Conventional Maximum Power Point Tracking Algorithms Research Articles

Optimal power generation of proton exchange membrane fuel cell using ANFIS based MPPT algorithm

Fuel cells are the most promising energy source for the future energy demand. The automobile industry is looking at the integration of fuel cells with electric vehicles (EV). This integration comes with many challenges like dynamic operational behaviors. For operating the fuel cell with maximum efficiency, this work proposes an Adaptive Neuro Fuzzy Inference System (ANFIS) based Maximum Power Point Tracking (MPPT) method. The hydrogen flow rate, pressure and stack temperature are the parameters considered to track the maximum power point of the fuel cell. The ANFIS-MPPT algorithm has been integrated with the 1.26 kW fuel cell in MATLAB/Simulink® and validated in different scenarios like dynamic variation in hydrogen pressure, stack temperature, load variation. The performance has been observed and compared with the conventional MPPT algorithms of Perturb and Observe (P&O) algorithm and Incremental Conductance (InC) algorithm. The proposed ANFIS-MPPT algorithm improves the power stability by 10–15% than the P&O and InC methods. Also, the proposed ANFIS-MPPT has 30% faster response as compared to the P&O algorithm, and 23% than the InC algorithm. From the analysis, it is observed that the ANFIS, P&O and InC methods are having the response time of 2.5 s, 3.6 s and 4.5 s respectively. Also the ANFIS method delivers the maximum power output of 1.26 kW, whereas the P&O and InC deliver 1.13 kW, 1.19 kW respectively. The detailed simulation analysis and results are presented in this paper.

A Coronavirus Optimization (CVO) algorithm to harvest maximum power from PV systems under partial and complex partial shading conditions

Power generation from solar photovoltaic (PV) systems is employed due to its recyclable nature, limitless source of energy, low maintenance, ease of installment, and eco-friendly potential. There is a major challenge in the PV system to harvest the maximal power in varying weather scenarios which decreases the overall efficiency and causes significant power loss. Maximum power point tracking (MPPT) methods are exploited to crop the maximum power from PV strings. Conventional algorithms do not find the Global Maxima (GM) and are trapped on any Local Minima (LM). However, bio-inspired methods find the GM but take more time to search the GM. This paper presents a Corona-Virus Optimization (CVO) algorithm to harvest maximum power against seven well-known conventional and bio-inspired MPPT algorithms. The proposed technique is estimated for Uniform Irradiance (UI), Partial Shading (PS), and Complex Partial Shading (CPS) conditions. Furthermore, the comparative analysis between seven already implemented and proposed techniques shows that the CVO algorithm lessens the unwanted oscillation and quicker searching the GM. Moreover, their numerical analysis is also conducted to evaluate the stability, robustness, performance, and sensitivity of the CVO algorithm. The proposed technique gives the highest efficiencies of 99.99%, 99.98%, 99.97%, and 99.98% in all cases of UI, PS-1, PS-2, and CPS respectively when comparing with P&O, InC, DFO, CS, FFO, PSO, and ACO techniques. Real-time data from the Beijing database is also used to verify the performance of the proposed algorithm.

Investigating the trade-off between response time and complexity in the Levenberg–Marquardt ANN-MPPT algorithm used in wind energy conversion systems

The integration of artificial intelligence (AI) models in renewable energy resources management, particularly in the utilization of maximum power point tracking (MPPT) optimizers, has gained significant attention. This study focuses on investigating the tradeoff between accuracy, response time, and system complexity by varying the number of neurons in artificial neural network (ANN) models for MPPT in wind energy conversion systems (WECSs). Traditionally, MPPT algorithms in WECSs are implemented using direct or indirect methods. However, these methods lack an accumulative learning curve and rely on instantaneous inputs. In contrast, ANN models trained on pre-existing datasets offer the potential for improved maximum point capturing processes. Nevertheless, the incorporation of ANN models may introduce additional complexity to the system. Two ANN models, direct and indirect, are examined in comparison to a reference model using the perturb and observe conventional MPPT algorithm. The results show that the ANN direct model exhibits better time response in the face of high variations in wind speed profiles. On the other hand, the ANN indirect model demonstrates a 4% increase in accuracy with minimal ripples.

