Conventional MPPT Methods Research Articles

Development of grey wolf optimization based modified fast terminal sliding mode controller for three phase interleaved boost converter fed PV system

The conventional MPPT method has drawbacks, such as that under partial shading conditions, several peaks occur and identifying the global peak is difficult. It may converge to a local peak and lead to poor conversion efficiency and tracking efficiency. Implementation of a hybrid algorithm by integrating P&O and metaheuristic algorithms can perform better under partial shading conditions. But the tracking speed is low and the response time is longer. To mitigate the issues mentioned above, a new hybrid algorithm has been suggested that integrates GWO and a modified fast terminal sliding mode controller (MFTSMC). The suggested method with three phase ILBC is incorporated into the PV system. The MATLAB tool is employed to experiment with this study. The performance of GWO-MFTSMC is analysed through MATLAB/ SIMULINK and compared with the performance of ANN-FTSMC and PSO-FTSMC algorithm based MPPT techniques. A hardware prototype is developed and tested for 5 × 200 W solar PV modules with the GWO-MFTSMC algorithm. The proposed method conversion efficiency is 99.72% and 96.15% under simulation and hardware realisation, respectively, which is higher than the ANN-FTSMC and PSO-FTSMC methods.

Design, implementation and empirical analysis of a cascaded hybrid MPPT controller for grid tied solar photovoltaic systems under partial shaded conditions

Solar Photovoltaic (PV) system based power generation is going to become the major power source in the following few decades. Because of the advantages such as reliability, cleanliness, feasibility in small scale to medium scale power generation etc., Solar Photovoltaic (PV) based power generation is developing very rapidly. Hybrid MPPT control methods are essential to track and achieve maximum power generation from solar PV systems under normal and Partially Shaded Conditions (PSC). The proposed Hybrid Grey Wolf Optimization (GWO) algorithm with Adaptive Neuro-Fuzzy Inference System (ANFIS) will effectively tracks the Maximum Power Point when compared to the conventional hybrid MPPT methods. Luo converter is employed as DC-DC converter to convert the variable DC output from solar panel into a nominal DC level. The Luo converter output is given to a single phase Voltage Source Inverter (VSI) and the inverter is connected to grid. The Arithmetic Optimization Algorithm (AOA) tuned Fractional order Proportional Integral controller (FOPI) is adopted to maintain the stability of the grid connected inverter. The tracking efficiency of the proposed system is maintained around 98 % even under different irradiance conditions with very less convergence time of 0.016 s.

Improved Artificial Neural Network Design for MPPT Grid-Connected Photovoltaic Systems

Abstract Photovoltaic (PVS) generators’ nonlinear electrical characteristics allow for greater performance and efficiency when they are forced to operate at their peak power (MPP). This article suggests an adaptive method for maximizing power point tracking that makes use of artificial neural network (ANN) techniques (MPPT). A step-up converter powered by a separate solar generator is under the control of an ANN controller built on a neural network training database (PVS). The results show that ANN-MPPT has good control performance and is near to the maximum power point of PVS when compared to conventional MPPT methods like perturb and observe and incremental conductance.

Performance Enhancement of Grid Integrated Photovoltaic System Using Luo Converter

The main aim of this article is to implement a novel active power decoupling-based Luo converter for the weak grid system interfacing photovoltaic (PV) system. The modified Perturb and Observe (P&#x0026;O) algorithm has been used for maximum power extraction from the PV source. The proposed system is capable of operating in the standalone mode and the grid-connected mode. The Luo converter is promising with the viewpoint of low inrush current, high voltage gain, small ripples along with high power density, high efficiency, and simple structure. Hence, the Luo converter has been used for maximum power extraction from the PV source, which is appropriate for both grid-tied and islanding modes of operation. The modified P&#x0026;O algorithm has been used for maximum power extraction. The proposed MPPT technique reduces the drift problem, which occurs in the conventional MPPT methods by including the data of change in current (<inline-formula> <tex-math notation="LaTeX">$\Delta I$</tex-math> </inline-formula>) in addition to the data used in the conventional P&#x0026;O algorithm. This extraction has achieved by using a novel decoupling method, which, in turn, reduces the size of the filter. The converter along with the decoupling method also ensures reduced harmonics at the grid output. The operational principle and the stability analysis of the proposed converter have been analyzed in different modes of operation, such as grid-connected mode and standalone. The simulation has been carried out in MATLAB/Simulink environment. The implementation of the converter is carried out using a microcontroller-based prototype model to validate the performance. The efficiency of the proposed converter has been calculated to be 87.53% when operating under the grid-connected mode, whereas the efficiency is 92.46% when operating under the standalone mode.

