A Review on MPPT Algorithms for Solar PV Systems (original) (raw)
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A comparative investigation of maximum power point tracking methods for solar PV system
Solar Energy, 2016
In recent years, the solar energy has been considered as one of principal renewable energy sources for electric power generation. However, the maximization of extracted power from PV system is a matter of concern as its conversion efficiency is low. Therefore, a maximum power point tracking (MPPT) controller is necessary in a PV system for maximum power extraction. In this paper, several MPPT methods have been studied and implemented in MATLAB/Simulink environment. Based on the generation of control signal, the MPPT methods have been innovatively proposed to be categorized into three classes i.e. conventional, artificial intelligence (AI) based and hybrid methods. Further, the considered MPPT methods are modeled and compared on the basis of various parameters. For achieving this purpose, MATLAB/ Simulink modeling of a double diode equivalent circuit based PV panel is developed and validated with commercially available solar panel. Then, the designed MPPT methods are implemented on this PV system under varying solar irradiation conditions to study their dynamic response for tracking the maximum power point. Based on this study, a novel comparison of various class of MPPT method is carried out in terms of output voltage, current, power, rise time, fall time, tracking efficiency etc.
2020
Backgrounds: Solar photo-voltaic (PV) arrays have non-linear characteristics with distinctive maximum power point (MPP) which relies on ecological conditions such as solar radiation and ambient temperature. In order to obtain continuous maximum power (MP) from PV arrays under varying ecological conditions, maximum power point tracking (MPPT) control methods are employed. MPPT is utilized to extract MP from the solar PV array, high-performance soft computing techniques can be used as an MPPT technique. Results: In order to show the feasibility and performance of the proposed Artificial Intelligence based Perturbe and Observe (AIAPO) MPPT controller, a simulation analysis has been carried out using the PV system. Combined results with different MPPT systems for power, voltage and current waveforms are the output values increase to 272.4W, 157V and 1.74A respectively. Using proposed AIAPO MPPT provides more accurate and stable result as compared to Perturbe and Observe (PO), Fuzzy Logi...
Artificial Intelligence Based MPPT techniques for Solar PV System: A Review
2019
A solar photovoltaic (PV) power generation system (SPPGS) is be important as energy sources because its benefits. In the large SPPGS, the partial shaded condition (PSC) is occurs and its effect is highly decrease efficiency of SPPGS. Under PSC, the multiple peaks in P-V characteristics, that condition conventional maximum power point tracking (MPPT) techniques not able to achieve maximum power point (MPP) in such a case. The artificial intelligence (AI) based MPPT (AI-MPPT) methods are faster, reliable in performance and improve the efficiency of SPPGS. In this paper different AI based MPPT techniques are review and compare different techniques on the bases of tracking speed or time, oscillation, efficiency and limitation. Keywords— Maximum power point tracking, artificial intelligence , Particle swarm optimization, Antcolony optimization, Artificial neural network , Fuzzy logic control.
MAXIMUM POWER POINT TRACKING BASED ARTIFICIAL NEURAL NETWORK APPROACH FOR SOLAR PHOTOVOLTAIC SYSTEM
IAEME PUBLICATION , 2021
In a photovoltaic system, tracking the maximum power point (MPP) is a difficult task due to changes in climatic conditions. In addition, due to several peaks in the power supply voltage characteristics, the tracking algorithm becomes more complicated under partial shadow conditions. This paper introduces a new method for tracking the global maximum power point under partial shadow conditions. This method combines the maximum power point tracking The method combines an artificial neural network controller. This paper discusses one of the most important algorithms to extract maximum power from the PV panel implemented with DC-to-DC converters and based Artificial Neural Networks based Maximum power point tracking used to provide maximum power from the photovoltaic module to the load. Therefore, this new ANN method shows the main ability to extract the maximum power. A new MPPT search method for the maximum power point based on artificial neural networks has been used in this work. Solar radiation changes sharply. It is possible to determine precisely the extract power of MPP, which can decide that the system will operate in a stable mode. An artificial neural network can predict solar radiation level and battery temperature according to different operating conditions under changing environmental conditions to optimize energy production and optimize solar power tracking from solar cell systems. Therefore, this new ANN method demonstrates its most important ability to extract maximum power from the solar panel MPPT algorithms are typically used in photovoltaic systems to optimize solar power. When solar radiation changes sharply, MPP benefits as higher fault tolerance and a simpler implementation, making the system work in stable conditions. The simulation of this proposed model has performed on MATLAB software, and 85%. accuracy obtained in this proposed system.
