Dynamic Maximum Power Point Tracking Research Articles

Dynamic PI-PD cascaded MPPT controller for SPV system with battery charging circuit

This paper proposes a dynamic maximum power point tracking (MPPT) controller for a solar photovoltaic (SPV) system with a battery charging circuit. The voltage and current, and consequently the maximum available power of SPV panels vary based on environmental conditions. To operate SPV system at maximum power point under different weather conditions, a cascaded (PI-PD) controller with PSO gain scheduling is suggested in this paper. Also, the FOPI control is applied to an accurate dynamic model of the buck converter to function as a charge controller. For tuning the FOPI controller parameters, a stochastic inertia weight GWO algorithm is employed which maintains an appropriate balance between detection and hunting strategies, and gives the fittest wolf position during iterations. The proposed algorithm is compared with the original GWO algorithm to show its superiority. The accuracy of the proposed cascaded controller used in the SPV system to find MPP ranges from 96.05% to 98.87%. The goal of this study is to operate the SPV panel at maximum power point under variable atmospheric conditions to increase efficiency at a lower cost. It also provides appropriate current and voltage for faster battery charging, thereby increasing the life span of the battery. The system is implemented and analyzed in MATLAB/Simulink, and results are validated.

A novel approach to evaluate dynamic performance for photovoltaic system using software platform

With the growing demand for renewable energy, solar photovoltaic (PV) systems have gained popularity as a reliable source of clean electricity. However, the performance of these systems can be limited by factors such as suboptimal maximum power point tracking (MPPT) algorithms. In order to improve the power generation efficiency of PV systems, it is important to evaluate the performance of dynamic MPPT algorithms that can adapt to varying operating conditions. Traditionally, such evaluations have been time consuming and expensive, often requiring extensive testing and measurement equipment. In this paper, we propose a novel approach to evaluate dynamic MPPT performance very quickly and simply using PSIM software. This approach enables accurate and efficient evaluation of MPPT performance under a wide range of operating conditions, while minimizing the cost and time involved in traditional testing methods. When applying the proposed method to a 3.7 kW inverter using the traditional perturbation and observation (P and O) method, we found that the highest average efficiency was 98.92% at an MPPT control period of 0.1s and a voltage perturbation of 1 V. This evaluation technique provides valuable insights into the design and optimization of more efficient MPPT control algorithms, leading to improved power generation efficiency and increased adoption of solar PV systems.

Conventional and Switched Capacitor Boost Converters for Solar PV Integration: Dynamic MPPT Enhancement and Performance Evaluation

This paper designs two DC-DC converter configurations integrated with solar PV renewable energy resource. Its focuses on comparing two converter topologies: the conventional boost converter and the switched capacitor boost converter. The Perturb and Observe (P&O), Incremental Conductance (INC), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) algorithms are employed to dynamically enhance the Maximum Power Point Tracking (MPPT) performance for both converters. The simulation results demonstrate that both converter topologies, when integrated with appropriate MPPT algorithms, can effectively harvest maximum power from the solar PV. However, the switched capacitor topology converter exhibits advantages in terms of current capabilities and voltage performance. In addition, combing the switched capacitor boost converter with the GA-MPPT algorithm improved the output voltage profile. The switched capacitor topology demonstrates distinct advantages by exhibiting enhanced current control, enabling improved handling of dynamic load changes and varying irradiance conditions. It shows voltage regulation, resulting in reduced output voltage fluctuations and enhanced stability, thereby optimizing energy extraction. The GA-MPPT simulation demonstrates a substantial increase in maximized output current for the switched capacitor boost configuration (70 A) when compared to the conventional type (10 A). The validation and implementation of the system models are carried out using MATLAB/Simulink.

Rapidly Determine the Maximum Power Point in the Parallel Configuration of the Photovoltaic System.