A coordinated control hybrid MPPT algorithm for a grid-tied PV system considering a VDCIQ control structure

In this paper, a new coordinated maximum power point tracking (MPPT) algorithm has been developed for a grid-tied PV system, whose inverter follows a VDCIQ control scheme. The control objectives of this system are shared between 2 converters: a DC boost converter which performs MPPT of the PV plant, and an inverter which is responsible for DC voltage setpoint control, specific reactive current injection under request and reduced harmonic content of AC grid currents. The proposed algorithm operates upon a proper switching amongst conventional MPPT algorithms, namely perturb and observe (P&O) and incremental conductance (IC) algorithms, to take advantage of the best characteristics of each MPPT method with a different step size and considering the influence of the inverter control constants. Two coordination schemes are proposed for this algorithm to prioritise the improvement of different performance aspects over others. The impact of the proposed algorithm according to the 2 coordination schemes is evaluated and compared with the impact of conventional MPPT algorithms according to the trackability of power, the impact on DC voltage and on the AC grid side. The results are analysed by simulations conducted in MATLAB-Simulink.

Solar irradiance estimation and optimum power region localization in PV energy systems under partial shaded condition

The efficient operation of PV systems relies heavily on maximum power point tracking (MPPT). Additionally, such systems demonstrate complex behavior under partial shading conditions (PSC), with the presence of multiple maximum power points (MPP). Among the existing MPPT algorithms, the conventional perturb and observe, and incremental conductance stand out for their high simplicity. However, they are specialized in single MPP problems. Thus, due to the existence of multiple MPPs under PSC, they fail to track the global MPP. Compared with the conventional schemes, the modified conventional algorithms, and several existing MPPT variants introduce a trade-off between complexity and performance. To enhance the simplicity of the PV system, it is crucial to adapt the operation of the simple conventional algorithm to scenarios under PSC. To achieve such an adaptation, the power-voltage curve that conventionally admits multiple MPPs under PSC must be converted to an equivalent curve having only a single MPP. To address such a requirement, this paper introduces a novel approach to the fast determination of the MPP. A consistent methodology for reducing the complex multiple MPP problem of PV systems under PSC, to a single MPP objective, is put forward. Thus such reduction enhances the tracking environment for simple conventional MPPT algorithms under partial shading. Studies of the PV array behavior for 735 partial shading patterns revealed an interesting possibility of reducing the classical PV curve to 8.2620% of its actual area. The newly established area is an optimum power region that accommodates a single MPP. To arrive at such a reduction, an intelligent neural network-based predictor, incorporating a cost-effective and reliable solar irradiance estimator is put forward. Unlike existing methods, the approach is free from the direct and expensive measurement of solar irradiance. The predictor relies on the PV array current and voltage only to precisely determine the optimum power region of the PV system.

Maximum Power Point Tracking Algorithm of Photo-Voltaic Array through Determination of Boost Converter Conduction Mode

In this paper, we present a new maximum power point tracking (MPPT) algorithm that can identify whether a boost converter is operating in continuous conduction mode (CCM) or discontinuous conduction mode (DCM). The conventional MPPT algorithm assumes that the converter is always in CCM mode, even though this is not always the case. The converter can enter DCM mode due to factors such as the inductor size, irradiance and temperature conditions, voltage step size of the algorithm, and operating point of the PV array. In the proposed work, the conduction mode of a boost converter is evaluated under different conditions. The region of the I–V curve where the converter is likely to operate in DCM mode is identified and a mathematical expression developed in this work is then used to detect the conduction mode of the converter. The proposed algorithm incorporates this expression into a modified perturb and observe (P&O) algorithm. In each iteration, the algorithm first detects the conduction mode of the converter. If the converter is in DCM mode, the algorithm takes a large voltage step to force the converter back into CCM mode, i.e., into the constant current region. The proposed MPPT algorithm was tested using simulation experiments, and the results show that the proposed algorithm can significantly improve the efficiency of the MPPT process.

