Design Of Maximum Power Point Tracker Research Articles

A novel MPPT design for a wind energy conversion system using grey wolf optimization

A significant problem is enhancing the reliability of the wind energy conversion system (WECS), when that runs in unpredictable weather. Therefore, it is essential to construct a maximum power point tracker (MPPT), a controller for measuring the optimum power that the WECS is expected to generate. Hill climbing-based techniques were used to simulate the tracker, but they had drawbacks in terms of tracking efficiency and speed. The Grey Wolf optimization algorithm (GWO) for modelling MPPT integrated with the WECS is proposed in this work as a novel, effective method. The system is made up of a wind turbine (WT) conjoined to a permanent magnet synchronous generator (PMSG), a 3-phase rectifier that converts the generator’s AC output power to direct current (DC), and a boost converter whose input DC voltage is controlled by the MOSFET duty cycle. The goal of the modelling procedure is the system’s electrical output power, which is presented as an optimization problem. The results confirmed the GWO-reliability MPPT’s in reaching the desired WECS performance.

High time resolution measurement of solar irradiance onto driving car body for vehicle integrated photovoltaics

AbstractVehicle integrated photovoltaic (VIPV) systems have much different requirements on maximum power tracking compared to stationary setups. The occurrence of fast changes between full irradiance and shading are demanding. To evaluate the specific impact of these conditions on the specifications of VIPV systems, we conduct high resolution measurements of the incident irradiance onto a car body while driving. We investigate the influence of environmental conditions like weather, season and building density in an urban environment on measured irradiance on the roof and the sides of a vehicle. We find that weather conditions have the highest impact on the measured irradiance on the roof, while the relative irradiance on the side depends more heavily on the season. We also find that changes in irradiance occur predominantly at frequencies below 1 Hz, but changes with 100 Hz or more can occur in certain situations, with a tendency toward higher frequencies for sunny weather. This must be considered in maximum power point tracker design.

Fuzzy Rule-Based and Particle Swarm Optimisation MPPT Techniques for a Fuel Cell Stack

The negative environmental impact and the rapidly declining reserve of fossil fuel-based energy sources for electricity generation is a big challenge to finding sustainable alternatives. This scenario is complicated by the ever-increasing world population growth demanding a higher standard of living. A fuel cell system is able to generate electricity and water with higher energy efficiency while producing near-zero emissions. A common fuel cell stack displays a nonlinear power characteristic as a result of internal limitations and operating parameters such as temperature, hydrogen and oxygen partial pressures and humidity levels, leading to a reduced overall system performance. It is therefore important to extract as much power as possible from the stack, thus hindering excessive fuel use. This study considers and compares two Maximum Power Point Tracking (MPPT) approaches; one based on the Mamdani Fuzzy Inference System and the other on the Particle Swarm Optimisation (PSO) algorithm to maintain the output power of a fuel cell stack extremely close to its maximum. To ensure that, the power converter interfaced to the fuel cell unit must be able to continuously self-modify its parameters, hence changing its voltage and current depending upon the Maximum Power Point position. While various methods exist for Maximum Power Point tracker design, this paper analyses the response characteristics of a Mamdani Fuzzy Inference Engine and the Particle Swarm Optimisation technique. The investigation was conducted on a 53 kW Proton Exchange Membrane Fuel Cell interfaced to a DC-to-DC boost converter supplying 1.2 kV from a 625 V input DC voltage. The modelling was accomplished using a Matlab/Simulink environment. The results showed that the MPPT controller based on the PSO algorithm presented better tracking efficiency as compared to the Mamdani controller. Furthermore, the rise time of the PSO controller was slightly shorter than the Mamdani controller and the overshoot of the PSO controller was 2% lower than that of the Mamdani controller.

Designing Localized MPPT for PV Systems Using Fuzzy-Weighted Extreme Learning Machine

A maximum power point tracker (MPPT) should be designed to deal with various weather conditions, which are different from region to region. Customization is an important step for achieving the highest solar energy harvest. The latest development of modern machine learning provides the possibility to classify the weather types automatically and, consequently, assist localized MPPT design. In this study, a localized MPPT algorithm is developed, which is supported by a supervised weather-type classification system. Two classical machine learning technologies are employed and compared, namely, the support vector machine (SVM) and extreme learning machine (ELM). The simulation results show the outperformance of the proposed method in comparison with the traditional MPPT design.