The maximum power point tracking (MPPT) solutions improve power generation efficiency, quickly stabilizing the output waveform of photovoltaic (PV) systems under variable operating conditions. Along with new algorithms, improved and adjusted methods to exploit energy from PV systems are increasingly being researched and proposed. However, the proposed solutions based on the traditional algorithms and their improvements have poor performance, while the advanced algorithms or hybrid methods bring high performance but need to be simplified, and the response speed is higher. Moreover, a suitable PV configuration makes choosing a simple but highly efficient algorithm, especially in low-power PV system applications such as rooftop solar power, traffic lights, and moving vehicles…where the number of PV panels is insufficient to implement flexible configurations. This paper proposes a modified version of the Perturb and Observe (MPO) algorithm to improve MPPT performance and increase convergence speed in the parallel structure of PV panels. The Short-Circuit Current (Isc) and Open-Circuit Voltage (Voc) are calculated directly at specific operating conditions to quickly determine the potential maximum power point (MPP) that will reduce power interruptions and increase power generation efficiency compared to periodic updates. Therefore, the proposed solution converges faster, with higher efficiency, and the output signal in static and dynamic MPPT situations is more stable. The results show that the highest efficiency in simulation and experiment is 99.99% and 99.93%, respectively, while the convergence speed is 0.01 s and 0.03 s, respectively. They are better than the traditional Perturb and Observe (P&O) algorithm, the Variable Step Size Perturb and Observe (VSSP&O) method, and the Particle Swarm Optimization (PSO) technique under the same operating conditions. In addition, its performance and convergence speed are also compared with the latest introduced algorithms. The results show that it is valuable and reliable for parallel PV configuration.

Optimal Dynamic and Steady-State Performance of PV-Interfaced Converters Using Adaptive Observers

The voltage regulation of a grid-tied PV system is constantly perturbed for the maximum power point tracking (MPPT), the transients of which determine the dynamic MPPT efficiency. Meanwhile, the PV-link voltage ripple coupled from the DC to single-phase AC power conversion adversely affects the static MPPT efficiency. Thus, optimal dynamic and steady-state performance is necessary to maximize the solar energy harvest. This brief proposes a novel adaptive mechanism preserving the advantages of two observers: the classical extended state observer and reduced-order generalized proportional integral observer. Simulation and experimental results prove the enhanced performance regarding the overshoot-free fast transient response and steady state against disturbances, even with small input capacitance. Meanwhile, the observer switching happens seamlessly through the proposed mechanism, which is confirmed analytically and experimentally.

Power enhancement using improved maximum power point tracking for solar photovoltaic systems under partial shading

Abstract One of the key reasons contributing to a reduction in the performance of a solar photovoltaic (SPV) system is the presence of partial shade on the solar panels. It is necessary to use maximum power point tracking (MPPT) in SPV systems to get around the non-linear behaviour of photovoltaic panels. This research analyses the reduction in power output of SPV systems caused by partial shading. In the latter part of the paper, the improved dynamic behaviour-based ant colony optimization MPPT approach under the effect of module-level partial shading is implemented in MATLAB®/Simulink®. Various partial shading levels were applied to SPV modules to evaluate the system performance. The proposed system follows the maximum power point with 99.9% accuracy and a response time of 2 ms. The addition of a weight coefficient accelerates the convergence of the algorithm.

Concept of an efficient self-startup voltage converter with dynamic maximum power point tracking for microscale thermoelectric generators

Microscale Thermoelectric Generators (microTEGs) have a high application potential for energy harvesting for autonomous microsystems. In contrast to conventional thermoelectric generators, microTEGs can only supply small output-voltages. Therefore, voltage converters are required to provide supply-voltages that are sufficiently high to power microelectronics. However, for high conversion efficiency, voltage converters need to be optimized for the limited input voltage range and the typically high internal resistance of microTEGs. To overcome the limitations of conventional voltage converters we present an optimized self-startup voltage converter with dynamic maximum power point tracking. The performance potential of our concept is theoretically and experimentally analyzed. The voltage conversion interface demonstrates energy harvesting from open-circuit voltages as low as 30.7 mV, and enables independent and full start-up from 131 mV. No additional external power supply is required at any time during operation. It can be operated with a wide range of internal resistances from 20.6 to − 4 kΩ with a conversation efficiency between η = 68–79%.

Enhanced model based algorithm to reinforce PV system with dynamic MPPT capability

<p>Photovoltaic (PV) power has emerged as the most attractive resource in form of a clean and green energy. However, one major challenge associated with PV interfacing is its intermittent output characteristic which varies dramatically with the operating conditions. Thus designing an effective maximum power point tracking (MPPT) algorithm is a key aspect for optimizing PV system performance. Numerous MPPT algorithms have been proposed earlier having their own specific advantages. However, these are found to have two major limitations which have to be essentially addressed. Firstly, they become ineffective in the dynamic conditions where there is rapid change in environmental parameters like insolation and temperature. Secondly, they fail to discriminate between global and local peaks under partial shading conditions. Therefore, to achieve a reliable and efficient system operation, this paper presents an enhanced model-based (MB) algorithm that overcomes both these deficiencies. Based on new governing equations and precised estimation technique, it predetermines the MPP analytically. First simulated results are obtained where it is tested for dynamic variations of all the three parameters. Then the experimental validation is carried out on a 2 KW installed panel where real time data is recorded through CR1000 data logger and environmental parameters are sensed with elements like pyranometer and humidity sensor. A large number of experimental results are obtained for tracked MPP in the dynamic conditions, which are then summarized in tabular forms. These are finally plotted and compared with simulated results to illustrate the effectiveness of proposed MB algorithm.</p>