Experimental validation of an advanced metaheuristic algorithm for maximum power point tracking of a shaded photovoltaic system: A comparative study between three approaches

Under partially shaded conditions, the power–voltage characteristic curve of the Photovoltaic System (PVS) presents more than one peak, so the Global Maximum Power Point (GMPP) cannot be detected using the conventional Maximum Power Point Tracking (MPPT) algorithms, such as the Perturb-and-Observe (P&O) algorithm. In order to overcome the limitations of the conventional MPPT algorithms, this paper suggests a metaheuristic MPPT called the Crow Search Algorithm (CSA) for the performance optimization of a standalone PVS. The CSA algorithm has the capability of attenuating the negative effects of the partial shading on the performance of the PVS by the accurate detection of the GMPP. The principle of the latter algorithm uses the crow skills and behaviors in the process of locating places to hide its food. Relative to other metaheuristic methods, the CSA utilizes only two tuning parameters that combine between simplicity of implementation and good efficiency. The simulation and experimental results under partial shading applications demonstrates the better performance of the suggested CSA algorithm compared to the particle swarm optimization and P&O algorithms. In fact, the comparison is carried out in terms of high efficiency, good accuracy, low convergence time and simplicity of implementation. Indeed, the proposed CSA-based MPPT approach extracts the maximum power produced by the PVS with an estimated average efficiency of 99.87%, whereas the PSO and P&O methods record average efficiencies of 99.39% and 95.23%, respectively. Furthermore, as compared to the PSO and P&O MPPT methods, the suggested CSA-based MPPT approach reduces convergence time by an average of 48.41% and 49.63%, respectively.

Emperor Penguin Optimization Based MPPT for PV System under Different Irradiation Condition

—Maximum Power Point Tracking (MPPT) is an essential process in the PV system to generate maximum power. The power generated in the system fluctuates due to the different irradiation levels from PV modules. Because of this effect, the Maximum Power (MP) produced has to be tracked with a minimum tracking time. With the conventional MPPT algorithm, the MP cannot be tracked with different irradiations, and the tracking time will be high. In this article, a 4x4 PV array is modeled to compare with different irradiation conditions. The Emperor Penguin Optimization (EPO) based MPPT is implemented to find the global maximum power. The maximum peak power is tracked under different irradiation conditions in a total cross tied (TCT) configured network. The MP tracked in the 4 × 4 TCT-PV array is contrasted with the bridge linked (BL) and serial parallel (SP) and bridge linked honey-comb (BL-HC) configuration. The proposed method yields approximately 4% improved power tracking compared to the other configuration.

Sensorless fuzzy logic controller (FLC) based maximum power point tracking (MPPT) algorithm for hydrokinetic energy harnessing

This paper presents a sensor-less control strategy based on the Fuzzy Logic Controller (FLC) Maximum Power Point Tracking Algorithm (MPPT) for stand-alone hydrokinetic energy harnessing in the river. The FLC MPPT algorithm was proposed due to the variation of the water velocity and the weakness of the conventional MPPT algorithm, such as the Perturb and Observe (P&O) MPPT algorithm under highly nonlinear water velocity. The hydrokinetic comprises a water turbine, permanent magnet synchronous generator (PMSG), passive rectifier, and DC boost converter. In this work, MATLAB/Simulink has been implemented to design the circuit topology for hydrokinetic technology. Then, the modeling of the complete system has been simulated under the different speeds of water velocities. The proposed FLC MPPT has been compared to the conventional P&O MPPT algorithm regarding power efficiency and tracking response time. The results show that the proposed FLC MPPT algorithm achieved an average 93 % efficiency in extracting the output power and a 35.49 % faster response time than the P&O MPPT algorithm.

Hybrid Maximum Power Tracking Technique for Partially Shaded PV Arrays

Photovoltaic panels are the most important green energy source because of their inexhaustible moreover its clean. It is important to connect the PV panels to the maximum power point tracking (MPPT) controller to optimize its output power. The PV panels output power efficiency is related to the rapidly variation of the incident irradiance moreover the partial shading pattern. The variation of the incident irradiance and the partial shading pattern makes the tracking of the global maximum peak (GMP) through the local ones too difficult, which extremely decrease the efficiency of the PV panels. The problem is the GMP value varies as the sun irradiance varies so; the detection of the GMP needs an efficient and fast algorithm which cannot be done by the traditional MPPT. In order to solve the problem, proposed system a hybrid new algorithm can combine a traditional MPPT algorithm, such as perturb and observe, or incremental conductance, with the ANN (artificial neural network) This new algorithm can combine a traditional MPPT algorithm, The proposed hybrid MPPT algorithm is based on the ANN and used to predict the global MPP region by estimating its voltage boundaries. Consequently, the conventional MPPT algorithm searches for the MPP in the predicted region. The proposed technique is modeled and simulated using MATLAB/Simulink.

