Conventional MPPT Research Articles

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.

ARTIFICIAL INTELLIGENCE TECHNIQUE BASED MPPT CONTROLLER FOR STANDALONE SOLAR ENERGY CONVERSION SYSTEM

This paper presents the development and performance analysis of Artificial Intelligence Technique based MPPT controller for a standalone solar energy conversion system. The proposed system consists of 2.5 kW PV array, DC to DC boost converter, inverter and different load. The DC to DC Converter connected to Inverter through DC link capacitor. The inverter power fed to the load through step down transformer. The standalone solar energy conversion system consists of MPPT controller. In this research Artificial Intelligence Technique based MPPT controller on Levenberg algorithm developed. For the proposed Artificial Intelligence Technique based MPPT takes two variables as PV voltage and PV current as an input and output the duty ratio for DC to DC boost converter. The Artificial Intelligence Technique based MPPT controller for standalone solar energy conversion system with various load is modeled and simulated in MATLAB/ Simulink environment. Performance of both the Artificial Intelligence Technique based MPPT controller compared with conventional incremental conductance MPPT. Simulation results show that for a wide range of input irradiance, Artificial Intelligence Technique based MPPT controller shows improved performance than the conventional incremental conductance MPPT with at various operating conditions.

A novel hybrid maximum power point tracking method based on improving the effectiveness of different configuration partial shadow

The generation power of solar cells depends on the ambient temperature and the intensity of the sun's radiation. Also, a set of unconventional phenomena such as buildings, trees, clouds, etc. causes a slight shadow on solar arrays. Partial shadows will create a disparate effect between the modules such as reduced production capacity and hotspots phenomenon. While in this condition several maximum power points in the power-voltage characteristic are created. In the case of uniform radiation, the conventional MPPT method can be used. However, in partial shade conditions, these methods are not effective due to the presence of several maximum power points in the power-voltage characteristic. One of the ways to improve the condition of the solar array is rearranging the array to distribute the uniform shadow in the array. In this paper, an algorithm based on the different shadow patterns is proposed for a fixed reconfiguration in such a way that maximum efficiency is achieved in different shadow conditions. On the other hand, a new method for the MPPT is provided under partial shadow conditions, so that in the case of uniform radiation, tracking operations are observed and followed by the proposed algorithm, and in the case of partial shadow, it works by constant voltage. The simulation results show that the configuration and distribution of the shadow at the level of the solar array with the proposed algorithm with a novel MPPT has a good improvement in the higher productivity of photovoltaic arrays compared to other methods introduced for reconfiguration.

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.

Performance analysis of multiple gain boost converter with hybrid maximum power point tracker for solar PV connected to grid

Abstract This paper analyses the performance of a multiple gain boost converter (MGBC) connected to a solar photovoltaic (SPV) system with a grid-tied inverter. The capacitor inductor capacitor (CLC)-based MGBC is integrated to lift the output voltage of the SPV to the required level. The voltage lifts/levels in a boost converter (BC) are based on the number of CLC cells. The suggested MGBC is compared with various conventional BCs in terms of boost factor and component count. A hybrid fuzzy logic–perturb &amp; observe maximum power point tracker (HFL–P&amp;O MPPT) developed is used to monitor Pmax (maximum power) generated by the SPV. Also, a comparative analysis of the suggested MPPT with a conventional P&amp;O and fuzzy logic-based (FLB) MPPT for a CLC-based MGBC is presented. The comparison is made in terms of rise time, voltage ripples (%), steady-state time and boost factor. The output characteristics of the developed model for different irradiation conditions are analysed using the MATLAB/Simulink tool. The MPPT simulation result exhibits minimum oscillations at MPP and a significantly better rise time of 0.015 s when compared with a conventional MPPT and FLB MPPT. The proposed CLC-MGBC gives an appreciable boost factor of 4. The generated SPV power is supplied to the grid through a conventional inverter and synchronized using a phase-locked loop system. The total harmonic distortion of 1.66% obtained in the output current of the inverter is within the permissible range of the IEEE standards.

Maximum energy harvesting of centralized thermoelectric power generation systems with non-uniform temperature distribution based on novel equilibrium optimizer

Recently thermoelectric generation (TEG) systems are gaining popularity due to their increased power density and wide applications in industrial and energy conversion applications. This paper presents a novel optimal control technique for TEG systems under non-uniform temperature distribution (NUTD). Although centralized MPPT control can reduce the footprint and cost of MPPT of TEG systems and provide excellent advantages over the cross-tied, string, and distributive control configurations, it suffers from drawbacks caused by NUTD on the hot or cold sides of TEG modules. Specifically, the bypass diodes streamline the power flow in centralized TEG systems, which may generate multiple local maxima in electrical characteristics curves of TEG systems. To resolve such issues, this paper implements a novel MPPT control technique based on a swarm intelligence (SI)-based optimization algorithm, Equilibrium Optimization (EQO). To study the effectiveness of the proposed EQO technique, five distinct case studies were used. Additional sensitivity analysis and statistical studies were also done to analyze the comparative performance. The results show that the power tracking efficiency can be increased to 99.68% with 1.8–8% more energy harvesting. The stable voltage and current transients can be achieved with a min tracking time of 180 ms, achieving up to 46% faster GM tracking and 238% faster settling time than some conventional MPPT control techniques. In light of experimental simulations, it is safe to conclude that the proposed EQO-based technique outperforms the existing control techniques for TEG systems under all concerned operating conditions.

