Peak Current Control Based Maximum Power Point Trackers for Faster Transient Responses

Due to the Randomness and fluctuation of the photovoltaic (PV) system, Maximum Power Point Tracking (MPPT) algorithm is employed to ensure the maximum power extraction from PV arrays, when it is connected to the grid. The previous studies have indicated that the MPPT algorithm is one of the main causes of interharmonics emission in PV systems. The MPPT algorithm parameters such as perturbation step size inevitably have a strong impact on the interharmonics in the current injected to the grid. In general, a smaller MPPT perturbation step size will reduce the interharmonics emission level, but it will result in poor tracking performance of the MPPT algorithm. Thus, there is a trade-off between the MPPT efficiency and the interharmonics emission in PV system. In this paper, an interharmonics suppression scheme with variable step size MPPT algorithm was proposed. In the suppression scheme, two voltage values near by the MPPT voltage and two tracking step size are preset. When the dc-link voltage of PV is between the two preset voltage values, it means that the PV power near its MPP and the smaller perturbation step size implemented in the MPPT algorithm. Otherwise, the bigger perturbation step size is employed. It can greatly reduce interharmonics emission level and maintain the MPPT efficiency. Simulations have been carried out to verify the effectiveness of the suppression scheme.

The perturbation and observation (P&O) maximum power point tracking (MPPT) algorithms are commonly used in photovoltaic (PV) systems due to their easy implementation and ability to track the maximum power point (MPP) of the solar array under widely varying atmospheric conditions viz. solar irradiation, panel temperature etc. P&O algorithm based on peak current control and the use of instantaneous sampled values to calculate the next perturbation direction have the potential for faster transients and smaller oscillations around the MPP. The use of fixed variation of the reference current results in a compromise sub-optimum solution. This paper discusses a fuzzy logic based P&O MPPT with peak current control with variable variation of the reference current for improved transient as well as steady-state performance. Simulation results show a 15 % gain in the transient response and decrease of the power loss in the steady state. Besides, both the P&O scheme with fixed variation for the reference current and the intelligent MPPT algorithm were able to identify the global MPP in a partially shaded PV module, however the performance of the intelligent MPPT algorithm was better

An on-line MPPT algorithm for rapidly changing illuminations of solar arrays

Comparative study of variable size perturbation and observation maximum power point trackers for PV systems

By detection of input signal, Maximum Power Point Tracking (MPPT) algorithms are used to maximize the potential output power. Since Perturbation and Observe (P&O) method was first applied in a Photovoltaic (PV) system, a myriad of MPPT algorithms has been proposed. With the pros and cons of various MPPT algorithms, a detailed analysis of several typical direct search and module-integrated MPPT algorithms is presented in this paper. Directions of future work for implementing new MPPT algorithms are also outlined.

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.

This paper presents the analysis and operation of a grid connected photovoltaic (PV)energy conversion system with an Adaptive Neuro-Fuzzy Inference System (ANFIS)based maximum power point tracking (MPPT)algorithm. Particle swarm optimization is used to train the membership functions while the least squares algorithm is used to update the consequent parameters of the ANFIS with changing operating condition of PV solar system. The MPPT algorithm maximizes conversion efficiency by adjusting the duty cycle of the buck boost converter to change the output voltage of the solar panel and hence achieving the maximum panel output power for a given set of environmental conditions. The ANFIS is trained by using a hybrid algorithm implementing least squares estimator and particle swarm optimization with data obtained by operating the system using the Perturb and Observe (P&O)MPPT algorithm. The performance of the proposed ANFIS based MPPT algorithm is validated in simulation using MATLAB/Simulink at different operating conditions. It is proven that the designed ANFIS based MPPT scheme achieves a very fast response with little oscillations while transferring maximum power from solar panel to the grid line as compared to the conventional P&O based MPPT scheme.

This paper proposed an adaptive Delta (AdaDelta) based maximum power point tracking (MPPT) algorithm for a PV system. Unlike existing gradient descent (GD) MPPTs in which the learning rate (or step) size is fixed, the learning rate in the proposed method is automatically changed with the past accumulated gradient and distance from the maximum power point (MPP), e.g., the closer to MPP, the smaller step size becomes. Compared to the conventional perturb and observe (P&O) MPPT algorithm, the proposed AdaDelta-MPPT is more general, i.e., P &O is a special case of the AdaDelta- MPPT. The simulation and experimental results validated its superiority over the P&O in both transient and steady-state conditions, regardless of the operating points. Particularly, the approximate zero-step size around MPP with AdaDelta-MPPT eliminates the steady-state power oscillation.

ABSTRACTThis paper presents the modelling, simulation and hardware implementation of the incremental conductance (INC) maximum power point tracking (MPPT) algorithm. The extraction of maximum power from the solar photovoltaic system was made by integrating the PV module with a DC–DC converter and utilising the MPPT algorithm. In this work, the INC MPPT algorithm is practically implemented on an Arduino Uno board to track the maximum power point (MPP) and also simulated in Matlab/Simulink under the same environmental conditions. The performance of the INC algorithm was also compared with the conventional Perturb and Observe (P&O) MPPT algorithm. The experimental and simulation results are provided for both MPPT techniques. Matlab/Simulink was used for simulation studies.

