Showing papers on "Maximum power point tracking published in 2021"

Steady Output and Fast Tracking MPPT (SOFT-MPPT) for P&O and InC Algorithms

Abstract: Among the various MPPT algorithms developed until date, Perturb and Observe (P&O) and Incremental conductance (InC) algorithms are most widely used, mainly due to their simplicity and ease of implementation. However, both algorithms suffer from the drawbacks of continuous steady state oscillations, and inefficient tracking in case of rapid changes in irradiation. In this article, a SOFT-MPPT algorithm has been proposed which, in a simple way, aims to improve both the steady state as well as the tracking performance of both P&O and InC algorithms. An adaptive step size is used to track the MPP, which provides a faster tracking performance. The proposed algorithm identifies the attainment of the steady state and then stops the artificial perturbations. This stops the oscillations around the MPP and provides a steady power output from the PV panel at the MPP value. The SOFT-MPPT algorithm is also able to detect any change in operating conditions while operating in the steady state, and that too without using any additional sensors. Accordingly, it restarts the tracking process in order to track the new MPP. The detailed theoretical discussions of the SOFT-MPPT algorithm, is followed by the simulation and experimental results to validate the proposed performance improvements.

An Efficient Fuzzy-Logic Based Variable-Step Incremental Conductance MPPT Method for Grid-Connected PV Systems

Abstract: Recently, solar energy has been intensively employed in power systems, especially using the photovoltaic (PV) generation units In this regard, this paper proposes a novel design of a fuzzy logic based algorithm for varying the step size of the incremental conductance (INC) maximum power point tracking (MPPT) method for PV In the proposed method, a variable voltage step size is estimated according to the degree of ascent or descent of the power-voltage relation For this purpose, a novel unique treatment is proposed based on introducing five effective regions around the point of maximum PV power To vary the step size of the duty cycle, a fuzzy logic system is developed according to the locations of the fuzzy inputs regarding the five regions The developed fuzzy inputs are inspired from the slope of the power-voltage relation, namely the current-voltage ratio and its derivatives whereas appropriate membership functions and fuzzy rules are designed The benefit of the proposed method is that the MPPT efficiency is improved for varying the step size of the incremental conductance method, thanks to the effective coordination between the proposed fuzzy logic based algorithm and the INC method The output DC power of the PV array and the tracking speed are presented as indices for illustrating the improvement achieved in MPPT The proposed method is verified and tested through the simulation of a grid-connected PV system model The simulation results reveal a valuable improvement in static and dynamic responses over that of the traditional INC method with the variation of the environmental conditions Further, it enhances the output dc power and reduce the convergence time to reach the steady state condition with intermittent environmental conditions

Maximum Power Point Tracking Using Modified Butterfly Optimization Algorithm for Partial Shading, Uniform Shading, and Fast Varying Load Conditions

Abstract: In this article, a new maximum power point tracking algorithm based on a modified butterfly optimization algorithm has been proposed. The proposed method is capable of differentiating between different partial shading patterns, uniform shading, solar intensity, and load variation conditions with fast convergence speed (CS). Only one dynamic variable is used as a tuning parameter reducing the complexity of the algorithm. The search space skipping method has been proposed to improve the CS. The proposed method is hybridized with a constant impedance method to improve the response time of the system for fast varying load variations. The proposed method has been validated experimentally on the SEPIC converter topology with a sampling time of 0.05 s. The experimental validation proved the average tracking time for different shading patterns is less than 1 s with steady-state efficiency of 99.85% on average. The CS for uniform shading conditions is improved by 47.20%. The response to load variation is also improved by 86.15% and becomes eligible to be utilized for fast varying load variations. Finally, the comparison table based on the MPPT rating has been presented to determine the effectiveness of the proposed method among other popular metaheuristic approaches used for MPPT.

DMPPT Control of Photovoltaic Microgrid Based on Improved Sparrow Search Algorithm

Abstract: There are some problems in the photovoltaic microgrid system due to the solar irradiance-change environment, such as power fluctuation, which leads to larger power imbalance and affects the stable operation of the microgrid. Aiming at the problems of power mismatch loss under partial shading in photovoltaic microgrid systems, this paper proposed a distributed maximum power point tracking (DMPPT) approach based on an improved sparrow search algorithm (ISSA). First, used the center of gravity reverse learning mechanism to initialize the population, so that the population has a better spatial solution distribution; Secondly, the learning coefficient was introduced in the location update part of the discoverer to improve the global search ability of the algorithm; Simultaneously used the mutation operator to improve the position update of the joiner and avoid the algorithm falling into the local extreme value. The results of the model in Matlab showed that the ISSA can track the maximum power point(MPP) more accurately and quickly than the perturbation observation method (P&O) and the particle swarm optimization (PSO) algorithm, and had good steady-state performance.

