Proposed Maximum Power Point Tracking Method Research Articles

Comprehensive Analysis of Improved Hunter–Prey Algorithms in MPPT for Photovoltaic Systems Under Complex Localized Shading Conditions

The Hunter–Prey Optimization (HPO) algorithm represents a novel population-based optimization approach renowned for its efficacy in addressing intricate problems and optimization challenges. Photovoltaic (PV) systems, characterized by multi-peaked shading conditions, often pose a challenge to conventional maximum power point tracking (MPPT) techniques in accurately identifying the global maximum power point. In this research, an MPPT control strategy grounded in an improved Hunter–Prey Optimization (IHPO) algorithm is proposed. Eight distinct shading scenarios are meticulously crafted to assess the feasibility and effectiveness of the proposed MPPT method in capturing the maximum power point. A performance evaluation is conducted utilizing both MATLAB/simulation and an embedded system, alongside a comparative analysis with alternative power tracking methodologies, considering the diverse climatic conditions across different seasons. The simulation outcomes demonstrate the capability of the proposed control strategy in accurately tracking the global maximum power point, achieving a commendable efficiency of 100% across seven shading conditions, with a tracking response time of approximately 0.2 s. Verification results obtained from the experimental platform illustrate a tracking efficiency of 98.75% for the proposed method. Finally, the IHPO method’s output performance is evaluated on the StarSim Rapid Control Prototyping (RCP) platform, indicating a substantial enhancement in the tracking efficiency of the photovoltaic system while maintaining rapid response times.

An adaptive fuzzy maximum power point tracking for PV systems by a mutant gray wolf optimization algorithm

Abstract An innovative and adaptive maximum power point tracking (MPPT) based on a fuzzy controller is proposed in this article. The proposed controller is optimized by the mutant gray wolf optimization (MGWO) algorithm, and the objective function uses an integral squared time squared error (ISTSE) index. To indicate the efficiency of the proposed MPPT algorithm (MGWO-AF-MPPT), the simulations for several irradiation conditions are modeled in MATLAB. In addition, two fair comparisons are performed between the simulation results of the proposed MPPT method with an adaptive fuzzy controller that optimized with particle swarm optimization (PSO-AF-MPPT), as well as a Perturbation and Observation method (P&O-MPPT). The comparison results illustrate that the amount of generated electrical power from the PV system for all adaptive MPPT systems is almost close to each other, indicating the high accuracy of both fuzzy-based controllers. However, the PV power generation of the proposed MGWO-AF-MPPT is higher, and also its ripple is lower than PSO-AF-MPPT. Note that, the calculated total harmonic distortion (THD) for the P&O-MPPT, PSO-AF-MPPT, and the proposed MGWO-AF-MPPT methods are calculated at 3.4%, 2.3%, and 2.1%, respectively. A lower THD indicates better power quality generated by the PV system employing the MGWO-AF-MPPT method.

Maximum Power Point Tracking Constraint Conditions and Two Control Methods for Isolated Photovoltaic Systems

A maximum power point (MPP) always exists in photovoltaic (PV) cells, but a mismatch between PV system circuit parameters, weather conditions and system structure leads to the possibility that the MPP may not be tracked successfully. In addition, the introduction of an isolation transformer into a basic PV system allows for moderate values of the converter duty cycle and electrical isolation. However, there is no comprehensive research on MPPT (maximum power point tracking) constraint conditions for different isolated PV systems, which seriously hinders the application of isolated PV systems and the development of a related linear control theory. Therefore, in this paper, the overall mathematical models of different isolated PV systems are first established based on the PV cell engineering model and the MPP linear model, and then, two sets of constraint conditions are found for the successful realization of MPPT. These MPPT constraint conditions (MCCs) describe in detail the direct mathematical relationships between PV cell parameters, weather conditions and circuit parameters. Finally, based on the MPP linear model and MCCs, two new MPPT methods are designed for isolated PV systems. Considering the MCCs proposed in this paper, a suitable range of load and transformer ratios can be estimated from the measured data of irradiance and temperature in a certain area, and the range of MPPs existing in PV systems with different structures can be estimated, which is a good guide for circuit design, theoretical derivation and product selection for PV systems. Meanwhile, comparative experiments confirm the rapidity and accuracy of the two proposed MPPT methods, with the MPPT time improving from 0.23 s to 0.03 s, and they have the advantages of a simple program, small computational volume and low hardware cost.

