control block for determining reference voltage for each MPP and the second stage is modified model reference adaptive controller (MMRAC) block fine‐tunes converter duty cycle to maintain MPP of PV panel. The probabilistic evaluation is executed through four distinct levels of uncertainty, including stand‐alone, partially‐shaded, grid integration, and OPAL‐RT to examine the system robustness in diverse scenario. The possibility of the tracking failure caused by dynamic irradiance change is also verified. Moreover, efficiency, tracking ability, power loss, ripple, and error rate are compared with the state‐of‐the‐art strategies, that is, ANFIS, INC, P&O, and VSPO. The satisfactory performances of the new strategy under simultaneously varying irradiance, temperature, and load; partially shaded and grid integration shows the supremacy.

Reduction in Residential Electricity Bill and Carbon Dioxide Emission through Renewable Energy Integration Using an Adaptive Feed-Forward Neural Network System and MPPT Technique

Increasing electricity demand and the emergence of smart grids have given home energy management systems new potential. This research investigates the use of an artificial neural network algorithm for a home energy management system. The system keeps track of and organizes the use of electrical appliances in a typical home with the objective of lowering consumer electricity bills. An artificial-neural-network-based maximum-power-point-tracking scheme is applied to maximize power generation from photovoltaic sources. The proposed neural network senses solar energy and calculates load requirements to switch between solar and grid sources effectively. The implementation of improved source utility does not require numerical calculations. Traditional relational operator techniques and fuzzy logic controllers are compared with the suggested neural network. The model is simulated in MATLAB, and the results show that the artificial neural network performs better in terms of source switching following load demand, with an operating time of less than 2 s and a reduced error of 0.05%. The suggested strategy reduces electricity costs without affecting consumer satisfaction and contributes to environmental friendliness by reducing CO2 emissions.

Hybrid Neural Network and Adaptive Terminal Sliding Mode MPPT Controller for Partially Shaded Standalone PV Systems

Hybrid Neural Network and Adaptive Terminal Sliding Mode MPPT Controller for Partially Shaded Standalone PV Systems

Adaptive energy harvesting approach for smart wearables towards human-induced stochastic oscillations

Harvesting diverse renewable energy sources from humans offers an effective approach to achieving cleaner production and sustainable development. The integration of self-powered smart wearables enables continuous power supply for long-term operation of outdoor civilian and military electronics, especially in situations with limited access to conventional power sources. Resonant energy harvesting schemes working at the dominant frequencies of human movement can trigger large mechanical oscillations and significantly enhance the generation of electrical power. This study presents the development of a nonlinear two-degree-of-freedom (2DOF) resonant energy harvesting system with a maximum power point tracking (MPPT) algorithm that adapts to individual human differences and stochastic motion patterns. Through an analysis of the system characteristics based on equivalent circuit modeling, it is found that the electrical damping of the harvester plays a crucial role in determining the actual power output, and the relationship between its optimal value and the oscillation frequency is unique. Thereby, an energy harvesting circuit with an adaptive MPPT algorithm is developed to optimize the electrical damping in real time by applying a novel Perturb and Observe technique based on the average power over each time period. In addition, a variable-step search method and a special convergence judgment are proposed to enhance the tracking speed. The sensitivity analysis of algorithm parameters indicates that the time period and power tolerance need to be reasonably selected to strike a balance between search speed and accuracy. The results of constant motion pattern tests show that the proposed algorithm with a variable-step search method exhibits superior search speed and accuracy over fixed-step configurations. The results of variable motion pattern tests reveal an increase in the actual system power output of up to 194% compared to the conventional fractional open-circuit voltage (FOCV) algorithm. Finally, the system adaptability test is conducted to validate the MPPT efficiency in four resonant systems. The results showcase an impressive MPPT efficiency of 93%–99%, demonstrating its reliability and robustness in various application scenarios.

A Compact and Efficient Boost Converter in a 28 nm CMOS with 90 mV Self-Startup and Maximum Output Voltage Tracking ZCS for Thermoelectric Energy Harvesting