Review of Multilevel Inverters for PV Energy System Applications

Abstract: Over the last decade, energy demand from the power grid has increased significantly due to the increasing number of users and the emergence of high-power industries. This has led to a significant increase in global emissions with conventional energy generation. Therefore, the penetration of renewable energy resources into the power grid has increased significantly. Photovoltaic systems have become the most popular resources as their protentional is enormous, thus, the worldwide installed PV capacity has increased to more than 635 gigawatts (GW), covering approximately 2% of the global electricity demand. Power electronics are an essential part of photovoltaic generation; the drive for efficient power electronic converters is gaining more and more momentum. Presently, multilevel inverters (MLI) have become more attractive to researchers compared to two-level inverters due to their abilities to provide lower electromagnetic interference, higher efficiency, and larger DC link voltages. This paper reviews multilevel inverters based on their classifications, development, and challenges with practical recommendations in utilizing them in renewable energy systems. Moreover, PV systems with various maximum power point tracking (MPPT) methods have been extensively considered in this paper as well. The importance and the development of a modified multilevel inverter are also highlighted in this review. In general, this paper focuses on utilizing multilevel inverters for PV systems to motivate and guide society to focus on inventing an efficient and economical multilevel inverter that has the combined capabilities of these converters reported in the literature.

Improved Perturb and Observation Maximum Power Point Tracking Technique for Solar Photovoltaic Power Generation Systems

Abstract: The primary concerns in the practical photovoltaic (PV) system are the power reduction due to the change in operating conditions, such as the temperature or irradiance, the high computation burden due to the modern maximum power point tracking (MPPT) mechanisms, and to maximize the PV array output during the rapid change in weather conditions. The conventional perturb and observation (P&O) technique is preferred in most of the PV systems. Nevertheless, it undergoes false tracking of maximum power point (MPP) during the rapid change in solar insolation due to the wrong decision in the duty cycle. To avoid the computational burden and drift effect, this article presents a simple and enhanced P&O MPPT technique. The proposed technique is enhanced by including the change in current ( dI ), in addition to the changes in output voltage and output power of the PV module. The effect of including the dI profile with the traditional method is explained with the fixed and variable step-size methods. The mathematical expression for the drift-free condition is derived. The traditional boost converter is considered for validating the effectiveness of the proposed methods by employing the direct duty cycle technique. The proposed algorithm is simulated using MATLAB/Simulink and validated under various scenarios with the developed laboratory prototype in terms of drift-free characteristics and tracking efficiency. The result proves that the proposed technique can track the MPP accurately under various operating conditions.

MPPT Algorithm Based on Artificial Bee Colony for PV System

Abstract: Energy structures from non-conventional energy source has become highly demanded nowadays. In this way, the maximum power extraction from photovoltaic (PV) systems has attracted the attention, therefore an optimization technique is necessary to improve the performance of solar systems. This article proposes the use of ABC (artificial bee colony) algorithm for the maximum power point tracking (MPPT) of a PV system using a DC-DC converter. The procedure of the ABC MPPT algorithm is using data values from PV module, the P-V characteristic is identified and the optimal voltage is selected. Then, the MPPT strategy is applied to obtain the voltage reference for the outer PI control loop, which in turn provides the current reference to the predictive digital current programmed control. A real-time and high-speed simulator (PLECS RT Box 1) and a digital signal controller (DSC) are used to implement the hardware-in-the-loop system to obtain the results. The general system does not have a high computational cost and can be implemented in a commercial low-cost DSC (TI 28069M). The proposed MPPT strategy is compared to the conventional perturb and observe method, results show the proposed method archives a much superior performance.