Improving dynamic efficiency of photovoltaic generation systems using adaptive type 2 fuzzy‐neural network via EN 50530 test procedure

AbstractIn recent years, photovoltaic generation systems (PGSs) have been fairly popular thanks to the methods proposed for the improvement of the dynamic efficiency. For a more accurate evaluation of the actual dynamic performance in PGSs, a dynamic maximum power point tracking (MPPT) efficiency test based upon an international test procedure such as the European Procedure EN 50530 is of vital importance. The varying gradient irradiance profile in EN 50530 test procedure causes uncertainties in PGSs, which lead to unstable operating conditions for PGSs. It is a challenging task to provide stable operation for PGSs. To overcome this problem, an effective MPPT method based upon adaptive type 2 fuzzy‐neural network (AT2FNN) is proposed using EN 50530 test procedure specifically designed for the dynamic performance of PGSs. The dynamic efficiency performance of the proposed method is analyzed using comparative simulation studies with conventional MPPT methods such as perturb and observe (P&O) and incremental conductance (IC) in Matlab/Simulink environment. The average dynamic efficiency of PGSs is used as performance criteria in the present study. The numerical results obtained from comparative simulation studies indicated that the proposed method improved average dynamic efficiency by almost 4.3% and 9.2% compared to IC and P&O MPPT methods, respectively. The average dynamic efficiency performance of PGS was considerably improved by the proposed method.

An energy harvesting system for time-varying energy transducers with FOCV based dynamic and adaptive MPPT for 30 nW to 4 mW of input power range

This article, for the first time, presents an energy harvesting system (EHS) for harvesting the energy from the continuous time-varying energy harvesters using the fractional open-circuit-voltage (FOCV) based dynamic and adaptive maximum power point tracking (MPPT). The proposed EHS includes an energy harvester, a start-up circuit, a DC-DC boost converter, a DC-DC buck converter, and a control circuit. In the FOCV based MPPT, a fraction of the open-circuit-voltage (OCV) of the energy harvester, is maintained at the output of the energy harvester (input of a DC-DC boost converter). In the proposed work, OCV of the energy harvester is dynamically and adaptively sampled depending on the ambient conditions and the maximum available power from the energy harvester. This helps in improving the tracking efficiency of the proposed MPPT circuit for the time-varying energy harvester by more than 10%. The boost converter switching time period instants are utilized for sampling the OCV of the energy harvester. A 12-stage Dickson based charge pump along with a 5-stage ring oscillator with a modified clock distribution is used for the start-up. Post-layout simulated results of the proposed EHS are presented in the 180 nm mixed-mode CMOS technology. The tracking efficiency and the boost converter efficiency for a wide range of the available input power (30 nW to 4 mW) compatible with many time-varying ambient energy harvesters in both indoor and outdoor places are presented. For a time-varying energy harvester, the tracking efficiency is more than 96%. For a source resistance of 100 kΩ, the boost converter's efficiency is 60.09% and 90.77% at available power of 56 nW and 1.6 μW, respectively.

An Improved MPPT Method with Dynamic Voltage Error Compensation under Two Varying External Conditions

This paper presents a new maximum power point tracking (MPPT) method which can improve the dynamic tracking ability under two varying external conditions, rapid irradiance variation and grid voltage fluctuation. Different from irradiance variation, the dynamic error of tracking voltage caused by grid voltage fluctuation is usually ignored by MPPT methods. The dynamic error of tracking voltage can make the unexpected power change of photovoltaic system and influence the dynamic MPPT efficiency. According to the different current increment changes under the irradiance variation and the grid voltage fluctuation, the reasons of dynamic voltage errors are analyzed and distinguished. And two corresponding modes of variable step size are designed in the tracking algorithm to regulate the voltage deviation from MPPs. The effectiveness and the improved dynamic tracking performance of the proposed MPPT method are verified by simulation results under the above two varying external conditions.

