MPPT Using Soft Computing Technique with MLI in Photovoltaic System

This Harnessing energy from the abundant and free sunlight is now a days a hot-topic among the research community. The availability of cheap solar modules has made it possible to harvest solar energy with better efficiency. The nature of solar modules is nonlinear and therefore the process of impedance matching is essential. The proper impedance matching ensures extraction of the maximum power from solar PV module. Several algorithms are reported in literatures that are used to operate the DC-DC converter around MPP. Maximum power point tracking (MPPT) algorithm acting an significant part in photovoltaic (PV) power generating system because it exploit the output power from a PV generating set for a various climatically conditions. This paper suggested a new modified work for MPPT of PV energy gathering system by using the optimized novel fuzzy logic controller (FLC). In the optimized FLC method, the membership functions tune automatically. In this paper MATLAB simulation effort shows with MPPT controller, a zero voltage and zero current dc to dc buck-boost Luo converter feeding a battery load is achieved. The results demonstrate that the suggested novel rule FLC MPPT in the photovoltaic generating system is valid.

The photovoltaic (PV) system’s nonlinear nature, uncertainty in temperature, and light intensity in the environment are responsible for the fluctuation of the maximum power point (MPP). Many maximum power point tracking (MPPT) algorithms are proven effective in the literature, including use of artificial intelligence. Proportional integral derivative controller and fuzzy controllers found effective to track MPP. Using the combination of Fuzzy-PID controller, swiftly adapt to changes in the atmosphere to confirm the solar array is continually operating at its highest efficiency with MPP. Easy and effective oscillation elimination is possible with PID control. To regulate the maximum power of the PV system combination of Fuzzy-PID self-tuning controllers is found effective.The goal is to enhance a PV system’s dynamic response to in presence of irradiation and temperature fluctuations. For tuning Fuzzy logic controller (FLC), gain scheduler values of the proportional, integral, and derivative constants are fundamental. The design processes for non-linear conventional PID controllers, FLC, and series combination of Fuzzy-PID controller are discussed. It compares the traditional PID controller, FLC, and Fuzzy-PID controller. The comparison of outputs produced for PID, FLC, and series Fuzzy-PID controller confirms the effectiveness of the algorithm. Use of Fuzzy controller helps in self-tuning of PID controller constant value. MATLAB/Simulink based environment used to perform the simulations.

Dynamic global maximum power point tracking of the PV systems under variant partial shading using hybrid GWO-FLC

This paper presents a control for a three-phase an eleven-level cascaded H-bridge inverter for photovoltaic (PV) system. The maximum power point tracking (MPPT) is capable of extracting maximum power from the PV array connected to each DC link voltage level. The MPPT algorithm is solved by fuzzy logic controller. Space vector pulse width modulation (SVPWM) algorithm uses a simple mapping to generate gate signals for the inverter. This is done to achieve high dynamic performance with low total harmonic distortion (THD). The validity of the system is verified through MATLAB/Simulink and results are compared with three-phase three-level, five-level, seven-level and nine-level cascaded H-bridge inverter for PV system in terms of THD. Finally, simulation results are presented to verify the effectiveness and accuracy of the proposed system.

Solar Energy is the clean and renewable source of energy. Solar PV systems are gaining attraction for energy generation in these days of increasing energy demand due to its reducing cost and new researches carried on its efficient use. The solar cell is always operated at its Maximum Power Point for better results. There are different methods to search the maximum power point of PV system. In this paper, a Maximum Power Point Tracking method for Solar PV system using Sliding Mode Control is proposed. The PV system modeling practice under standard test conditions is discussed. The proposed controller strategy is robust technology in steady state and during varying environmental conditions. A DC/DC boost converter is utilized as a control actuator for the MPP tracking using PWM control on the switches. Simulation results in MATLAB/SIMULINK and discussion are provided to demonstrate the effectiveness of the proposed controller design.