A Comparative Study on Photovoltaic MPPT Algorithms Under EN50530 Dynamic Test Procedure

Abstract: Dynamic performance of maximum power point tracking (MPPT) algorithms is important to ensure high-power output under practical operating conditions. In this article, after reviewing three dynamic test procedures, including stepped operation procedure, day-by-day operation procedure, and EN50530 dynamic test procedure, three typical MPPT algorithms such as the fixed-step-size perturb and observe (P&O), variable-step-size incremental conductance, and hybrid-step-size beta method are evaluated experimentally under the EN50530 dynamic test procedure. Two dynamic EN50530 test sequences are adopted for the performance evaluation to cover different irradiance changing conditions. The PV model for EN50530 dynamic test sequences is built, and the effects of wrong-step changes by using three MPPT algorithms are analyzed systematically. The experimental comparison of three MPPT algorithms in terms of the tracking routines, accumulated energy, and tracking efficiency is presented. The research shows that the 0.5% fixed-step-size P&O may fail to track the MPP due to the tracking drift, whereas the beta algorithm exhibits the highest tracking efficiency under both dynamic sequences. The average tracking efficiency improvement of the beta algorithm compared with other two algorithms are experimentally measured as $\text{24.2}\%$ and $\text{18.8}\%$ , respectively.

Two-Level Combined Control Scheme of VSC-MTDC Integrated Offshore Wind Farms for Onshore System Frequency Support

Abstract: This paper proposes a two-level combined control (TLCC) scheme of voltage source converter-based multi-terminal high-voltage direct current (VSC-MTDC) integrated offshore wind farms to provide frequency support for onshore system. The proposed TLCC scheme consists of two levels, which are the step start-up and adaptive inertial droop control of the offshore wind turbine level, and the communication-free allocation control of the onshore VSC station level. On the first level, each wind turbine adopts the inertial and droop control with adaptive coefficients, and all wind turbines (WTs) work at the maximum power point tracking (MPPT) mode without energy reserve. To reduce the second frequency drop (SFD), the WTs are divided into different clusters according to their rotor speed, and a step start-up control scheme is adopted for the WT clusters to provide frequency support sequentially. On the system level, the communication-free allocation control strategy is proposed using local frequency signal of onshore VSC stations to share the active power among onshore VSC stations reasonably. The proposed TLCC scheme can provide onshore system with frequency support and reduce the SFD simultaneously, while all WTs work at MPPT mode. Case studies are carried out on a 3-area 4-terminal VSC-MTDC based offshore wind farms (OWFs). Simulation results demonstrate the effectiveness and universality of the proposed TLCC scheme under different scenarios.

Promising MPPT Methods Combining Metaheuristic, Fuzzy-Logic and ANN Techniques for Grid-Connected Photovoltaic.

Abstract: This paper addresses the improvement of tracking of the maximum power point upon the variations of the environmental conditions and hence improving photovoltaic efficiency Rather than the traditional methods of maximum power point tracking, artificial intelligence is utilized to design a high-performance maximum power point tracking control system In this paper, two artificial intelligence-based maximum power point tracking systems are proposed for grid-connected photovoltaic units The first design is based on an optimized fuzzy logic control using genetic algorithm and particle swarm optimization for the maximum power point tracking system In turn, the second design depends on the genetic algorithm-based artificial neural network Each of the two artificial intelligence-based systems has its privileged response according to the solar radiation and temperature levels Then, a novel combination of the two designs is introduced to maximize the efficiency of the maximum power point tracking system The novelty of this paper is to employ the metaheuristic optimization technique with the well-known artificial intelligence techniques to provide a better tracking system to be used to harvest the maximum possible power from photovoltaic (PV) arrays To affirm the efficiency of the proposed tracking systems, their simulation results are compared with some conventional tracking methods from the literature under different conditions The findings emphasize their superiority in terms of tracking speed and output DC power, which also improve photovoltaic system efficiency

Most Valuable Player Algorithm based Maximum Power Point Tracking for a Partially Shaded PV Generation System