Archimedes optimization algorithm (AOA)-Based global maximum power point tracking for a photovoltaic system under partial and complex shading conditions

During partial shading conditions (PSCs), the efficiency of power transfer in a Photovoltaic (PV) system decreases significantly, which can result in the formation of hotspots in the PV array. Although the insertion of bypass diodes can alleviate this issue, it can result in multiple power peaks on the power-voltage (P–V) characteristics and complicate the process of maximum power tracking. To tackle this problem, using metaheuristic algorithms for Maximum Power Point Tracking (MPPT) can yield favourable results by avoiding convergence to local power peaks and reducing computation stress on the microcontroller. However, continuous research is required in this area due to variations in the performance of metaheuristic algorithms. Therefore, this work introduces a novel MPPT approach based on the Archimedes Optimization Algorithm (AOA) for the successful capture of the Maximum Power Point (MPP) under various PS scenarios. The performance of AOA is evaluated against the other state-of-the-art algorithms such as particle swarm optimization (PSO), Jaya, and the newly proposed variant of Jaya called the adaptive Jaya (A-Jaya). The proposed MPPT method is analysed in MATLAB/Simulink software and validated using real-time results obtained from Typhoon Hardware-in-the-loop (HIL)-402 emulator. The proposed algorithm outperforms the previous algorithms, as evidenced by a comparison of the results in terms of power tracking efficiency, tracking time, and the quantity of power fluctuations.

Improving Photovoltaic MPPT Performance through PSO Dynamic Swarm Size Reduction

Efficient energy extraction in photovoltaic (PV) systems relies on the effective implementation of Maximum Power Point Tracking (MPPT) techniques. Conventional MPPT techniques often suffer from slow convergence speeds and suboptimal tracking performance, particularly under dynamic variations of environmental conditions. Smart optimization algorithms (SOA) using metaheuristic optimization algorithms can avoid these limitations inherent in conventional MPPT methods. The problem of slow convergence of the SOA is avoided in this paper using a novel strategy called Swarm Size Reduction (SSR) utilized with a Particle Swarm Optimization (PSO) algorithm, specifically designed to achieve short convergence time (CT) while maintaining exceptional tracking accuracy. The novelty of the proposed MPPT method introduced in this paper is the dynamic reduction of the swarm size of the PSO for improved performance of the MPPT of the PV systems. This adaptive reduction approach allows the algorithm to efficiently explore the solution space of PV systems and rapidly exploit it to identify the global maximum power point (GMPP) even under fast fluctuations of uneven solar irradiance and temperature. This pioneering ultra-fast MPPT method represents a significant advancement in PV system efficiency and promotes the wider adoption of solar energy as a reliable and sustainable power source. The results obtained from this proposed strategy are compared with several optimization algorithms to validate its superiority. This study aimed to use SSR with different swarm sizes and then find the optimum swarm size for the MPPT system to find the lowest CT. The output accentuates the exceptional performance of this innovative method, achieving a time reduction of as much as 75% when compared with the conventional PSO technique, with the optimal swarm size determined to be six.

MAXIMUM POWERPOINT TRACKING IN SOLAR PANELS DURING PARTIAL SHADING

For maximum utilization of solar energy, photovoltaic (PV) power systems should be operated at the maximum power point (MPP) which can be achieved using maximum power point tracking (MPPT) methods. However, the occurrence of multi-peak on P-V curve of a PV array due to the changing environmental conditions such as being partially shaded increases the complexity of the tracking process. The global MPP cannot always be achieved by the conventional MPPT methods. Therefore, a novel MPPT method for PV systems using flower pollination (FP) algorithm is proposed in this paper and the Levy flight is used to improve the convergence of FP algorithm. MPPT model of the PV system is established in MATLAB to verify the effectiveness of the proposed method, and the proposed method is compared with two well established MPPT methods. The simulation results indicate that the proposed MPPT method can quickly track the changes in external environment and effectively handle the partially shaded condition. Key Words: Photovoltaic (PV), maximum power point tracking (MPPT), partially shaded condition, flower pollination (FP)

A Thermoelectric Energy-Harvesting Interface With Dual-Conversion Reconfigurable DC–DC Converter and Instantaneous Linear Extrapolation MPPT Method

This article presents a thermoelectric (TE) energy-harvesting interface with a reconfigurable dc–dc converter and a time-based instantaneous linear extrapolation (ILE) maximum power point tracking (MPPT) method. The proposed reconfigurable dc–dc converter comprises five switches for buck–boost operation and an additional switch for a dual-conversion mode in which battery power and TE generator (TEG) power are extracted in one cycle; therefore, the improved power density is achieved. The proposed ILE MPPT algorithm does not require TEG disconnection from the dc–dc converter, ensuring continuous TE energy extraction. The proposed MPPT method modulates the open-circuit voltage of TEG by performing linear extrapolation with a fully integrated circuit based on the steady-state voltage ripple information. The proposed interface is fabricated using a 180-nm CMOS process. The peak end-to-end efficiency of 90.48% is achieved with 0.2-V input voltage. The MPPT errors are measured up to 2.0% for the input capacitance variation and up to 1.067% for the equivalent resistance of TEG variation.