A Review of Maximum Power Point Tracking Algorithm for Solar Photovoltaic Applications
The world's large dependency on conventional energy sources has not only posed a threat to the environment but also they are non-renewable. Therefore, a huge interest is put upon renewable sources of energy. Amongst them, solar technology has become a rapid growing industry for power generation. This paper briefly reviews the technological challenges of maximum power point (MPP) tracking of photovoltaic (PV) energy obtained from solar cells. The paper describes the evolution of several MPP techniques that are popular commercially and presents their basic working, utilisation ability in different scenarios, cost of implementation and new research performed to find better techniques. The study also includes incorporation of soft computing in solar MPP tracking. It is observed that, the MPP tracking techniques are rapidly evolving from simple to complex methods, as per the demands dictates. The simpler methods like perturb and observe are cost effective and have simpler design, but are highly inefficient in terms of efficiencies under drastically changing environment. They find application in streetlights and solar lanterns. The incorporation of soft computing methods like ANNs, FLCs, can drastically increase efficiency, but are cost ineffective. Such techniques find place where efficiency matters the most. In large PV plants, these systems prove to be highly efficient.
Solar energy is energy which can be harnessed conveniently and free. However, its conversion result may not be easily obtained. Based on the previous research, solar power plant is a source of renewable energy, utilizing solar energy. Solar power plant converts solar energy into electricity using Photovoltaic (PV) or solar cells. Even though solar power plant is considered as better energy alternative, it presents problems and weaknesses. In this case, the problems are related to insufficient power generation with low power efficiency, high oscillation and very slow power tracking. Hence, in order to solve these problems, Maximum Power Point Tracking (MPPT) has been utilized. Combination method of P&O-fuzzy and IC-fuzzy is employed to its design. Moreover, combined algorithm may result better power from conventional algorithm due to appropriate performance of duty cycle according to system design, with efficiency result of 79%-85.6%, tracking in searching output power of 0,0055s-0,008s, low oscillation and maximum power generated by combined algorithm of 1028 watt.
International Journal of Innovative Science and Research Technology, 2021
The uncertainty associated with modelling and performance of solar photovoltaic systems could be easily and efficiently solved by using Maximum power point techniques. During the past decade of 2010 to 2021, the classification of techniques based on intelligent, nonintelligent and their hybrid models are found as potential techniques for detecting the maximum power point of a photovoltaic system. In addition, for this decade there is no extensive and comprehensive review on applicability of intelligent, non-intelligent and their hybrid models for performance prediction and modelling of solar photovoltaic systems. Therefore, this article focuses on extensive review on design, modelling, maximum power point tracking, advantages, disadvantages of each technique, evolutionary trend, convergence and tracking speed, and output efficiency prediction of solar photovoltaic systems under partial shading conditions and non-partial shading conditions using intelligent, non-intelligent and their hybrid techniques. Furthermore, a total of 77 selected articles on the solar PV tracking technique and their hybrid models together with the PV technology were reviewed. Total of 22 articles are reviewed and summarized in this review paper for the period of 2010 to 2021 with 12 articles in non-intelligent technique, 7 articles in intelligent technique and 3 articles in their hybrid form. The review showed the suitability and reliability of intelligent, nonintelligent and their hybrid models for accurate detection of maximum power point and the performance characteristics of solar photovoltaic systems. Finally, this review presents the guidance for the researchers and engineers in the field of solar photovoltaic systems to select the suitable techniques for enhancement of the performance characteristics of the solar photovoltaic systems and the utilization of the available solar radiation.
CERN European Organization for Nuclear Research - Zenodo, 2022
The performance of a photovoltaic (PV) module can be improved by employing maximum power point tracking (MPPT) controllers. MPPT controllers are algorithms that are included in PV battery charge controllers or inverters to extract the maximum available power from PV modules for any given temperature and irradiance. Several studies report that the use of PV modules without MPPT controllers results in power losses, which ultimately results in the need to install more solar panels for the same power requirement. Numerous techniques of varying complexities have been proposed in the literature to solve the MPPT objective function. This paper presents a comparative analysis of three computational intelligence (CI) based MPPT techniques namely, the fuzzy logic (FL) based controller, artificial neural networks (ANN) based controller, adaptive neuro-fuzzy inference system (ANFIS) based controller and one conventional technique, the perturbation and observation (P&O) controller. These MPPT controllers are designed, simulated and analysed in the MATLAB/Simulink environment. The performance of the studied MPPT techniques is evaluated under steady-state weather conditions, rapidly changing weather conditions and varying load conditions. CI-based MPPT controllers are found to be more efficient than the P&O controller. Moreover, the ANFIS-based MPPT controller shows an outstanding MPPT performance for all the scenarios studied.