Enhancement of MPPT controller in PV-BES system using incremental conductance along with hybrid crow-pattern search approach based ANFIS under different environmental conditions

Today, photovoltaic (PV) systems have turned into one of the best options to supply the load demand in developed countries thanks to their numerous benefits. The amount of active power generation in solar arrays effectively depends on their terminal voltage. To enhance the operation of PV systems, it is needed to use the maximum power point tracking methods known as MPPT algorithms. The conventional MPPT methods do not function properly in rapidly changing atmospheric conditions. An MPPT algorithm is developed is in the paper, utilizing the incremental conductance (INC) technique, together with a hybrid crow and pattern search (HCS-PS) algorithm based adaptive neuro-fuzzy inference system (ANFIS). The developed framework would be deployed to specify and track the MPP within a bi-step technique. The optimal voltages would be determined by employing the hybrid HCS-PS, considering various values of temperature and solar irradiance and used as the input–output training information set in the ANFIS in the first stage. The INC technique is applied for the tracking cycle process and begins an accurate tracking method from the mentioned point in the second stage. The power output of the solar PV panel is associated with substantial volatility, indicating the storage requirements. In this regard, a battery energy storage (BES) unit can be utilized along the solar panel. The simulation results confirm that the proposed HCS-PS-ANFIS-INC strategy can successfully decrease the convergence time and increase the output power under different conditions.

An intelligent BWO algorithm-based maximum power extraction from solar-PV-powered BLDC motor-driven light electric vehicles

A Maximum Power Tracking Technique (MPPT) for Photovoltaic Powered e-Vehicles via Black Widow Optimization Technique is introduced. The proposed system addresses the problems of conventional MPPT methods via a black widow spider-inspired optimization approach. As a result, the design would require fewer iterations to achieve prime conditions, thus increasing the complete efficiency of the proposed system. Field-oriented control (FOC) is used for speed control of the BLDC engine (e-vehicle). The proposed model was first designed, and then simulated in MATLAB environment. The simulink results run in parallel with the Typhoon HIL 402 setup. The results obtained the superior performance of the BWO-based MPPT technique. Details of the modeling of a new MPPT used for PV-driven BLDC-based e-vehicles are also discussed in this paper. There are many factors involved in a real situation for poor efficiencies, such as shade, irregular sunlight, and weather conditions, which show the non-linear characteristics of PV. The MPPT approach discussed in this article may be used to increase overall productivity and minimize costs for the operation of e-vehicles based on the PV framework.

PSO and ANN Based Hybrid MPPT Algorithm for Photovoltaic Array under Partial Shading Condition

MPPT is an electronics device that extracts maximum available power from a PV module under varying environmental conditions. But most of the conventional MPPT methods fail to track maximum power under partial shading condition (PSC). Partial shading is the most common situation in PV power generation, which is caused if part of the series-connected strings is partially shaded. This situation leads to the multiple peaks in the P-V characteristics curve of the PV system. So stochastic search method, Particle Swarm Optimization (PSO), is used instead of the conventional methods to track maximum power under PSC. But the PSO method has the limitation of slow operation. So in this paper, a fast hybrid method is presented, which combines the PSO method with the ANN method. In this hybrid method, the ANN enables the existing PSO method to track MPP quickly by providing more accurate initial particle positions of the PSO algorithm.

Modeling of photovoltaic system with maximum power point tracking control by neural networks

&lt;p&gt;This paper presented the study, development and implementation of the maximum power point of a photovoltaic energy generator adapted by elevator converter and controlled by a maximum power point command. In order to improve photovoltaic system performance and to force the photovoltaic generator to operate at its maximum power point, the idea of the context of this paper deals with the exploitation of the technique of the artificial intelligence mechanism (neural network) certainly based on the three parts of the photovoltaic system (photovoltaic module inputs (temperature and solar radiation), photovoltaic module and control (MPPT)) that have been adopted within a simulation time of 24 hours.&lt;/p&gt;&lt;p&gt;In addition, to reach the optimal operating point regardless of variations in climatic conditions, the use of a neuron network based disturbance and observation algorithm (P&amp;amp;O) is put into service of the system given its reliability, its simplicity and view that at any time it can follow the desired maximum power.&lt;/p&gt;&lt;p&gt;The entire system is implemented in the Matlab / Simulink environment where simulation results obtained are very promising and have shown the effectiveness and speed of neural technology that still require a learning base so to improve the performance of photovoltaic systems and exploit them in energy production, as well as this technique has proved that these results are much better in terms (of its very great precision and speed of computation) than those of the controller based on the conventional MPPT method P&amp;amp;O.&lt;/p&gt;

MPPT Controller for PV Array under Partially Shaded Condition

The output power of the Photovoltaic system having multiple arrays is reduced to a great extent when it is partially shaded due to environmental hindrances. Conventional popular MPPT methods are effective under uniform solar irradiance. However, under partially shaded conditions, these MPPTs can fail to track the real MPP because of the multiple local maxima which can be existed on PV characteristic curve under partially shaded condition. This paper reports the development of a maximum power-point tracking method for photovoltaic systems under partially shaded conditions using bond graph. The major advantages of the proposed method are simple computational steps, faster convergence, and its implementation on a low-cost microcontroller. The performance of proposed MPPT is analyzed according to the position of real MPP. Simulation results have been contrasted with real measured data from a commercial PV module of Photowatt PW1650.