Adaptive MPPT control applied to virtual synchronous generator to extend the inertial response of type-4 wind turbine generators

The increasing penetration level of wind generation in power systems has resulted in the degradation of the system frequency response and stability issues, where the decrease of the system equivalent inertia is one of the main causes of these problems. In this context, this work proposes an operational and control approach to provide emulated inertial response to wind turbine generators based on fully rated converter that operate at the maximum power point. The inertial response of the wind turbine generator is extended by a control strategy that combines an adaptive MPPT control with an adaptive inertia control based on virtual synchronous generator. The proposed approach mitigates the antagonistic interaction between the traditional MPPT control and inertia control. The impact of the MPPT control and inertia control on the dynamics of the wind turbine and system frequency is comprehensively addressed. The results have shown that the proposed strategy is effective in extending the inertial response of the wind turbine generator and mitigating the amplitude of the second frequency dip and electromechanical oscillations caused by the conventional MPPT control. The adaptive inertia control also prevents the violation of the minimum speed limit of the wind turbine

Cascaded Feed Forward Multilayer Neural Network based MPPT Controller for Improving the Performance of Photovoltaic System

This paper deals with the energy production of photovoltaic (PV) cells in different weather conditions. Today, photovoltaic generation plays an important role in generating electricity and satisfies the demand of the island's consumers. The power generation of the PV cell was completely dependent upon sunlight and temperature, but sunlight and temperature changed forever in nature. The many researchers are working on different MPPT technologies for a PV system. Conventional MPPT controllers cannot withstand a sudden change of weather conditions. The main aim of this article is to compare the various conventional and intelligent controller such as the GA, Fuzzy, KGMO, and CNFF for MPPT of the PV system. The proposed intelligent controller was developed and simulated by the MATLAB environment for MPPT in the PV system. In addition, the above results are evaluated and compared. Based on performance, the optimal smart controller has been recommended as MPPT of the PV system

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.

Recent developments of MPPT techniques for PV systems under partial shading conditions: a critical review and performance evaluation

This review covers global maximum power point tracking (GMPPT) methods for photovoltaic (PV) systems under partial shading conditions. Unlike the previous review works that primarily focused on soft computing and hybrid GMPPT, this study gives exclusive attention to the improvement achieved by the conventional MPPT (perturb and observe, hill climbing, and incremental conductance). The improved methods include the popular 0.8 × V oc model and, more recently, the skipping algorithms. In addition to providing qualitative descriptions of the available techniques, this work also attempts to provide a fair evaluation of GMPPT to determine their comparative performances. The competing algorithms, which are selected to represent every category (conventional and soft computing and hybrid MPPT), are benchmarked under carefully selected operating conditions and shading scenarios. The evaluation is focused on four main criteria: tracking accuracy, convergence time, length of voltage fluctuations, and transient efficiency during the search for the global maximum power point. The results obtained from this study can become a basis for researchers and designers to select the best MPPT technique for their respective applications.

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.

Global maximum power point tracking of solar PV strings using the teaching learning based optimisation technique

ABSTRACT The rapid developing smart cities in India increase demand for electric power generation using renewable energy sources. Sun irradiation that falls on the solar photovoltaic (PV) panel has different P-V and I-V characteristics. This work presents the extraction of global maximum power point tracking of a PV system using the novel population-based optimisation method called the teaching learning based optimisation (TLBO) technique inspired by the class room teaching environment. The proposed method overcomes the shortfalls of other conventional MPPT tracking methods in view of their tracking efficiency and steady-state oscillations. TLBO and PSO algorithms were compared based on the number of fitness evaluations rather than that of the number of population under different environmental conditions. Also, the proposed research work was analysed in a qualitative and quantitative approach through matlab/simulink, respectively. It is found that TLBO met the heuristic method requirements, showed better performance compared with the PSO method with less computational effort and good consistency.