Currently, Photovoltaic (PV) energy is the most important energy resource in electrical power applications around the world since it is inexhaustible, clean and pollution free. As the output power from the PV arrays changing with solar irradiance and temperature, so it is necessary to operate the photovoltaic (PV) system at the maximum power point (MPP) to increase the power extraction from the solar panel. For this effective operation and maximum power extraction from a PV system, Maximum power point tracking (MPPT) algorithm plays a vital role by operating the PV energy system near the MPP. In this work, amongst several existing well established MPPT algorithms, the open voltage (OV) based algorithm is chosen for simulation of a PV system using Matlab / Simscape. As maximum power point (MPP) can be attained with a certain amount of load from the PV power system, so load matching is an important concept of MPPT algorithms. Here, the performances of this open voltage (OV) based algorithm method is evaluated for different values of solar radiation and battery was used as load to see the charging performance for the algorithm.

The solar energy is directly converted into electrical energy by solar PV module. Each type of PV module has its own specific characteristic corresponding to the surrounding condition such as irradiation, and temperature and this makes the tracking of maximum power point (MPP) a complicated problem. To overcome this problem, many maximum power point tracking (MPPT) control algorithms have been presented. Fuzzy logic (FL) has been used for tracking the MPP of PV modules because it has the advantages of being robust, relatively simple to design and does not require the knowledge of an exact model where a mathematical model of the PV module, DC-DC converter, are used in the study of FL based MPPT algorithm. It is suggested to present this problem in the form of two-folds; first to identify the deviation of the power to maximum power point, and secondly, to control the voltage of the DC-DC converter corresponding to maximum power. In this paper, the first discussion approach will stress out the integration of model predictive control in maximum power point tracking MPPT and as progressing a second approach is identified as fuzzy logic controller FLC and perturb & Observe P&O algorithms are analyzed. All are interrelated to MPPT model for a photovoltaic module, PVM, to search for and generate the maximum power; in this case what’s called P-max. As per the first technique the focus is on the optimal duty ratio, D, for a series of multi diverse types of converters and load matching. The design of the MPPT for a stand-alone photovoltaic power generation system is applied where the system will consist of a solar array with nonlinear time varying characteristics, and a converter with appropriate filters. The integration of model predictive control will be addressed first in this paper. The second fold will implement an MPPT system that use the FLC and compare it with a classical MPPT P&O algorithm through the utilization of Simulink. The novel design in the FLC will be based on the use of asymmetrical membership functions to compensate for the asymmetrical P-V curve of solar panel.

"Photovoltaic panel maximum power point tracker". As the name implied, it is a photovoltaic system that uses the photovoltaic array as a source of electrical power supply and since every photovoltaic (PV) array has an optimum operating point, called the maximum power point, which varies depending on cell temperature, the insolation level and array voltage. A maximum power point tracker (MPPT) is needed to operate the PV array at its maximum power point. The objective of this paper is to build MPPT to charge a 12-volts lead acid battery by using a (TBP1275) 74 - watts PV panel. A technique for efficiency extracting the maximum output power from a solar panel under varying meteorological conditions is presented. The methodology is based on connecting a pulse width modulated dc/dc SEPIC converter, which is controlled by a micro controller based unit. The main difference between the method used in the proposed MPPT systems and other technique used in the past is that PV array output power is used to directly control the dc/dc converter thus reducing the complexity of the system. The resulting system has high efficiency, low cost and can be easily modified. This approach ensured maximum power transfer under all conditions by using micro controller for calculation. The tracking capability of the proposed technique has been verified experimentally with a 74 W solar panel at different insolation levels.

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. In this paper, the proposed hybrid MPPT algorithm is used in the solar-PV system with variable climatic conditions. The performance of the proposed hybrid MPPT algorithm with different membership functions is analyzed to optimize the MPP. Simulation results establish that with the application of MPPT controller such as Perturb and Observe, Fuzzy Logic and a proposed hybrid MPPT for the solar-PV system, the proposed hybrid MPPT controller provides more accurate performance and also reduces the fluctuation about the MPP as compared to other MPPT techniques.