A dynamic particles MPPT method for photovoltaic systems under partial shading conditions

Combining with cuckoo search (CS) algorithm, this paper proposes a novel dynamic particles maximum power point tracking (MPPT) method to track global maximum power point (GMPP) for photovoltaic (PV) systems under partial shading conditions (PSCs). Based on the investigation of the transient characteristic of the boost converter, the concept of dynamic sample time is introduced and deployed to decrease system tracking time with a fuzzy positioning mechanism. In the proposed two-mode searching strategy, numbers of particles, which are gradually abandoned through several iterations in global mode, rapidly position the area near the GMPP. Then the local mode is activated to accurately track the GMPP. These implements allow traversal search of numbers of initial particles in overall area to handle random shading patterns of PV arrays with fewer iterations and less sample time compared to conventional optimization algorithm based MPPT techniques. Thus, the success rate of tracking GMPP is maximized with shorter tracking time and lower tracking power loss under PSCs. Comprehensive simulations and experiments are performed to verify the better effectiveness of the proposed dynamic particles MPPT method in comparison to other existing advanced optimization algorithms based MPPT techniques in PV systems under PSCs.

Dynamic MPPT Controller Using Cascade Neural Network for a Wind Power Conversion System with Energy Management

ABSTRACT This paper proposes a dynamic maximum power point tracking (MPPT) controller for a permanent magnet synchronous generator (PMSG)-based variable speed autonomous wind power conversion system (WPCS) with an energy storage system (ESS). The dynamic controller extracts the maximum power from the WPCS under variable speed conditions using a cascade neural network (CNN)-based MPPT algorithm. Also, the efficient power management between an ESS, load and WPCS is obtained using a conventional PI controller. The CNN is trained and tested using simulation and experimental data, respectively. The performance of CNN-based MPPT is also compared with feed-forward-based MPPT in terms of accuracy and complexity. This paper also discusses FPGA implementation aspects of CNN-based MPPT and hardware resources management for desired accuracy. The proposed method is first implemented on a simulation model 900-watt PMSG-based WECS and results are validated. Further, the enthusiastic results were finally evaluated in a hardware setup of 1 HP PMSG-based WECS. The proposed method is shown to extract maximum power with a simple structure and provide a better response to variable wind speed.

Numerical Experimental Validation of a Proposed MPPT Algorithm with Dynamic Hysteresis for PV Systems

In the last decades, the renewable energies have had a highly positive effect on the daily life. This due to the necessity of reducing the common fuel energies that have negatively impacted the environment. Among all the available renewable energies, the solar energy is one of the most common. This energy is obtained through Photovoltaic (PV) systems. Thus, in previous years, new techniques of Maximum Power Point Tracking (MPPT) to raise the quality of the energy provided by PV panels have been proposed. Since the energy provided by the PV cells depends on the external environmental conditions, as temperature or sun irradiance, the MPPT methods should be adequate to deal with these external changes by maintaining the desired power level. Hence, this paper proposes a recent hysteretic dynamic technique to extract the maximum power from a PV panel array by employing a Boost DC/DC converter to supply energy to an inductive load. Here, a comparative study between the results obtained with the well known Perturb and Observer (P&O) algorithm and by using our dynamic hysteretic MPPT method is analyzed, specifically when the PV panel is submitted to fast variations in temperature and irradiance. It will be proved through numerical experiments realized in MatLab/Simulink that our hysteretic MPPT algorithm provides a better achievement of the maximum power of the PV panel in comparison to the conventional Perturb and Observer method.

Application of Particle Swarm Optimization to Dynamic Maximum Power Point Tracking

Application of Particle Swarm Optimization to Dynamic Maximum Power Point Tracking

일사량 변화를 고려한 개선된 MPPT 알고리즘

The MPPT(Maximum Power Point Tracking) techniques are employed in photovoltaic (PV) systems to maximize the PV array output power which depends on solar irradiance and temperature. The dynamic MPPT performance under varying irradiance conditions affects the impact on overall PV system performance. This paper presents the improved MPPT algorithm by the simulation comparison with other algorithms. The simulation models are made by the Matlab & Simulink. The result of simulation, the dynamic MPPT efficiency of proposed algorithm is higher than the other algorithms.

Novel fast dynamic MPPT (maximum power point tracking) technique with the capability of very high accurate power tracking

In this paper, a novel maximum power point tracking (MPPT) technique is proposed to track the maximum power point (MPP) of photovoltaic (PV) systems. The proposed MPPT scheme concurrently uses PV voltage and current deviations to find the MPP of a PV module/array/panel under every condition. A PV system has been built to implement the proposed MPPT technique as a MPPT controller, and experimental results obtained under standard test condition (STC), uniform and non-uniform shading conditions, and partial shading conditions are presented that explicitly validate theoretical results. A comparative study together with real experimental results obtained under STC verify that the proposed MPPT technique has a dynamic response with the shortest convergence time of 12 ms and the highest MPPT efficiency more than 99.6% compared to other MPPT techniques. Static-dynamic response obtained experimentally under uniform and non-uniform shading conditions again verifies that the method has a very fast high accurate dynamic response with the MPPT efficiency more than 99.6%. It is also experimentally shown that the proposed technique high accurately tracks the global maximum power point (GMPP) under partial shading conditions, so that, the MPPT efficiency is again more than 99.6%, and the convergence time is less than 140 ms.