Fully Robust Sensorless Control of Direct-Drive PMSG Wind Turbine Feeding a Water Pumping System

In this paper, we propose a fully sensorless control strategy that guarantees robustness and stability properties. The aim is to maximize the extracted power and to ensure an optimum control without mechanical sensors to reduce the cost. Firstly, a fuzzy logic technique has been used to estimate the wind speed information. Then an improved model reference based on adaptive sliding mode controller has been developed to estimate the permanent-magnet synchronous generator speed. The proposed control strategy includes two control loops, an outer control loop based on fuzzy super twisting algorithm to regulate the generator speed to its optimal value and to provide the optimum DC side current, and an internal control loop based on sliding mode controller to regulate the DC side current to its optimal value and provides the appropriate signal for the DC-DC boost converter. The considered system in this work is supposed supplying a water pumping system for a use in isolated areas. The proposed control strategy has been compared to the conventional MPPT control method based on the concept of perturbation and observation. The obtained results show, a good estimation performance for the wind speed estimator and the improved MRAS speed observer, a good tracking performance and a good stability around the maximum power point. Comparing to the classical MPPT strategy, the current technique can greatly reduce the ripples that can generate vibrations and significant noise on the generator and the motor-pump group.

A Novel High Performance Characteristic Resistance MPPT Algorithm for Photovoltaic Systems

This paper proposes a novel high performance characteristic resistance MPPT algorithm for photovoltaic systems under rapidly changing environmental conditions. The methodology is based on the comparison of instantaneous resistance with characteristic resistance of the PV module corresponding to the prevailing climatic conditions. The characteristic resistanceof aPVmodule is the outputresistanceat its maximum power point. Compared to conventional MPPT techniques, this algorithm offers a better performance in terms of start-up, steady-state and transient characteristics for all climatic conditions. Additionally, this algorithm fully eliminates the problem of drift. This technique is also simpler than conventional MPPT techniques. This proposal has been validated by experimental studies under suddenly changing climatic conditions.

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.

Interpretation of MPPT Techniques in Grid Connected Solar PV Array System

Solar energy is one of the most used and readily available renewable energy sources among the other energy sources. The power generated by PV systems is dependent on solar irradiance and temperature parameters. In the literature, many researchers and studies are interested in estimating true maximum efficiency point for the PV systems. Due to that fact, MPPT applications and techniques become an important issue for PV systems under both uniform and non uniform conditions. Although, PV system under uniform environment has only one maxima point on P-V curve which is simple to estimate correctly by conventional MPPT techniques, it is not as simple as under non-uniform condition such as partial shading and mismatch effects. To overcome the drawbacks of the conventional MPPTs under non uniform condition, researchers has been investigated new soft computing MPPTs, PV array configurations, system architectures and topologies.

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.

Application of modified classical numerical methods for DMPPT on Buck and Boost converters

Application of Classical Numerical Methods (CNM) for Digital maximum power point tracking (DMPPT) confronts many issues. The issues highlighted in this paper include limited range of operation, PV array dependence and accuracy of the initial guess. In order to address such shortcomings of CNM for DMPPT, Hybrid Techniques (HT) have been proposed. The HT are a combination of the modified incremental conductance method (MINC) and various modified CNM. An overview of the considered MCNM, which are applied to the photovoltaic (PV) application, has also been provided. The HT not only address the issues confronted by the CNM, but also improve the transient response time and remove the steady state oscillations for the conventional MPPT technique. In addition, for DMPPT the DC-DC converter topology under consideration cannot be treated as a black box, by ignoring the effects of the converter topology and control dynamics. Here, a theoretical analysis has been provided to ascertain the optimum performance of DMPPT applications on various DC-DC converter designs. To measure the effectiveness of the proposed HT, Boost, 2-Stage Switch Capacitor Based (2-SSC) Boost, and Optimum Buck Converters (OBC) have been employed. Simulation and experimental results are provided to validate the effectiveness of the proposed HT.

Modeling and control of photovoltaic and fuel cell based alternative power systems

Photovoltaic (PV) systems and fuel cells (FCs) represent interesting solutions as being alternative power sources with high performance and low emission. This work presents a modeling and control study of two power generators; photovoltaic array and fuel cell based systems. An MPPT approach to optimize the PV system performances is proposed. The PV system consists of a PV array connected to a DC-DC buck converter and a resistive load. A maximum power point tracker controller is required to extract the maximum generated power. Based on Incremental Conductance (INC) principle, the idea of the proposed control is to use a Fuzzy Logic Controller (FLC) that allows the choice of the duty cycle step size which is used to be fixed in conventional MPPT algorithms. The variable step is computed according to the value of the PV power-voltage characteristic slope. The second working system comprises a controlled DC-DC converter fed by a proton exchange membrane fuel cell (PEMFC) and supplies a DC bus. The mathematical model of the PEMFC system is given. The converter duty cycle is adjusted in order to regulate the DC bus voltage. Obtained simulation results validate the control algorithms for both of studied power systems.