A global MPPT technique for PV systems under partial shading conditions

ABSTRACT Maximum Power Point Tracking (MPPT) algorithms are used to obtain maximum power from PV (Photovoltaic) systems in variable atmospheric conditions. Partial shading conditions (PSCs) have an important effect causing the maximum power points of PV systems to change. In this study, a new hybrid MPPT algorithm was proposed by combining Voltage Scanning (VSC) and Cuckoo Search Algorithm (CSA) methods to obtain maximum power from a PV system running under PSC. To test the proposed method, a 252.6 kW grid-connected PV system with 609 panels, including 7 serial panels and 87 parallel strings, was created in the MATLAB/Simulink environment. This system was tested under 10 different PSCs by using VSC, Particle Swarm Optimization (PSO), CSA, and the proposed MPPT methods. The average tracking efficiency and speed of the proposed method under these conditions were obtained as 99.74% and 0.3s, respectively. Under PSCs, the proposed method achieved the highest tracking efficiency of 99.92%, while the highest tracking speed that it achieved was 0.22s. On the other hand, the superiority of the proposed hybrid MPPT algorithm was clearly demonstrated by comparing it to the VSC, PSO, and CSA methods.

An efficient MPPT based photovoltaic control model considering environmental parameters

<span>This paper presents an efficient way for maximum power point tracking (MPPT) in photovoltaic (PV) system. MPPT is one of the crucial issues when working with PV system as well as the grid. To gain the highest efficiency for PV system the maximum power has to be generated continuously. The proposed MPPT method allows PV system to have real-time maximum power all the time with high accuracy and less fluctuations. As of the developed PV system control model presented in this work, an efficient MPP can be realized taking into account changes in irradiation level and temperature which are the thorny issues for other contender algorithms. To validate the model, results obtained from the proposed algorithm is compared with incremental conductance (IC) which is a universally accepted MPPT method. The simulation results exhibited that the developed model outperforms IC method in terms of accuracy of MPP and stability of the output in presence of variable irradiations and temperature. Based on the simulation results, the proposed algorithm is suitable for practical and real-time applications with promising results in terms of solution accuracy and execution. The model is implemented in MATLAB/Simulink.</span>

Maximum power point tracking in partially shaded photovoltaic systems using grasshopper optimization algorithm

The P–V curve of photovoltaic (PV) arrays has several peaks under partial shading conditions (PSCs). Therefore, to extract maximum power, it is necessary to detect its global maximum power point (GMPP). The conventional maximum power point tracking (MPPT) algorithms are trapped in local maximum power point (LMPP) under PSCs. In this paper, a MPPT method based on grasshopper optimization algorithm (GOA) has been presented. A PV system has been implemented in Matlab/Simulink and different operating conditions have been investigated to compare the performance of the proposed method with two well-known grey wolf optimization (GWO) and particle swarm optimization (PSO) algorithms. Furthermore, an experimental setup has been developed to verify the efficiency of the proposed MPPT method. The simulation and experimental results confirmed the speed and accuracy of convergence compared to GWO and PSO algorithms.

Global Maximum Power Point Tracking of Photovoltaic Module Arrays Based on Improved Artificial Bee Colony Algorithm

In this paper, an improved artificial bee colony (I-ABC) algorithm for the maximum power point tracking (MPPT) of a photovoltaic module array (PVMA) is presented. Even though the P-V output characteristic curve with multi-peak was generated due to any damages or shading discovered on the PVMA, the I-ABC algorithm could get rid of stuck on tracking the local maximum power point (LMPP), but quickly and stably track the global maximum power point (GMPP), thereby improving the power generation efficiency. This proposed I-ABC algorithm could search for the higher power point of a PVMA by a small bee colony, determine the next tracking direction through the perturb and observe (P&O) method, and keep tracking until the GMPP is obtained. This method could prevent tracking the GMPP for too long due to applying a small bee colony. First, in this study, the photovoltaic modules produced by Sunworld Co., Ltd. were used and were configured as a PVMA with four series and three parallel connections under different numbers of shaded modules and different shading ratios, so that corresponding P-V output characteristic curves with multi-peak values were generated. Then, the GMPP was tracked by the proposed MPPT method. The simulation and experimental results showed that the proposed method performed better both in dynamic response and steady-state performance than the traditional artificial bee colony (ABC) algorithm. According to the experimental results, it showed that the tracking accuracy for the GMPP based on the proposed MPPT with 100 iterations under 5 different shading ratios was about 100%; on the other hand, that of the traditional ABC algorithm was 70%, and that of the P&O method was lower at about 30%.