Abstract: Inclusion of bypass diodes at the output terminal of the PV array mitigates the effect of partial shading (PS) but causes multiple peaks of power at the output. The conventional hill climbing and perturb and observe algorithms cannot track the optimal point during partial shading phenomena for multiple peaks corresponding to the different shading pattern on the Power-Voltage (P-V) curve. Fuzzy logic controller and artificial neural network-based methods for Maximum Power Point Tracking (MPPT) provide satisfactory results but at the cost of increased memory and computational burden. Recent work to incorporate exploration and exploitation phenomena of nature-inspired algorithms to track optimal power point have shown encouraging results by preventing convergence to local maxima and posing less burden on the processor. However, due to performance variation between different algorithms of this category newer algorithms with improved performances are still a requirement. In this paper, a novel most valuable player algorithm (MVPA) has been used to track the optimal operation point for extracting maximum power from a solar PV system. The algorithm's performance is compared with the commonly employed particle swarm optimization (PSO) and the recently proposed Jaya algorithm's modified form. It is observed that the proposed algorithm outperformed both the algorithms with a considerable improvement in terms of tracking speed, power tracking efficiency, robustness, faster decision for convergence after tracking the maximum power and lesser number of power fluctuations for different shading patterns.

Analysis, Design, and Implementation of a Differential Power Processing DMPPT With Multiple Buck–Boost Choppers for Photovoltaic Module

Abstract: Submodule mismatch has become the major cause of losses in the photovoltaic (PV) power generation system, which has been an important factor restricting the development of PV technology. Differential power processing (DPP) architecture is increasingly employed in PV distributed MPPT (DMPPT) due to its low processed power. In order to improve the PV utilization efficiency, minimize the power loss and reduce the system cost, a novel scheme based on DPP is introduced in this article. The proposed DPP converter is composed of multiple buck–boost choppers, and a multidimensional perturb and observe (MPO) algorithm is proposed to achieve true DMPPT through correcting the voltage of each submodule to its voltage at the maximum power point by using a single current sensor. A prototype with multiple buck–boost choppers for 36 V/200 W PV module is designed and built to verify the operating principle and evaluate the performance of the proposed DPP converter and DMPPT strategy, and the experimental results show that the prototype is low-cost and effectively improves system efficiency.

Coordinated Power Management and Control of Standalone PV-Hybrid System With Modified IWO-Based MPPT

Abstract: To augment the photovoltaic (PV) power generation conversion, a maximum power point tracking (MPPT) technique plays a very significant role. This article introduces a hybrid MPPT algorithm integrating modified invasive weed optimization (MIWO) and perturb and observe (P&O) technique under the rapid weather change and partial shading scenarios for the efficient extraction of the maximum power from the standalone PV-based hybrid system. MIWO handles the initial stages of MPPT followed by the application of the P&O algorithm at the final stages in view of acquiring the rapid global peak and maximal PV power. The studied microgrid comprises of the PV system, battery, electrolyzer, fuel cell, and load. A coordinated dc-voltage regulation and power management strategy between each subsystem of the hybrid microgrid is implemented to save the battery from the undesirable charging/discharging operation. Additionally, with the monitoring of dc voltage, the dc/dc converter associated with the battery and dc link plays as an MPPT circuit of the PV without the requirement of an extra dedicated circuit. Takagi–Sugeno (TS) fuzzy controller is adopted for suppressing/mitigating the voltage oscillations of the microgrid during the variations in the solar irradiance/temperature and power demand. The results clearly exhibit the superior performance of the proposed methodology compared with some of the existing techniques.

An Improved Cuckoo Search Algorithm for Maximum Power Point Tracking of Photovoltaic Systems under Partial Shading Conditions

Abstract: The problem of partial shading has serious effects on the performance of photovoltaic (PV) systems. Adding a bypass diode in shunt to each PV module avoids hot-spot phenomena, but causes multi-peaks in the power–voltage (P–V) characteristics of the PV array, which cause traditional maximum power point tracking (MPPT) techniques to become trapped in local peaks. This problem has forced researchers to search for smart techniques to track global peaks and prevent the possibility of convergence at local peaks. Swarm optimization techniques have been used to fill this shortcoming; unfortunately, however, these techniques suffer from unacceptably long convergence time. Cuckoo search (CS) is one of the fastest and most reliable optimization techniques, making it an ideal option to be used as an MPPT of PV systems under dynamic partial shading conditions. The standard CS algorithm has a long conversion time, high failure rate, and high oscillations at steady state; this paper aims to overcome these problems and to fill this research gap by improving the performance of the CS. The results obtained from this technique are compared to five swarm optimization techniques. The comparison study shows the superiority of the improved CS strategy introduced in this paper over the other swarm optimization techniques.