Dynamic Performance of Maximum Power Point Trackers in TEG Systems Under Rapidly Changing Temperature Conditions

Characterization of thermoelectric generators (TEG) is widely discussed and equipment has been built that can perform such analysis. One method is often used to perform such characterization: constant temperature with variable thermal power input. Maximum power point tracking (MPPT) methods for TEG systems are mostly tested under steady-state conditions for different constant input temperatures. However, for most TEG applications, the input temperature gradient changes, exposing the MPPT to variable tracking conditions. An example is the exhaust pipe on hybrid vehicles, for which, because of the intermittent operation of the internal combustion engine, the TEG and its MPPT controller are exposed to a cyclic temperature profile. Furthermore, there are no guidelines on how fast the MPPT must be under such dynamic conditions. In the work discussed in this paper, temperature gradients for TEG integrated in several applications were evaluated; the results showed temperature variation up to 5°C/s for TEG systems. Electrical characterization of a calcium–manganese oxide TEG was performed at steady-state for different input temperatures and a maximum temperature of 401°C. By using electrical data from characterization of the oxide module, a solar array simulator was emulated to perform as a TEG. A trapezoidal temperature profile with different gradients was used on the TEG simulator to evaluate the dynamic MPPT efficiency. It is known that the perturb and observe (P&O) algorithm may have difficulty accurately tracking under rapidly changing conditions. To solve this problem, a compromise must be found between the magnitude of the increment and the sampling frequency of the control algorithm. The standard P&O performance was evaluated experimentally by using different temperature gradients for different MPPT sampling frequencies, and efficiency values are provided for all cases. The results showed that a tracking speed of 2.5 Hz can be successfully implemented on a TEG system to provide ∼95% MPPT efficiency when the input temperature is changing at 5°C/s.

A comparative study on maximum power point tracking techniques for photovoltaic generation systems operating under fast changing environments

Perturb and observe (P&O) maximum power point tracking (MPPT) algorithms are commonly used in photovoltaic (PV) generation system (PGS) due to its ease of implementation. However, conventional fixed step-size P&O exhibits a trade-off between tracking speed and tracking accuracy. By contrast, variable step-size MPPT can achieve satisfactory tracking accuracy and speed while maintains advantage of simple calculation. This paper aimed to study the performance of different variable step-size P&O MPPT techniques. Initially, a MATLAB based PGS model is developed and validated. Then, variable step-size MPPT methods are compared in terms of energy captured, rise time, settling time and steady-state tracking accuracy under various operating conditions. Performance indices are also proposed in this paper to improve the reliability of the comparison. Finally, the response of these MPPT techniques is evaluated in rapidly changing weather conditions using international standard EN50530 to study the dynamic MPPT efficiency of each method. The pros and cons of each method are then summarized and relevant design suggestions are proposed.

High performance monolithic power management system with dynamic maximum power point tracking for microbial fuel cells.

Microbial fuel cell (MFC) that can directly generate electricity from organic waste or biomass is a promising renewable and clean technology. However, low power and low voltage output of MFCs typically do not allow directly operating most electrical applications, whether it is supplementing electricity to wastewater treatment plants or for powering autonomous wireless sensor networks. Power management systems (PMSs) can overcome this limitation by boosting the MFC output voltage and managing the power for maximum efficiency. We present a monolithic low-power-consuming PMS integrated circuit (IC) chip capable of dynamic maximum power point tracking (MPPT) to maximize the extracted power from MFCs, regardless of the power and voltage fluctuations from MFCs over time. The proposed PMS continuously detects the maximum power point (MPP) of the MFC and matches the load impedance of the PMS for maximum efficiency. The system also operates autonomously by directly drawing power from the MFC itself without any external power. The overall system efficiency, defined as the ratio between input energy from the MFC and output energy stored into the supercapacitor of the PMS, was 30%. As a demonstration, the PMS connected to a 240 mL two-chamber MFC (generating 0.4 V and 512 μW at MPP) successfully powered a wireless temperature sensor that requires a voltage of 2.5 V and consumes power of 85 mW each time it transmit the sensor data, and successfully transmitted a sensor reading every 7.5 min. The PMS also efficiently managed the power output of a lower-power producing MFC, demonstrating that the PMS works efficiently at various MFC power output level.