An Improved Mechanical Sensorless Maximum Power Point Tracking Method for Permanent-Magnet Synchronous Generator-Based Small Wind Turbines Systems

This article proposes an improved maximum power point tracking (MPPT) method without mechanical and voltage sensors for small wind turbines (SWT) systems. The technique is suitable for SWT systems that employ a permanent-magnet synchronous generator (PMSG), a three-phase diode bridge rectifier, and a dc–dc converter, which is one of the most popular configuration for this kind of system. The proposed MPPT extracts the maximum available power from the PMSG operating with variable speed, and it is based on the output power characteristic in relation to the output current of the rectifier. The article proves that a constant output current in the rectifier ensures extracting the maximum electrical power available in the SWT system, independent of the rotational speed. Additionally, this constant current value depends solely on the PMSG characteristics. Therefore, the proposed MPPT control strategy is a simple current mesh with a constant reference that is implemented in a dc–dc converter connected in series to the rectifier, which is responsible to guarantee a constant current in the rectifier output. The proposed MPPT method was corroborated through simulation and experimental results in a 1.5-kW SWT. The tracking efficiency of the proposed MPPT was close to 97.64%.

Hybrid SFLA MPPT design for multi-module partial shading photovoltaic energy systems

ABSTRACT Under partial shading conditions, the photovoltaic (PV) array will induce several local maximum power points, such that the traditional maximum power point tracking (MPPT) methods will trap in false maximum power points and reduce the energy conversion efficiency of the solar system. To solve this problem, this paper firstly introduces a multi-module solar energy system, where a master boost converter is used as a voltage regulator for the DC load, while other slave boost converters in parallel perform MPPT to support the load power as high as possible. Furthermore, a hybrid MPPT method is proposed by combining the shuffled frog leaping algorithm (SFLA) and the incremental conductance (In-Cond) method. The hybrid SFLA method can successfully and quickly find the maximum power region, while the exact MPP is achieved in the incremental conductance searching phase. As a result, trapping local maximum power points is avoided, while the computing complexity is reduced. Finally, simulation and experiments are carried out to verify the validity of the proposed MPPT method under partial shading conditions.

Novel hybrid modified krill herd algorithm and fuzzy controller based MPPT to optimally tune the member functions for PV system in the three-phase grid-connected mode

For the technical, economic and environmental benefits, using solar energy has increased worldwide, especially with the help of photovoltaic (PV) systems. The amount of energy produced in PV systems is dependent on environmental circumstances such as temperature and solar irradiance. In order to extract the maximum possible power in PV systems and increase the efficiency under various environmental conditions, maximum power point tracking (MPPT) controllers have been proposed. To fine-tune the control parameters of the proposed MPPT approach, the fuzzy controller and modified krill herd (MKH) algorithm are jointly employed. Rule base and membership functions (MFs) are two important parameters for implementing the FLC and need to be fine-tune appropriately. However, in the condition where precise information concerning the system is not available, the fine-tuning of these parameters cannot be accurate. To cope with this problem, the MKH algorithm is used to optimize the scaling factors of MFs. To improve the stability of the system under study, the PV system is used with the storage system at the same time. This hybrid system can deal with the stochastic nature of the PV system and provide more stability in all atmospheric conditions. The proposed MPPT method is confirmed by comparing it with other well-known techniques.

High Performance MPPT Approach for Off-Line PV System Equipped With Storage Batteries and Electrolyzer

The current publication is directed to achieve a high-performance stand-alone PV system having the capability of tracking maximum output power, providing fixed output DC voltage, and attaining efficient system utilization, under different irradiation levels. A new maximum power point tracking (MPPT) approach integrating the incremental conductance algorithm and fuzzy logic control, and enhanced with PI-controller, was proposed to track maximum power. To provide fixed output DC voltage and approaching full system utilization, the PV system is equipped with a battery bank, electrolyzer; as a dump load, and buck-boost converter, with two controllers. The results of the proposed MPPT technique; modified incremental conductance (MINC), are compared with the corresponding results of three prevalently implemented MPPT algorithms: perturbed and observed (P&O), modified variable step-size P&O (VSZ-PO) and the ordinarily incremental conductance (INC). The highest output power, best tracking efficiency and best output power response are achieved by utilizing the proposed MPPT method. The results of the output voltage response and electrolizer on/off states confirm the ability of the PV scheme to provide fixed DC voltage and attain efficient system utilization, under varying irradiances.