A computationally efficient jaya optimization for fuel cell maximum power tracking

ABSTRACT Fuel cells typically exhibit non-linear, convex P − I characteristics with a single peak power-point for a constant temperature, membrane water content (MWC), hydrogen gas, and oxygen partial pressure. In this paper, a Jaya algorithm-based maximum power point tracking (MPPT) is developed for fast and accurate peak power tracking of a proton exchange membrane fuel cell (PEMFC). Most of the conventional MPPT algorithms are prone to continual steady-state oscillations. Further, most meta-heuristic MPPT algorithms use a PID controller to track the peak power-point. The use of combined meta-heuristic and PID controller affects the efficiency of MPPT since it is strongly dependent on PID controller gains and meta-heuristic optimization parameters. In this paper, a Jaya algorithm-based MPPT tracking approach without a PID controller is developed to fulfill the MPP of a PEMFC. The Jaya algorithm-based MPPT solution ensures a global maximum peak power-point solution that is independent of solver parameters. The efficacy of the proposed Jaya MPPT is evaluated by performing various simulation studies under various operating conditions with different perturbations and compared against widely accepted particle swarm optimization (PSO), conventional perturb and observe (P&O)-based MPPT techniques. The proposed method can track a maximum power of 1411.02 W within two iterations as compared to method particle swarm optimization (PSO), conventional perturb and observe (P&O), which could track a maximum power of 1376.11 W and 1370.4 W, respectively. Thus, giving an additional increase in power efficiency 2.53% Jaya algorithm. Furthermore, the proposed approach delivered an improved output power efficiency of 11.28% compared to the fuel cell operation without MPPT. Further, the real-time feasibility of the proposed algorithm is also validated by developing a hardware prototype and performing various case studies to track MPPT under different operating conditions and perturbations.

Investigation of Adaptive Intelligent MPPT Algorithm for a Low-cost IoT Enabled Standalone PV System

ABSTRACT This paper explicates a standalone solar photovoltaic system design to track maximum power by utilising intelligent adaptive control algorithms. Conventional MPPT algorithms are not efficient enough to follow the maximum power variable irradiation and temperature conditions. Therefore, an intelligent algorithm has been required to extract the maximum power in a standalone PV system. The present paper incorporates adaptive intelligent maximum power point tracking (MPPT) method adaptive neuro-fuzzy inference system (ANFIS) techniques to extract maximum voltage and power. The fuzzy logic controller (FLC) has been implemented to analyse the performance compared to the ANFIS method. Because of the utilisation of conventional techniques, the point of maximum power gets oscillated in a low irradiance level and the values move between forward and backwards but do not have a fixed value. The used ANFIS method takes all the possibility values from 0 to 1, increasing efficiency. The efficiency of the ANFIS-based MPPT method is 90% more accurate than those of other conventional methods, which has been presented in the paper. For the remote monitoring of the obtained voltage, current and power, internet of things (IoT) features have been incorporated into the considered standalone PV system. The presented standalone PV system has been experimentally verified and validated for the efficiency evaluation of the proposed ANFIS algorithm.

A New Improved Variable Step Size MPPT Method for Photovoltaic Systems Using Grey Wolf and Whale Optimization Technique Based PID Controller

In this work, we have developed two new intelligent maximum power point tracking (MPPT) techniques for photovoltaic (PV) solar systems. To optimize the PWM duty cycle driving the DC/DC boost converter, we have used two optimization algorithms namely the whale optimization algorithm (WOA) and grey wolf optimization (GWO) so we can tune the PID controller gains. The oscillation around the MPP and the fail accuracy under fast variable isolation are among the well-known drawbacks of conventional MPPT algorithms. To overcome these two drawbacks, we have formulated a new objective fitness function that includes WOA/GWO based accuracy, ripple, and overshoot. To provide the most relevant variable step size, this objective fitness function was optimized using the two aforementioned optimization algorithms (i.e., WOA and GWO). We have carried out several tests on Solarex MSX-150 panel and DC/DC boost converter based PV systems. In the simulation results section, we can clearly see that the two proposed algorithms perform better than the conventional ones in term of power overshoot, ripple and the response time.