there is an increased need for analysing the effect of atmospheric variables on photovoltaic (PV) production and performance. The outputs from the different PV cells in different atmospheric conditions, such as irradiation and temperature , differ from each other evidencing knowledge deficiency in PV systems [14]. Maximum power point tracking (MPPT) methods are used to maximize the PV array output power by tracking continuously the maximum power point (MPP). Among all MPPT methods existing in the literature, perturb and observe (PO however, it presents drawbacks such as slow response speed, oscillation around the MPP in steady state, and even tracking in wrong way under rapidly changing atmospheric conditions. In order to allow a functioning around the optimal point Mopt, we have inserted a DC-DC converter (Buck–Boost) for a better matching between the PV and the load . This paper, we study the Maximum power point tracking using adaptive Intelligent fuzzy logic and conventional (P&O) control for stande-alone photovoltaic Array system .In particular, the performances of the controllers are analyzed under variation weather conditions with are constant temperature and variable irradiation. The proposed system is simulated by using MATLAB-SIMULINK. According to the results, fuzzy logic controller has shown better performance during the optimization.

This paper focuses on the intelligent techniques used for tracking the maximum power point of the solar photovoltaic (SPV) system for varying environmental conditions. The most widely used perturb and observe (P&O) maximum power point tracking (MPPT) technique is discussed briefly for the comparison with the intelligent techniques. The paper proposes control technique for the SPV system by using fuzzy logic controller (FLC)-based MPPT algorithm and the optimisation of its various parameters by genetic algorithm (GA). The performance of the FLC optimised with GA is compared with the P&O and the fuzzy-based MPPT technique. MATLAB/simulink software is used to design the different stages of the MPPT controllers. Simulation results reported that GA optimised FLC perform much better than the P&O and fuzzy logic-based MPPT controllers.

In this paper, a solar photovoltaic (PV) array powered scalar control of the induction motor (IM) drive for the water pumping application is presented. This control scheme involves the standalone operation of the IM drives. Solar PV array has nonlinear I- V characteristics, it requires additional control technique for Maximum Power Point (MPP) tracking. The efficiency is improved with the help MPP tracking (MPPT) technique which extracts maximum power available to the PV array. Perturb and observe (P&O) control technique offers simple control, ease of implementation and low cost is utilized along with DC/DC converter. This Stage extracts maximum power available from the PV array and fed power to the next stage of the DC/AC converter. The switching of DC/AC converter is generated with the help of open-loop V/F control of IM. The performance of the system is examined with the help of MATLAB/Simulink and corresponding results are present to support the suitability of the control scheme.

International audience

Maximization of power from a solar photovoltaic (SPV) module is of special interest as the efficiency of the SPV module is very low. The present work describes the maximum power point tracker (MPPT) for the SPV module connected at variable load conditions. A personal computer (PC) is used for control of the MPPT algorithm. The power tracker is developed and tested successfully in the laboratory. The simulations are carried out in Matlab/Simulink. Data acquisition, monitoring, and control are done by the dSPACE software. Different types of algorithms are studied. The algorithm implemented here is “perturb and observe” in a microcontroller-based PC to extract maximum power from the SPV module. The measured parameters such as panel voltage, current, and power are obtained with and without MPPT systems and have been tested on the SPV Module at various load conditions, different temperatures and insolation labels. The comparative results with and without MPPT are shown; the results show that the tracking capability with MPPT improves as compared with without MPPT.

Photovoltaic systems are one of the most critical and clean renewable energy sources used in solar cells. Due to the increasing shortages of energy from other sources, photovoltaic systems grew in popularity through solar energy. Since current and voltage have a nonlinear relationship, tracking the maximum power point is essential. Therefore, various methods are developed to improve solar energy tracking. One of these techniques is fuzzy logic. This work designed an intelligent artificial system based on a Mamdani fuzzy inference type FL controller under standard test conditions using a photovoltaic panel with battery storage controlled by a bidirectional converter. The proposed intelligent maximum power point tracking controller has been tested using the MATLAB / Simulink program. The system comprises two input sources: a PV module and battery, a boost converter, a bidirectional converter, and the fuzzy logic controller. A different result for different steady cases (irradiation (1000, 600, 400 & 1000) and loads full load & no load) confirm the proper function of the fuzzy logic controller. The results show that the intelligent system controller responds better, faster, and higher efficiency than the conventional controller applied to the same system. When the system is up and running, even though the setup costs initially appear to be high, it pays for itself within a short time.