A Comparative Performance Analysis of ANN Algorithms for MPPT Energy Harvesting in Solar PV System

Abstract: In this paper, artificial neural network (ANN) based Levenberg-Marquardt (LM), Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG) algorithms are deployed in maximum power point tracking (MPPT) energy harvesting in solar photovoltaic (PV) system to forge a comparative performance analysis of the three different algorithms. A comparative analysis among the algorithms in terms of the performance of handling the trained dataset is presented. The MATLAB/Simulink environment is used to design the maximum power point tracking energy harvesting system and the artificial neural network toolbox is utilized to analyze the developed model. The proposed model is trained with 1000 dataset of solar irradiance, temperature, and voltages. Seventy percent data is used for training, while 15% data is employed for validation, and 15% data is utilized for testing. The trained datasets error histogram represents zero error in the training, validation, and test phase of data matching. The best validation performance is attained at 1000 epochs with nearly zero mean squared error where the trained data set is converged to the best training results. According to the results, the regression and gradient are 1, 1, 0.99 and 0.000078, 0.0000015739 and 0.26139 for Levenberg-Marquardt, Bayesian Regularization and Scaled Conjugate Gradient algorithms, respectively. The momentum parameters are 0.0000001 and 50000 for Levenberg-Marquardt and Bayesian Regularization algorithms, respectively, while the Scaled Conjugate Gradient algorithm does not have any momentum parameter. The Scaled Conjugate Gradient algorithm exhibit better performance compared to Levenberg-Marquardt and Bayesian Regularization algorithms. However, considering the dataset training, the correlation between input-output and error, the Levenberg-Marquardt algorithm performs better.

A modified Marine Predator Algorithm based on opposition based learning for tracking the global MPP of shaded PV system

Abstract: Under partial shading condition, the power-voltage curve of the photovoltaic (PV) system contains several maximum power points (MPPs). Among these points, there is only single global and some local points. Accordingly, modern optimization algorithms are highly required to tackle this problem. However, the methods are considered as time consuming. Therefore, finding a new algorithm that capable to solve the problem of tracking global maximum power point (GMPP) with minimum number of population is highly appreciated. Several new straightforward methods as well as meta-heuristic approaches are exist. Recently, the Marine Predator Algorithm (MPA) has been developed for engineering applications. In this study, an alternative method of MPA, integrating Opposition Based Learning (OBL) strategy with Grey Wolf Optimizer (GWO), named MPAOBL-GWO, is proposed to cope with the implied weaknesses of classical MPA. Firstly, Opposition Based Learning (OBL) strategy is adopted to prevent MPA method from searching deflation and to obtain faster convergence rate. Besides, the GWO is also implemented to further improve the swarm agents’ local search efficiency. Due to that, the MPA explores the search space well better than exploiting it; so, this combination improves the efficiency of the MPA and avoids it from falling in local points. To verify the effectiveness of the enhanced method, the well-known CEC’17 test suite and the maximum power point tracking (MPPT) of photovoltaic (PV) system problem are solved. The obtained results illustrate the ability of the proposed MPAOBL-GWO based method to achieve the optimum solution compared with the original MPA, GWO and Particle Swarm Optimization (PSO). The findings revealed that, the proposed method can be viewed as an efficient and effective strategy for more complex optimization scenarios and the MPPT as well.

Optimized Fuzzy Controller for MPPT of Grid-connected PV Systems in Rapidly Changing Atmospheric Conditions

Abstract: Due to nonlinear behavior of power production of photovoltaic (PV) systems, it is necessary to apply the maximum power point tracking (MPPT) techniques to generate the maximum power. The conventional MPPT methods do not function properly in rapidly changing atmospheric conditions. In this study, a fuzzy logic controller (FLC) optimized by a combination of particle swarm optimization (PSO) and genetic algorithm (GA) is proposed to obtain the maximum power point (MPP). The proposed FLC uses the ratio of power variations to voltage variations and the derivative of power variations to voltage variations as inputs and uses the duty cycle as the output. The range of changes in fuzzy membership functions and fuzzy rules are proposed as an optimization problem optimized by the PSO-GA. The proposed design is validated for MPPT of a PV system using MATLAB/Simulink software. The results indicate a better performance of the proposed FLC compared to the common methods.