There are increasing demands for the Internet of Things (IoT), wearable electronics, and medical implants. Wearable devices provide various important daily applications by monitoring real-life human activities. They demand low-cost autonomous operation in a miniaturized form factor, which is challenging to realize using a rechargeable battery. One promising energy source is thermoelectric generators (TEGs), considered the only way to generate a small amount of electric power for the autonomous operation of wearable devices. In this work, we propose a compact and efficient converter system for energy harvesting from TEGs. The system consists of an 83.7% efficient boost converter and a 90 mV self-startup, sharing a single inductor. Innovated techniques are applied to adaptive maximum power point tracking (A-MPPT) and indirect zero current switching (I-ZCS) controllers for efficient operation. The startup circuit is realized using a gain-boosted tri-state buffer, which achieves 69.8% improved gain at the input VIN = 200 mV compared to the conventional approach. To extract the maximum power, we use an A-MPPT controller based on a simple capacitive divider, achieving 95.2% tracking efficiency. To address the challenge of realizing accurate voltage or current sensors, we propose an I-ZCS controller based on a new concept of maximum output voltage tracking (MOVT). The integrated circuit (IC) is fabricated using a 28 nm CMOS in a compact chip area of 0.03 mm2. The compact size, which has not been obtained with previous designs, is suitable for wearable device applications. Measured results show successful startup operation at an ultralow input, VIN = 90 mV. A peak conversion efficiency of 85.9% is achieved for the output of 1.07 mW.

Dynamic and Adaptive Maximum Power Point Tracking Using Sequential Monte Carlo Algorithm for Photovoltaic System

Dynamic and Adaptive Maximum Power Point Tracking Using Sequential Monte Carlo Algorithm for Photovoltaic System

Adaptive LMS MPPT Controller and Adaptive Inverter Control Law to Control the Solar Photovoltaic System

Adaptive LMS MPPT Controller and Adaptive Inverter Control Law to Control the Solar Photovoltaic System

Adaptive maximum power point tracking based on Kalman filter for hydrogen fuel cell in hybrid unmanned aerial vehicle applications

In this paper, an adaptive real-time estimation method based on Kalman filter is proposed for tracking the maximum power point (MPP) of a hydrogen fuel cell (FC) in hybrid unmanned aerial vehicle (UAV) applications. To achieve the adaptive MPP tracking (MPPT), a mathematical model for the hydrogen FC is established. Then, the recursive least square method is employed to identify the initial values of model parameters. On this basis, the MPP of the hydrogen FC under steady conditions can be derived. Furthermore, the state and observation equations based on Kalman filter are introduced to adaptively estimate the model parameters in real-time. Moreover, the real-time model parameters would be used to optimize the MPP in accordance with the operating conditions such that the adaptive MPPT can be achieved. Finally, various simulations and experiments are conducted to verify the effectiveness and accuracy of the adaptive MPPT for the hydrogen FC in hybrid UAV applications. Results show that the adaptive MPPT can not only track the MPP accurately in real-time, but also reduce the oscillation of the hydrogen FC. Compared with the MPPT methods based on perturb and observe (P&O) and particle swarm optimization (PSO), the maximum power tracking error of the adaptive MPPT can be improved by 2.83% and 1.10%, respectively.

Elektrikli Araç Şarj İstasyonuna Dayalı Bir Solar FV-Pil Tasarlamak

Increasing transport demand necessitates higher oil consumption, resulting in an increase in carbon dioxide (CO2) emissions, which is a major cause of air pollution. The use of electric vehicles (EVs) is becoming more common around the world. Recent advancements in lithium-ion battery technology have increased the improvement of EVs. In this work, a solar photovoltaic (PV) battery-based EV charging station is designed. Meanwhile, the overall system comprises a battery energy storage system (BESS), solar PV module, grid and EV charging station. Thus, the primary source for the charging station is the PV source but due to less power during the night, we included battery storage as a backup. Grid source is also recommendable for an uninterruptable power supply. An artificial neural network strategy is developed in MATLAB/Simulink for proper power management of the solar PV-battery based EV charging station connected to the AC grid. Moreover, by employing an adaptive neuro-fuzzy inference system (ANFIS) and PI controller-based MPPT, the grid voltage and current, real/reactive grid power and the maximum output power are obtained. The overall system is evaluated under different scenarios of irradiance level and temperature with a state of charge (SOC) greater than 10 % for simulation purposes. The result shows that during the night hour due to less power from the PV source, an artificial neural network begins to regulate the grid power so that it supplies power to the stationary storage and EV battery.

Adaptive Maximum Power Point Tracking Control Algorithm for Wind Energy Conversion Systems

For small-scale wind energy conversion systems (WECSs), an adaptive maximum power point tracking (MPPT) algorithm is presented in this paper to increase wind energy harvesting. For its adaptability across a wide range of WECSs and quick tracking of maximum power point, the proposed algorithm combines the computational behavior of hill climb search, tip speed ratio, and power signal feedback control algorithms. Using buck–boost-featured single-ended primary inductor converters, the proposed MPPT algorithm is implemented in this paper to extract maximum power from the entire wind velocity profile. A laboratory-scaled dc motor drive-based WECS emulator is used to test the proposed algorithm. The in-lab experimental setup's control schemes are carried out with the help of a 32-bit floating point digital signal controller called TMS320F28335. The experimental results demonstrate that the proposed algorithm's tracking capability is effective and efficient in both sudden and gradual wind conditions.

