MPPT Methods for Solar PV System: A Simulation-based Comparative Analysis

The escalating demands and increasing awareness for the environment, resulted in deployment of Photovoltaic (PV) system as a viable option. PV system are widely installed for numerous applications. However, the challenges in tracking the maximum power with intermittent atmospheric condition and varying load is significant. Maximum Power Point Tracking (MPPT) algorithms are employed and based on their convergence speed, control of external variations and oscillation, the output power efficiency, and other significant factors viz. the algorithm complexity and implementation cost, novel MPPT approach are preferable than the conventional approach. This paper presents an artificial intelligence-based optimization controller for MPPT in a PV system under varying load and irradiance conditions. Comparative analysis of Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) based MPPT is simulated and analysed. The PV system consisting of PV array and boost converter with MPPT controller feeds the DC load. The power conversion and panel efficiency are the significant factors to determine the effectiveness of tracking maximum power point. The simulation results show the performance of these controllers on the PV panel output power and the load side output power under changing loads and irradiance. In addition, the comparison of PV panel efficiency of ANN and PSO based MPPT techniques w.r.t changing loads is carried out. Based on the above analysis, PSO based MPPT algorithm marginally outperforms the ANN based MPPT algorithm. Further, the implementation of hybrid MPPT (ANN &PSO) for higher accuracy and tracking capability can be carried out as future work.

Since the performance of the Photo-Voltaic (PV) arrays is dependent on irradiation and temperature, the PV output power fluctuates with the ambient temperature and the solar irradiance. The achievement of the Maximum Power Point (MPP) under various shading patterns is, as a result, an important aspect in the enhancement of PV systems' overall performance. Because existing methodologies, such as perturb & observe, incremental conductance, etc., are likely to fail, it is necessary to develop an improved Maximum Power Point Tracking (MPPT) method to distinguish between the Global MPP (GMPP) and the Local MPP (LMPP). The characteristics of a PV array under shading conditions include several LMPPs and a single GMPP. To improve the MPPT method of shaded photovoltaic systems, this paper introduces an improved Particle Swarm Optimization (PSO) algorithm with Time-Varying Acceleration Coefficients (PSO-TVAC) that is fast and more efficient than the PSO. Firstly, the PSO-TVAC algorithm is mathematically modeled and applied to the MPPT application for solar PV systems. The primary objective of this paper is to analyze the performance of the PSO-TVAC for the MPPT application. Three shading patterns are considered to study the effectiveness of the PSO-TVAC in handling the MPPT application. The results and discussions prove that PSO-TV AC can be an alternative tool for MPPT application for PV systems.

Artificial intelligence methods such as fuzzy logic and particle swarm optimization (PSO) have been applied to maximum power point tracking (MPPT) for solar panels. The P-V curve of a solar panel exhibits multiple peaks under partial shading condition (PSC) when all modules of a solar panel do not receive the same solar irradiation. Although conventional PSO has been shown to perform well under uniform insolation, it is often unable to find the global maximum power point under PSC. Fuzzy adaptive PSO controllers have been proposed for MPPT. However, the controller became computation-intensive in order to adjust the PSO parameters for each particle. In this paper, fuzzy adaptive PSO-based and conventional PSO-based MPPT are compared and evaluated in the aspect of design and performance. A simple fuzzy adaptive PSO controller for MPPT was designed to reach the global optimal point under PSC and uniform irradiation. The controller combines the advantages of both PSO and fuzzy control. The fuzzy controller dynamically adjusts the PSO parameter to improve the convergence speed and global search capability. Since tuning of the PSO parameter is designed to be common for all particles, it reduced the computation complexity. The fuzzy controller’s rule base is designed to obtain a fast transient response and stable steady state response. Design of the fuzzy adaptive PSO-based MPPT is verified with simulation results using a boost converter. The results are evaluated in comparison to the results using a conventional PSO controller under PSC. Simulation shows the fuzzy adaptive PSO-based MPPT is able to improve the global search process and increase the convergency speed. The comparison indicates the settling time using the fuzzy adaptive PSO-based MPPT is 14% faster under PSC on average and 30% faster under uniform irradiation than the settling time using the conventional PSO. Both the fuzzy adaptive and conventional PSO controllers have similar output power tracking accuracy.