Fuzzy Logic MPPT Techniques in Solar Photovoltaic System
IJRASET, 2021
This paper presents the comparative analysis of most commonly used Maximum Power Point Tracking (MPPT) techniques viz Open Circuit Voltage (OCV), Perturb and Observe (PnO) and Incremental Conductance (INC) methods that are capable of extracting maximum power from the PV generation system. The OCV technique is an indirect MPPT method that tracks the Maximum Power Point (MPP) using empirical data or mathematical expressions with numerical corrections and approximations. To operate PV panels at that point (MPP) there are many MPPT method in literature, FLC MPPT method was preferred in this study because, its rapid response to changing environmental conditions and not affecting by change of circuit parameters. The accuracy of FLC MPPT method used in this system to find MPP changes, from 94.8% to 99.4%. To charge a battery there are two traditional methods which are constant current (CC), and constant voltage (CV) methods. For fast charging with low loss constant current and voltage source is a need. One of the methods providing constant is PI control which used in this study.
An Improved Maximum Power Point Tracking Controller for PV Systems Using Artificial Neural Network
This paper presents an improved maximum power point tracking (MPPT) controller for PV systems. An Artificial Neural Network and the classical P&O algorithm were employed to achieve this objective. MATLAB models for a neural network, PV module, and the classical P&O algorithm are developed. However, the developed MPPT uses the ANN to predict the optimum voltage of the PV system in order to extract the maximum power point (MPP). The developed ANN has a feedback propagation configuration and it has four inputs which are solar radiation, ambient temperature, and the temperature coefficients of Isc and Voc of the modeled PV module. Meanwhile, the optimum voltage of the PV system is the output of the developed ANN. Based on the results; the response of the proposed MPPT controller is faster than the classical P&O algorithm. Moreover, the average tracking efficiency of the developed algorithm was 95.51% as compared to 85.99% of the classical P&O algorithm. Such developed controller increases the conversion efficiency of a PV system. Streszczenie. W artykule zaprezentowano ulepszony układ śledzenia maksymalnej mocy w systemie fotowoltaicznym. Zastosowano sieć neuronową i klasyczny algorytm P&O. Sieć neuronowa w sprzężeniu zwrotnym ma cztery wejścia: promieniowanie słoneczne, temperatura otoczenia i współczynniki temperaturowe I SC i V oc. Wyjściem jest optymalne napięcie systemu. (Ulepszona metoda śledzenia maksymalnej mocy systemu fotowoltaicznego z wykorzystaniem sieci neuronowej) Introduction Photovoltaic systems are one of the direct solar energy systems. Whereas, photovoltaic systems collect light from the sun and convert it to electricity. PV systems are clean whereas it reduces greenhouse gases, and it is non-polluting. However, the typical photovoltaic system is consisted of PV modules, DC-AC inverter, charger controller and batteries. In a PV system, the PV modules generate D.C electricity which is used to charge batteries through a charge controller. Meanwhile, inverters convert the D.C current to A.C current. However, the main drawbacks of PV systems are the capital cost and the dependence on climate conditions such as solar radiation and ambient temperature. As a fact, each photovoltaic module has an optimum operation point, called maximum power point (MPP). This point varies depending on cell temperature, solar radiation, and load impedance. However, the MPPT is a power electronic device located between the PV modules and the loads, in order to ensure the maximum power operation. Many methods proposed in the literature to track the MPP for a PV system. In [1] a MPPT is developed to avoid the oscillation in the classical P&O algorithm that compares only two points, which are the current operation and the subsequent perturbation point to observe their changes in power. Then, based on the difference in the output power the controller increase or decrease the PV module array output voltage. The authors of [1] developed an algorithm of three-point weight comparison, which runs periodically perturbing the solar array terminal points of the P-V curve. The three points are the current operation point A, a point B perturbed from point A, and a point C with doubly perturbed in the opposite direction from point B. If two points are positively weighted, the duty cycle of the converter must be increased. While, when two points are negatively weighted, the duty cycle of the converter should be decreased. In the other cases with one positive and one negative weighting, the MPP is reached or the solar radiation has changed rapidly and the duty cycle is not able to be changed. The authors of [1] claimed that the experimental test verified the tracking efficiency as well as avoided the oscillation in the classical P&O algorithm. In [2] a modified P&O based MPPT is presented to track the maximum power point of PV systems. The proposed algorithm is divided into two major