A High-Performance Global Maximum Power Point Tracker of PV System for Rapidly Changing Partial Shading Conditions

Abstract: This article proposes a fast and efficient maximum power point tracker (MPPT) for photovoltaic (PV) systems under rapidly changing partial shading conditions. An intelligent mechanism is adopted to systematically schedule the search for the global maximum power point (GMPP) on the P–V curve. As a result, the voltage track, i.e., the path length that the operating point traverses along the voltage axis of the curve (until it converges to GMPP), is reduced. The search region is further minimized using a novel skipping scheme, where the voltage section that does not contain GMPP is discarded. The superiority of the proposed scheme is evaluated against two recent algorithms, namely, the maximum power trapezium and the flower pollination MPPT. The performance is analyzed in terms of convergence time, voltage track, and transient efficiency. The MATLAB simulation is verified experimentally using a PV array simulator, in conjunction with a buck–boost converter. The competing MPPT algorithms are implemented using the TMS320F240 DSP on the dSPACE DS1104 platform. The results indicate that under the same operating and shading conditions, the proposed scheme is the fastest and most reliable and exhibits the highest overall transient efficiency.

A Day and Night Operational Quasi-Z Source Multilevel Grid-Tied PV Power System to Achieve Active and Reactive Power Control

Abstract: A day and night operational single-phase energy stored quasi-Z-source-cascaded H-bridge (ES-qZS-CHB) inverter photovoltaic (PV) power system to achieve the active and reactive power control is proposed in this article. The ES-qZS-CHB inverter PV power system usually employs the unity power factor control method to ensure the output current tracking the desired reference in phase with the grid voltage, combining with distributed maximum power point tracking (MPPT) to determine the values of shoot-through duty ratios Dn . These cannot operate at night because there is no PV power input. Meanwhile, as multiple combinations of Dn and modulation ratio Mn could achieve the same voltage gain at night operation, but which is optimal has not been addressed. This article proposes a solution to address these issues. First, a comprehensive control scheme, which not only has the attractiveness of day and night operation but also shows the advantage of active and reactive power control simultaneously, is proposed; then, an optimization control method is proposed for night operational ES-qZS-CHB inverter to achieve optimal combination of Dn and Mn . Simulation and experimental results validate the day and night operational ES-qZS-CHB inverter and the proposed optimal control technique.

A comprehensive review and classified comparison of MPPT algorithms in PV systems

Abstract: One of the most available energy sources in the world is solar energy, while in the category of renewable and nonrenewable energies is in the first group. Power generation of a photovoltaic (PV) system is a technique which is possible by using solar cells. Since photovoltaic systems cannot force solar cells to operate at MPP, a controller is needed to do so. If the controller can operate more accurately, or in other words, be optimized, the system will have an appropriate output. Many papers have been presented on maximum power point tracking algorithms. This paper intends to review the previous articles and provide a proper division, performance method. This explains the performance, application, advantages and disadvantages of algorithms to be a good reference for selecting the appropriate algorithm. Algorithms in this paper are divided into four categories methods based on measurement, calculation, intelligent schemes and hybrid schemes. The exhibition of new algorithms and the optimization of previous algorithms have led to the number of articles in this field over the years. In order to review the methods a comparative table is also provided. Finally, a PV system has been controlled by using three algorithms P&O, IC and Fuzzy-PI. The outputs control signals from the MPPT have been applied by Boost and SEPIC converters, and the outputs have been compared. Simulations have been performed in MATLAB/Simulink software.

Novel Spline-MPPT Technique for Photovoltaic Systems Under Uniform Irradiance and Partial Shading Conditions

Abstract: Different maximum power point tracking (MPPT) techniques used in photovoltaic (PV) systems are evaluated based on several criteria such as simplicity, speed, and accuracy. There are tradeoffs among these criteria, and generally higher accuracy is achieved at the expense of speed and simplicity. This article aims to introduce Spline-MPPT technique as a fast, accurate, and uncomplicated method to find the maximum power point of PV systems under uniform irradiance and partial shading condition (PSC) in which characteristics of the PV string are distorted. The proposed method is based on cubic spline interpolation that defines an approximate function for a few sample points. Several interpolation-based methods have been proposed in the literature to find MPP under uniform irradiance. They, however, are incapable of finding the global maximum power point (GMPP) under partial shading conditions. The Spline-MPPT technique only uses a small number of current and voltage samples to estimate the MPP of the system and maintain on this point as long as environmental conditions remain unchanged. Simulation results attest to the superiority of the proposed method.