Application of mutant particle swarm optimization for MPPT in photovoltaic system

<span>The P –V characteristic of a photovoltaic system (PVs) is non-linear and de-pends entirely on the extreme environmental condition, thus a large amount PV energy is lost in the environment. To enhance the operating efficiency of the PVs, a maximum power point tracking (MPPT) controller is normally equipped in the system. This paper proposes a new mutant particle swarm optimization (MPSO) algorithm for tracking the maximum power point (MPP) in the PVs. The MPSO-based MPPT algorithm not only surmounts the steady-state oscillation (SSO) around the MPP, but also tracks accurately the optimum power under different varying environmental conditions. To demonstrate the effectiveness of the proposed method, MATLAB simulations are implemented in three challenging scenarios to the PV system, including changing irradiation, load variation and partial shading condition (PSC). Furthermore, the obtained results are compared to some of the con-ventional MPPT algorithms, such as incremental conductance (INC) and clas-sical particle swarm optimization (PSO) in order to show the superiority of the proposed approach in improving the efficiency of PVs. </span>

GMPPT by using PSO based on Lévy flight for photovoltaic system under partial shading conditions

The photovoltaic (PV) system contemplated in the study displays multiple peaks on power-voltage ( P - V ) curve under partial shading condition (PSC) results in a complicated maximum power point tracking (MPPT) process. Conventional MPPT algorithms work in an effective manner under uniform irradiance conditions. However, these algorithms are unable to track the global peak effectively under different irradiance conditions. In this study, a velocity of particle swarm optimisation-based Levy flight (VPSO-LF) for Global MPPT of PV system under PSCs is proposed. For the changes in irradiance, when verified with VPSO-LF, tracking time and a number of iterations are fewer to reach the global peak of PV array. It also minimises the number of tuning parameters of the velocity of particle swarm optimisation (PSO). The proposed technique is simulated in MATLAB/SIMULINK as well as experimentally validated. It is observed that the results obtained using VPSO-LF is superior to conventional PSO and hill-climbing algorithm under different patterns of PV array.

Asymmetrical interval type-2 fuzzy logic control based MPPT tuning for PV system under partial shading condition

The conventional maximum power point tracking (MPPT) algorithm shows best performance under uniform insolation but when photovoltaic (PV) array is partially irradiated, the Power vs Voltage (P–V) plot consists of multiple local maxima power point (LMPP) and one global maxima power point (GMPP). The conventional MPPT algorithm may track local peak and fluctuate around it, resulting in lower power tracking. To eradicate this drawback of conventional algorithm, the solar PV system requires the synthesis of modified controller which is able to discriminate between local and global peak point. Along with implementing modified MPPT controller, to minimise the adverse effect of partial shading on PV system, different PV array arrangements like series-parallel (SP), honey comb (HC), total cross tied (TCT) etc. may be used. Author(s) in the present study, has proposed asymmetrical interval type-2 fuzzy logic control (IT-2 AFLC) based MPP algorithm for tracking global peak in partial shading condition (PSC) with different PV array arrangements. The presented algorithm has been compared with other approaches viz. perturb & observe (P&O) and type-1(T-1) FLC for GMPP tracking, fill factor, shading losses, mismatch loss and efficiency to establish its superiority. For evaluating the efficiency of different algorithms, the EN50530 MPPT efficiency test has been performed under dynamic condition. The proposed algorithm has been developed under MATLAB/Simulink environment.

An Adaptive Resistance Perturbation Based MPPT Algorithm for Photovoltaic Applications

This paper proposes a Resistance perturbation based maximum power point tracking (MPPT) with an adaptive control limit algorithm to extract the maximum power from solar photovoltaic (PV) array. This algorithm consists of two main functions, namely 1) resistance perturbation & observation (RP&O) and 2) adaptive resistance control (ARC) limit. The RP&O operates the PV array at maximum power point (MPP), and the ARC limit continuously monitors the resistance of the PV R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pv</sub> to determine the operating limit of MPP. The ultimate aim of proposing this algorithm is to reduce the oscillations and improve MPP's tracking performance for sudden variation in temperature and irradiance conditions. Furthermore, it does not require an expensive pyranometer or temperature sensor to track the MPP of the PV array. This paper also compares the proposed and conventional MPPT algorithm's performance. Its validation results in both MATLAB/Simulink and experimental studies are presented under constant and sudden changes in irradiance conditions.