Implementation of a novel robust model reference adaptive controller-based MPPT for stand-alone and grid-connected photovoltaic system

ABSTRACT The fast advancement of photovoltaic (PV) power generation technologies led to the integration of solar-based generation systems into several modes of transportation, including aircraft, automobiles, and trains. In these PV systems, solar irradiation levels fluctuate significantly and invalidate specific maximum power point (MPP) tracking (MPPT) control strategies, lowering the energy conversion efficiency. It is evident that the new robust model reference adaptive control (MRAC) proposed in this paper alleviates these problems by reducing tracking direction loss and oscillations near MPP. To evaluate the proposed controller’s performance, MATLAB/Simulink is utilized and compared with well-known techniques (P&O, VSPO, INC, ANFIS, and swarm-based MPPT) under three different modes, i.e. stand-alone, grid-connected, and real-time mode. The proposed scheme has tracking efficacy lies between 99.02% and 99.96% and achieved MPP in just 4 msec under highly fluctuating radiation and temperature conditions. It takes only 0.08 sec to capture the global MPP, which is on average two times faster than swarm-based global MPPT methods. Furthermore, the effectiveness is tested in a 50 kW three-phase grid-connected mode with and without cloud effects in realistic weather situations. Finally, real-time validation on the OPAL-RT simulator demonstrates the proposed technique’s practicality in real-world applications.

Photovoltaic MPPT algorithm based on adaptive particle swarm optimization neural-fuzzy control

Since the BP neural network has poor performance and unstable learning rate in the maximum power point tracking (MPPT) algorithm of photovoltaic (PV) system, an adaptive particle swarm optimization BP neural network-fuzzy control PV MPPT algorithm (APSO-BP-FLC) is proposed in this paper. First, the inertia weight, learning factor and acceleration factor of particle swarm optimization (PSO) are self-updating, and the mutation operator is adopted to initialize the position of each particle. Second, the APSO algorithm is used to update the optimal weight threshold of BP neural network, where the input layer is irradiation and temperature, and the output layer is the maximum power point (MPP) voltage. Third, the fuzzy logical control (FLC) is employed to adjust the duty cycle of Boost converter. The inputs of FLC are voltage difference and duty ratio D(n-1) at the previous time, and the output is duty ratio D(n). Moreover, D(n-1) is optimized by |dP/dU| to improve the search range of FLC. The irradiation, temperature and MPP voltage of PV cell are adopted as the datasets for simulation in a city in Shaanxi province, China. Simulation results show that the proposed MPPT algorithm is superior to the APSO-BP, FLC and perturbation and observation (P&O) algorithm with tracking performance, steady state oscillation rate and efficiency. In addition, the efficiency of proposed MPPT algorithm is improved by 0.37%, 6.2%, and 6.8% as compared to APSO-BP, FLC and P&O algorithm.

Design and implementation of a new adaptive MPPT controller for solar PV systems

This research provides an adaptive control design in a photovoltaic system (PV) for maximum power point tracking (MPPT). In the PV system, MPPT strategies are used to deliver the maximum available power to the load under solar radiation and atmospheric temperature changes. This article presents a new adaptive control framework to enhance the performance of MPPT, which will minimize the complexity in system control and efficiently manage uncertainties and disruptions in the environment and PV system. Here, the MPPT algorithm is decoupled with model reference adaptive control (MRAC) techniques, and the system gains MPPT with overall system stability. The simulation and design of the new MRAC for MPPT based on a boost converter are addressed here. Moreover, a mathematical model is formulated and an efficient MRAC is designed for MPPT. To validate the robustness of the controller, MATLAB/Simulink is utilized to compare with the state-of-the-art approach, which is incremental conductance (INC) and perturb & observe (P&O) under various operating conditions based on the convergence time, tracking efficiency, PV current & voltage ripple, overall efficiency, and error rates. The proposed controller’s average tracking efficiency is 99.77% and 99.69% under diverse temperature and radiation conditions, respectively. In addition, it takes only 3.6 msec to capture MPP, which is around ten times faster than INC and twelve times faster than the P&O approach. When compared to INC and P&O, the MPP error rates in the MRAC-MPPT scheme are significantly lower. The simulation outcomes indicate that the presented controller exhibits excellent tracking under varying circumstances like solar radiation and temperature.