Analysis and Implementation of MPPT Algorithm for a PV System with High Efficiency Interleaved Isolated Converter

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.

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.

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..

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.

A model is derived to estimate the primary-side inductor current of a discontinuous current mode Flyback converter. The model is validated by comparing the current estimated by the model to the current sensed using a sense resistor. The model was applied in a photovoltaic (PV) system which interfaced a PV module to a battery via a Flyback converter in order to charge a 12V lead acid battery. The system successfully controlled power flow from the PV module according to a model-based current perturb and observe (P&O) maximum power point tracking (MPPT) algorithm.

This paper presents an event-triggered approach to optimally implement a Maximum Power Point Tracking (MPPT) algorithm into a Digital Signal Processor (DSP). The proposed method allows improving the amount and distribution of time required for executing control tasks. The used nested loop control architecture has an outer loop of MPPT generating the conductance reference used by an inner loop which regulates the input conductance of a DC-DC converter. This last loop enforces a sliding-mode loss-free-resistor behavior for the power converter by means of a simple hysteresis comparator. Computations required by the MPPT algorithm are synchronously executed by the two possible commutation events produced by the inner loop during a switching period. Then, the acquisition of signals must be activated only at an instant before each one of the switching events, releasing the most of the time to implement other tasks. This last characteristic and the use of a nested loop control architecture facilitate the integration of the other essential control functions for photovoltaic (PV) generators in microgrids. Simulation and experimental results confirm the high potetialities of this implementation approach.

This paper presents a comprehensive comparative study between two prominent Maximum Power Point Tracking (MPPT) algorithms: the Perturb and Observe (P&O) method and the Incremental Conductance method (IC). The study delves into their operational principles, efficiency, robustness, implementation complexity, response time, and sensitivity to parameter changes. Through theoretical analysis and numerical simulations, the strengths and limitations of each algorithm are thoroughly assessed, offering valuable insights for optimizing photovoltaic (PV) systems. These simulations utilize established mathematical models of PV systems and MPPT algorithms. The findings reveal nuanced differences between the P&O and Incremental Conductance methods. Incremental Conductance demonstrates superior efficiency, particularly in environments with dynamic irradiance levels and partial shading conditions, owing to its ability to dynamically adjust the operating point. However, it exhibits increased implementation complexity compared to the simpler and more robust P&O method. In conclusion, this comparative study offers valuable insights into MPPT algorithm optimization for PV systems. While Incremental Conductance excels in efficiency and adaptability, P&O remains a viable option for applications with limited computational resources or stable environmental conditions due to its simplicity and robustness.

Wind energy systems have become a major commodity in the use of renewable energy resources, but its implementation requires great technical challenges in power extraction. Giligenting Island, Madura, East Java, is a prospective area for wind energy development. An algorithm applied to obtain optimal power from wind power generation. This paper presents a performance evaluation of the methods that can be applied as the MPPT (Maximum Power Point Tracking) algorithms. P&O (Perturbation and Observation), IC (Incremental Conductance), and PSO (Particle Swarm Optimization) are studied and discussed as the MPPT techniques in this research. All three methods are implemented on systems that have the same characteristics, they are compared to the simulation results. At the base wind turbine speed of 12 m/s, the P&O method obtained MPPT power of 295.82 W with settling time at 1.02 s. The IC method obtained MPPT power of 297.08 W with settling time at 1 s. Then, The PSO method achieved an MPPT power of 297.45 W with time settling at 0.88 s. According to the simulation results, the PSO is the algorithm that provided the best performance among all MPPT algorithms studied. Beside good response to wind speed variations, PSO also has a simple structure and easy to implement.

This paper presents a comparive analysis between four Maximum Power Point Tracking (MPPT) algorithms in centralized grid-tied Photovoltaic (PV) systems. The algorithms are used to control the duty cycle of a DC-DC converter in order to boost the output voltage of the PV generator and guarantee the operation of the solar panels in the Maximum Power Point (MPP). Three of the analyzed algorithms correspond to the classical Perturb and Observe (P&O) method, the Incremental Conductance (IC) technique and the Constant Voltage (CV) method. The fourth analyzed algorithm is a modification of the classical P&O method and it has been designed to improve the tracking of the MPP under voltage disturbances at the DC-side capacitor (interface between the DC-DC and the DC-AC stage). The performance of the MPPT techniques is compared through simulations in PSIM under two conditions: sudden changes of irradiance and a distortion of second order in the voltage at the DC-side capacitor. The comparative analysis is made according to some criterias such as the number of sensors, the rise time and the efficiency.

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.

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.

This paper investigates and compares four different maximum power point tracking (MPPT) algorithms that are commonly used to improve the efficiency in photovoltaic (PV) systems. These studied algorithms are: the Perturb and observe (P&O) algorithm, the incremental conductance (IC) algorithm, the fuzzy logic (FL) control and the sliding mode control (SMC). The comparison is based on several performance criteria: settling time, tracking accuracy, implementation complexity and efficiency. Each MPPT algorithm is tested under various irradiation conditions using Matlab/Simulink. The comparative analysis shows that the SMC method is considered as the fastest and the most stable in MPP tracking as compared to the other studied methods except the presence of the chattering phenomena which is its major drawback.

Photovoltaic (PV) modules subject to partial shading (PS) or inhomogeneous irradiation drastically decrease the PV string power output. An algorithm composed of maximum voltage point tracking (MVPT) and maximum power point (MPP) tracking (MPPT) is presented to restore part of the power loss caused by PS. The MVPT can simultaneously drive the operating point of series PV modules toward their respective MPPs and guarantee power peak uniqueness. The MVPT is performed by means of a controllable current transformer (CCT) disposed at the terminal of each PV module permitting a compatible current in the series path of a PV string. The CCT output current can be regulated using a dependent current source according to the MPPT algorithm. An appropriate CCT current determined by the MPPT algorithm can avert undue CCT activation and consequently reduces the switching and copper losses of the converter circuit. Experimental results validate that the proposed strategy can balance the PV modules' output current subject to PS and facilitate convergence to the theoretical global MPP.

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.

In grid connected photovoltaic (PV) systems, maximum power point tracking (MPPT) algorithm plays an important role in optimizing the solar energy efficiency. In this paper, the new artificial neural network (ANN) based MPPT method has been proposed for searching maximum power point (MPP) fast and exactly. For the first time, the combined method is proposed, which is established on the ANN-based PV model method and incremental conductance (IncCond) method. The advantage of ANN-based PV model method is the fast MPP approximation base on the ability of ANN according the parameters of PV array that used. The advantage of IncCond method is the ability to search the exactly MPP based on the feedback voltage and current but don't care the characteristic on PV array. The effectiveness of proposed algorithm is validated by simulation using Matlab/ Simulink and experimental results using Card DSPACE 1104.