An Enhanced Incremental Conductance Algorithm for Photovoltaic System

The energy obtained from the photovoltaic array is dependent on the available solar insolation, the panel tilt angle and the power point tracking algorithm of the system. Some of the Conventional MPPT methods are developed by considering uniform solar irradiance. During partial shading conditions, solar panel may produce multiple Local Maximum Power Points (LMPPs) in its power voltage characteristic curve. A new algorithm has been developed in this paper by using sequential sampling embedded with existing incremental conductance procedure in order to predict the Global Maximum Power Point (GMPP). The tracking capability of proposed algorithm is verified with simulation works carried out in MATLAB/SIMULINK. The results of proposed algorithm are likened with the results classical Perturb and Observe (P&amp;O) and Incremental Conductance algorithms.

A Novel Technique for Maximum Power Point Tracking of a Photovoltaic Based on Sensing of Array Current Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Abstract Maximum Power Point Tracking (MPPT) is now widely used method in increasing the photovoltaic (PV) efficiency. The conventional MPPT methods have many problems concerning the accuracy, flexibility and efficiency. The MPP depends on the PV temperature and solar irradiation that randomly varied. In this paper an artificial intelligence based controller is presented through implementing of an Adaptive Neuro-Fuzzy Inference System (ANFIS) to obtain maximum power from PV. The ANFIS inputs are the temperature and cell current, and the output is optimal voltage at maximum power. During operation the trained ANFIS senses the PV current using suitable sensor and also senses the temperature to determine the optimal operating voltage that corresponds to the current at MPP. This voltage is used to control the boost converter duty cycle. The MATLAB simulation results shows the effectiveness of the ANFIS with sensing the PV current in obtaining the MPPT from the PV.

Design of Soft Switching Interleaved Boost Converter for Photovoltaic Application

The objective of our study is to reduce the size of passive components and reduction of ripple in output voltage and input current by using interleaved soft switching dc-dc converter (interleaving action). The PV system converts solar energy into electrical energy but its output varies with variation of solar radiation and temperature. To provide impedance matching between PV and load a dc-dc converter with MPPT technique is used in PV (Photo Voltaic) Systems. This study incorporates design of the interleaved soft switching dc-dc converter for PV System by using P&O (Perturb and Observe) fuzzy logic controller. The P&O Fuzzy MPPT provides an accurate and fast converging under varying illumination and load conditions compared to conventional MPPT methods. The performance of the system is first analyzed by simulation using Matlab/Simulink software. The results of simulation were compared and analyzed.

MPPT of Two-Peak PV System Combined the Hysteresis Comparison Method with Incremental Conductance

Due to the different PV modules and external conditions, large-scale photovoltaic power plants often show a multi-peak PV characteristics. But the traditional MPPT methods is only for single-peak P-V curve to strike a maximum power point. So we need to improve the conventional MPPT methods. In the MATLAB/Simulink environment, we combined the Hysteresis Comparison Method with Incremental Conductance to build the S-function to create a simulation model obtaining the maximum power point power, voltage and current. In the same simulation system the traditional Incremental Conductance was used to obtain the maximum power, voltage and current which compare with the maximum power, voltage and current which obtained by the proposed method . The results of simulation illustrated the effectiveness of the proposed method .

온도측정 기반의 최적전압을 이용한 PV 시스템의 TMOV MPPT 제어

The characteristics of I-V and P-V of solar cell nonlinearly changes according to irradiation, temperature and load. Therefore, to use efficiently PV system, operating point must be always operating at maximum power point. Also, PV system is semiconductor, so it generates loss by temperature. But because of conventional MPPT methods are not considering temperature, it has problem which decrease efficiency. This paper proposes temperature measurement based optimal voltage(TMOV) MPPT algorithm using temperature measurement based optimal voltage. It analyzes characteristics of solar cell according to irradiation and temperature and conventional MPPT methods. The TMOV control algorithm proposed in this paper is compared and analyzed conventional MPPT methods. The validity of this paper proves using this result.