PV irrigation systems have begun to be used intensively today, as energy needs increase. In Partially Shaded Conditions (PSC), the efficiency of the PV system decreases significantly, and traditional Maximum Power Point Tracking (MPPT) algorithms become insufficient. On the other hand, traditional MPPT algorithms require sensors to measure the current and voltage of the PV system. In this study, a sensorless hybrid MPPT algorithm is proposed to reduce system costs and enable operation without the need for PV system data. A simulation study was conducted in the MATLAB/Simulink environment to examine the PV system. The proposed algorithm has been tested under four different PSC scenarios. PV system power, motor speed, and currents were examined under each condition. The high maximum power tracking performance of the proposed algorithm is demonstrated through simulation results. In the steady state, the lowest MPPT efficiency was 95.66%, whereas the highest MPPT efficiency was 99.9%. The MPPT algorithm completed in less than 2 seconds, with the first stage taking 1.3 seconds to reach most of the maximum PV system power. The second stage of the MPPT algorithm was used to achieve maximum power in a narrower area.

A comparative study between frequently used maximum power point tracking (MPPT) algorithms and a proposed Least Squares Support Vector Machine (LS-SVM) based MPPT algorithm under rapidly changing insolation condition (RCIC) is presented in this paper. The comparison shows that the proposed LS-SVM based MPPT algorithm offers some advantages over existing MPPT algorithms under RCIC, namely elimination of ripples in the output voltage of a PV solar system as well as improved tracking accuracy. The algorithms have been tested through simulation studies carried out on real data recorded by the Australia Bureau of Meteorology. The study compares the performances of LS-SVM, P&O and IncCond.

In order to operate a solar photovoltaic (PV) system at its maximum power point (MPP) under numerous weather conditions, it is necessary to achieve uninterrupted optimal power production and to minimize energy losses, energy generation cost, and payback time. Under partial shading conditions (PSC), the formation of multiple peaks in the power voltage characteristic curve of a PV cell puzzles conventional MPP tracking (MPPT) algorithms trying to identify the global MPP (GMPP). Meanwhile, soft-computing MPPT algorithms can identify the GMPP even under PSC. Drawbacks such as structural complexity, computational complexity, huge memory requirements, and difficult implementation all affect the viability of soft-computing algorithms. However, those drawbacks have been successfully overcome with a novel ten check algorithm (TCA). To improve the performance of the TCA in terms of MPPT speed and efficiency, a novel concept of data arrangement is introduced in this paper. The proposed structure is referred to as Optimized TCA (OTCA). A comparison of the proposed OTCA and classic TCA algorithms was conducted for standard benchmarks. The results proved the superiority of the OTCA algorithm compared to both TCA and flower pollination (FPA) algorithms. The major advantage of OTCA in MPPT stems from its speed as compared to TCA and FPA, with almost 86% and 90% improvement, respectively.

Photovoltaic (PV) systems are increasingly used as a sustainable energy source, but their efficiency is significantly affected by environmental factors such as shading and temperature variations. This study proposes a novel Maximum Power Point Tracking (MPPT) algorithm based on the fusion of Support Vector Regression (SVR) and Incremental Conductance (INC) techniques, designed to enhance the performance of PV systems under partial shading conditions. The SVR model is employed to predict the power output of the PV system based on voltage and current measurements, leveraging its capability to handle non-linear relationships with high accuracy. The INC method is then integrated with the SVR predictions to dynamically adjust the operating point of the PV array, ensuring rapid and precise tracking of the maximum power point (MPP). The algorithm was implemented in MATLAB, and comprehensive simulations were conducted to evaluate its performance. Simulation results demonstrate that the SVR-INC fusion algorithm significantly improves the accuracy and stability of MPPT under varying shading conditions. The algorithm effectively reduces the tracking time to the MPP and minimizes power losses caused by partial shading. Compared to traditional MPPT methods, the proposed algorithm shows enhanced robustness and faster convergence, making it highly suitable for practical applications in real-world PV systems. Furthermore, the integration of SVR with INC provides a robust framework for dealing with the complexities of $P V$ power generation. The algorithm’s ability to adapt to changing environmental conditions ensures that the PV system operates at its maximum efficiency. The study also discusses the optimization of SVR parameters and the importance of datadriven approaches in enhancing MPPT performance. In conclusion, the proposed SVR-INC fusion MPPT algorithm offers a promising solution for improving the efficiency and reliability of PV systems under partial shading. The findings highlight the potential of this approach for future advancements in PV technology, contributing to the development of more efficient and resilient solar energy systems..

In this paper, a linear-prediction maximum power point tracking (MPPT) algorithm for photovoltaic (PV) power generation is presented. This allows rapid tracking without step-size reference. The new methodology has two parts: linear prediction and error correction. The first part estimates the maximum power point (MPP); this improves the MPPT response speed. The second part calibrates the error after the linear prediction; this enhances the calculation accuracy which leads to a faster MPP convergence. Theoretical analysis and simulations are put forward to validate the feasibility of the linear prediction method. Convergence, error correction, and steady and dynamic state evaluations are made. The results show that the algorithm can work effectively and have the advantages of fast response and high efficiency when compared to the Perturbation and Observe (P&O) algorithm.

This paper presents a performance analysis of grid connected SPV system for different maximum power point tracking (MPPT) algorithms. The detailed mathematical model of grid connected three-phase solar photovoltaic (SPV) system with parametric model of SPV cell, thermal modelling and switching loss calculation of switching devices are discussed. The performance evaluation has been carried out for perturb-and-observe (P&O) and incremental conductance (IC)-based MPPT algorithms for various outputs of the SPV array, in terms of energy injected to grid, switching losses, junction temperature and sink temperature, for switching in the DC-DC boost converter. The results validated the effectiveness of the MPPT algorithm in increasing SPV output energy, decrease in the switching losses, junction and sink temperature. The results show that IC method is slightly better to P&O method in energy injected to the grid with lower switching losses, junction and sink temperature.