DESIGN AND PERFORMANCE ASSESSMENT OF HYBRID-MAXIMUM POWER POINT TRACKING ALGORITHM

There is unprecedented interest in renewable energy, this work is accomplished in order to meet rising electricity demand as well as reduce the dependence on fossil fuels. In the distinctive means of renewable energy, recent years have seen rapid growth on installed power and output power as well. With the development of power electronic techniques and manufacturing materials, the installation cost of photovoltaic (PV) is reducing gradually, promising intense competition with the conventional energy in foreseeable future. However, this mean of energy is affected by coincident variation of irradiance parameters and environment temperature, etc. Hence, power electronic converters must integrate the algorithms of Maximum Power Point Tracking (MPPT) so that improve the output power of Photovoltaic systems. The previous MPPT algorithms have existed some drawbacks in operation such as oscillation and the slow process, etc. This article build a MPPT based on simulation and experimental methods and is compared to the conventional MPPT algorithms. The verifiable results show the hybrid MPPT algorithm has better performance, enhance the quantity of obtained power and increase the stability of the system. &#x0D; X&#x0D;

A Fast GMPPT Algorithm Based on PV Characteristic for Partial Shading Conditions

Photovoltaic (PV) modules experience some partial shading conditions (PSC) due to some various factors. In that kind of a condition, a few maximum power points (MPPs) possibly appear on the power-voltage (P-V) curve, which increases the tracking difficulties. It is known that maximum power point tracking (MPPT) may not be realized by hill climbing (HC) based conventional MPPT algorithms under PSCs. In this context, this paper presents a novel micro converter based algorithm that was developed by using P-V characteristics of PV modules. Unlike voltage or duty ratio scanning techniques, this paper introduces a new deciding method to determine the correct global MPP (GMPP) region. For this, the proposed method uses some duty ratios that were calculated corresponding to each MPP region. Thus, the initialization of duty ratio is done properly, which results in high tracking speed and accurate tracking of the GMPP. The other advantages of the proposed algorithm are structural simplicity, less computational burden, and ease of implementation with a basic microcontroller. The simulation results show that this algorithm has fast tracking capability and it manages to track GMPP for PSCs correctly, since it includes an artificial scanning procedure. Single ended primary inductance converter (SEPIC) is built in order to validate the proposed global maximum power point tracking (GMPPT) algorithm. The performance of the proposed GMPPT technique is verified by experimental studies. The results show that the proposed GMPPT technique is fast by up to five times than an adaptive full scanning strategy and improved IC algorithm. Furthermore, the proposed algorithm can be commercially used in micro converters, since it is compatible with small number of bypass diodes in a module.

Robust Integral Backstepping Based Nonlinear MPPT Control for a PV System

A photovoltaic system generates energy that depends on the environmental conditions such as temperature, irradiance and the variations in the load connected to it. To adapt to the consistently increasing interest of energy, the photovoltaic (PV) system must operate at maximum power point (MPP), however, it has the issue of low efficiency because of the varying climatic conditions. To increase its efficiency, a maximum power point technique is required to extract maximum power from the PV system. In this paper, a nonlinear fast and efficient maximum power point tracking (MPPT) technique is developed based on the robust integral backstepping (RIB) approach to harvest maximum power from a PV array using non-inverting DC-DC buck-boost converter. The study uses a NeuroFuzzy network to generate the reference voltage for MPPT. Asymptotic stability of the whole system is verified using Lyapunov stability criteria. The MATLAB/Simulink platform is used to test the proposed controller performance under varying meteorological conditions. The simulation results validate that the proposed controller effectively improves the MPPT in terms of tracking speed and efficiency. For further validation of the proposed controller performance, a comparative study is presented with backstepping controller, integral backstepping, robust backstepping and conventional MPPT algorithms (PID and P&amp;O) under rapidly varying environmental conditions.