MPPT Design Using PSO Technique for Photovoltaic System Control Comparing to Fuzzy Logic and P&O Controllers

The Maximum Power Point Tracker (MPPT) is the optimum operating point of a photovoltaic module. It plays a very important role to obtain the maximum power of a solar panel as it allows an optimal use of a photovoltaic system, regardless of irradiation and temperature variations. In this research, we present a novel technique to improve the control’s performances optimization of the system consisting of a photovoltaic panel, a buck converter and a load. Simulations of different parts of the system are developed under Matlab/Simulink, thus allowing a comparison between the performances of the three studied controllers: “Fuzzy TS”, “P&O” and “PSO”. The three algorithms of MPPT associated with these techniques are tested in different meteorological conditions. The obtained results, in different operating conditions, reveal a clear improvement of controlling performances of MPPT of a photovoltaic system when the PSO tracking technique is used.

Performance evaluation of modified perturb & observe maximum power point tracker for solar PV system

Solar photovoltaic (PV) is used to convert the solar energy (a major source of renewable energy) into unregulated electrical energy. Maximum power point tracker (MPPT) is used to extract the maximum power from solar PV. Among various types of MPPT schemes, perturb & observe (P&O) scheme is one of the most popular method. The study aims at to develop a modified P&O MPPT scheme and to evaluate the performance parameters such as convergence time, steady state oscillations, and reliability under change in ambient conditions as well as load variations. In the modified MPPT design, the response time of the power electronics DC–DC converter is considered for deciding the interval between two perturbations. The simulation study is performed using PSIM software and the prototype hardware is developed in the laboratory. The modified MPPT scheme is implemented using buck DC–DC converter. The results show the convergence time of 0.16 s and steady state oscillation of 1 W with step load change response time of 1.8 s. The comparison of findings obtained in the modified P&O MPPT scheme and existing methods showed significant improvements. The modified P&O MPPT scheme may be useful for home applications, in DC microgrids.

An Efficient Partial Power Processing DC/DC Converter for Distributed PV Architectures

In this paper, a dc/dc power converter for distributed photovoltaic (PV) plant architectures is presented. The proposed converter has the advantages of simplicity, high efficiency, and low cost. High efficiency is achieved by having a portion of the input PV power directly fed forward to the output without being processed by the converter. The operation of this converter allows for a simplified maximum power point tracker design using fewer measurements. The stability analysis of the distributed PV system comprised of the proposed dc/dc converters confirms the stable operation even with a large number of deployed converters. The experimental results show a composite weighted efficiency of 98.22% with very high maximum power point tracking efficiency.

브이용 전력시스템 설계 및 분석

본 논문은 브이용 PV 시스템 성능과 최대전력점추적기의 알고리즘에 대하여 기술하고자 한다 현재 운영되고 있는 브이는 대부분 태양광을 활용한 독립형 전력 시스템을 이용하고 있다. 이러한 태양광 전력 시스템은 직류 출력 특성을 가지고 있으며 직류 버스와 연결된다. 태양광 전지판의 I-V 출력 특성은 일사량, 전지판의 온도에 따라 변화하며 태양광 전지 마다 그 특성이 각기 다르게 나타나기 때문에 태양광 전지는 최대전력추적방식의 알고리즘에 의해 운영되어야 한다. 이에 본 논문은 태양광 전지의 특성, 최대전력추적방식, 태양광 전지판의 최적경사각을 논의하고자 한다. This paper presents the performance of PV(Photovoltaic) system, the design of MPPT(Maximum Power Point Tracker). Output of PV power system is DC, and PV power system is linked to the DC bus. The current(I)-voltage(V) output characteristic of PV cells changes with solar irradiance and cell temperature as parameters. As various PV modules respond differently to each of the parameters cited above. Maximum output of PV modules am be achieved by MPPT(Maximum Power Point Tracker) algorithm This paper includes a discussion on the performance of PV module, MPPT algorithm and the influence of PV module angle.