An Improved MPPT Method with Dynamic Voltage Error Compensation under Two Varying External Conditions

This paper presents a new maximum power point tracking (MPPT) method which can improve the dynamic tracking ability under two varying external conditions, rapid irradiance variation and grid voltage fluctuation. Different from irradiance variation, the dynamic error of tracking voltage caused by grid voltage fluctuation is usually ignored by MPPT methods. The dynamic error of tracking voltage can make the unexpected power change of photovoltaic system and influence the dynamic MPPT efficiency. According to the different current increment changes under the irradiance variation and the grid voltage fluctuation, the reasons of dynamic voltage errors are analyzed and distinguished. And two corresponding modes of variable step size are designed in the tracking algorithm to regulate the voltage deviation from MPPs. The effectiveness and the improved dynamic tracking performance of the proposed MPPT method are verified by simulation results under the above two varying external conditions.

Intelligent Perturb and Observe Based MPPT Approach Using Multilevel DC-DC Converter to Improve PV Production System

In this paper, an optimized maximum power point tracking (MPPT) control for a standalone photovoltaic (PV) system using a three-level boost (TLB) converter is introduced. The proposed MPPT method is based on an intelligent perturb and observe algorithm using the artificial neural network (ANN-P&O) to reduce the oscillations at the maximum power point (MPP). In advance, The ANN provides the values of the voltage and the current at the MPP for any solar irradiance and cell temperature. Based on the provided voltage and current, the P&O algorithm generates the optimal duty cycle of the TLB converter to perfectly track the MPP of the PV generator for different values of cell temperature and sunlight irradiance. Besides, a proportional-integral (PI) controller is added to ensure the TLB capacitor voltage balance. The established ANN-P&O approach is validated in Matlab/Simulink and compared to the conventional P&O algorithm under various scenarios: (i) irradiance variations, (ii) temperature variations, and (iii) load variations.

Nonlinear Neuro-Adaptive Control for MPPT Applied to Photovoltaic Systems

This paper presents an intelligent control technique using a nonlinear neuro-adaptive method. This method is based on a nonlinear model describing the dynamics of the boost converter, the PV array and the load for maximum power point tracking (MPPT) under varying environmental conditions. The proposed approach consisted of a radial basis function-neuro observer for online estimation of unknown PV system parameters (i.e., irradiation and temperature) and an online trained neuronal controller that ensures a satisfactory MPPT, whatever be the position of the photovoltaic panel. The real-time implementation of the proposed controller is achieved using Arduino Mega board. The performance of the proposed MPPT method is analyzed under different operating conditions and compared to those provided by the P&O method. Simulation results using MATLAB/Simulink software coupled to experimental results demonstrate the feasibility and the robustness of the proposed controller.

Performance analysis of grid-tied photovoltaic system under varying weather condition and load

Model and simulation of the impact of the distribution grid-tied photovoltaic (PV) system feeding a variable load with its control system have been investigated in this study. Incremental Conductance (IncCond) algorithm based on maximum power point tracking (MPPT) was implemented for the PV system to extract maximum power under different weather conditions when solar irradiation varies between 250W/m2 and 1000W/m2. The proposed system is modelled and simulated with MATLAB/Simulink tools. Under different weather conditions, the dynamic performance of the PV system is evaluated. The results obtained show the efficacy of the proposed MPPT method in response to rapid daytime weather variations. The results also show that the surplus power generated is injected into the grid when the injected power from the PV system is higher than the load demand; otherwise, the grid supplies the load.

Distributed SM MPPT Controller for Solar PV System Under Non-Uniform Atmospheric Conditions

This paper presents a diverse distributed maximum power point tracking (MPPT) method based on Cuk converter for solar photovoltaic system. The proposed MPPT method offers fast dynamic performance with absolute robustness by the concept of sliding mode control law to control the Cuk converter. The proposed configuration improves the dynamic response and reduces the ripple to considerable amount, resulting in their operation closer to maximum power point. Simulation is carried out to analyze the system behavior in response to varying atmospheric condition and the analytical results are also validated with simulation results.