Dynamic global maximum power point tracking of the PV systems under variant partial shading using hybrid GWO-FLC

Currently, Photovoltaic (PV) energy is the most important energy resource in electrical power applications around the world since it is inexhaustible, clean and pollution free. As the output power from the PV arrays changing with solar irradiance and temperature, so it is necessary to operate the photovoltaic (PV) system at the maximum power point (MPP) to increase the power extraction from the solar panel. For this effective operation and maximum power extraction from a PV system, Maximum power point tracking (MPPT) algorithm plays a vital role by operating the PV energy system near the MPP. In this work, amongst several existing well established MPPT algorithms, the open voltage (OV) based algorithm is chosen for simulation of a PV system using Matlab / Simscape. As maximum power point (MPP) can be attained with a certain amount of load from the PV power system, so load matching is an important concept of MPPT algorithms. Here, the performances of this open voltage (OV) based algorithm method is evaluated for different values of solar radiation and battery was used as load to see the charging performance for the algorithm.

Purpose Silicon photovoltaics technology has drawbacks of high cost and power conversion efficiency. In order to extract the maximum output power of the module, maximum power point (MPP) is used by implying the nonlinear behavior of I-V characteristics. Different techniques are used regarding maximum power point tracking (MPPT). The paper aims to review the techniques of MPPT used in PV systems and review the comparison between Perturb and Observe (P&O) method and incremental conductance (IC) method that are used to track the maximum power and gives a comparative review of all those techniques. Design/methodology/approach A study of MPPT techniques for photovoltaic (PV) systems is presented. Matlab Simulink is used to find the MPP using P&O simulation along with IC simulation at a steady temperature and irradiance. Findings MATLAB simulations are used to implement the P&O method and IC method, which includes a PV cell connected to an MPPT-controlled boost converter. The simulation results demonstrate the accuracy of the PV model as well as the functional value of the algorithms, which has improved tracking efficiency and dynamic characteristics. P&O solution gave 94% performance when configured. P&O controller has a better time response process. As compared to the P&O method of tracking, the incremental conductance response rate was significantly slower. Originality/value In PV systems, MPPT techniques are used to optimize the PV array output power by continuously tracking the MPP under a variety of operating conditions, including cell temperature and irradiation level.

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.

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..

Maximum Power Point Tracking (MPPT) is a technique used in photovoltaic (PV) systems to maximize the power output from the solar panel by constantly tracking and adjusting the optimal operating point. To achieve this, various algorithms have been developed, with Particle Swarm Optimization (PSO) being a widely used method. By adjusting the control system’s parameters, PSO can determine the optimal operating point of the solar panel and improve its overall performance. PSO employs swarm intelligence by simulating the behavior of particles to find the best solution for a given problem. Long Short-Term Memory (LSTM) belongs to the family of Recurrent Neural Networks (RNN) in machine learning and is designed to address the limitations of traditional RNNs in capturing long-term dependencies that exist in sequential data. The combination of PSO and LSTM techniques can result in an efficient MPPT algorithm that leverages the benefits of both. PSO is utilized to optimize the control parameters of the MPPT algorithm, while LSTM is used to predict the solar panel’s power output based on historical data. Consequently, this integration can lead to an accurate and efficient MPPT algorithm that can effectively track the solar panel’s maximum power point. In this research article, an effort has been made to control the duty cycle of the converter by suitably controlling the system gain. A Matlab-based Simulink model in conjunction with Python programming has been used to make the system more robust.

To get the most power out of photovoltaic (PV) panels, PV systems must utilize a maximum power point tracking (MPPT) controller. In partial shading conditions (PSC), the power-voltage (P-V) characteristic of the PV network may show a single global maximum power point (GMPP) and two or more local maximum power points (LMPP). This indicates that the PV cells and panels do not get uniform illumination. As they converge on the maximum power point (MPP) that makes contact first, which is often one of the LMPPs in this scenario, common MPPT approaches like incremental conductance (InC) and perturb and observe (P&O) are unable to distinguish between a GMPP and LMPPs. In this paper, the extraction of the GMPP of the PV system under PSC based on a suggested particle swarm optimization (PSO) approaches is presented. The particularity of the suggested approach is that it takes into account the calculation of the position of each particle as a function of the duty cycle and the global maximum power. Results on the performance of the suggested PSO method show an advantage over the conventional PSO and the commonly used traditional P&O method. The suggested PSO technique offer better performance in terms of global power extracted, ripple rate of the power and efficiency.

"Photovoltaic panel maximum power point tracker". As the name implied, it is a photovoltaic system that uses the photovoltaic array as a source of electrical power supply and since every photovoltaic (PV) array has an optimum operating point, called the maximum power point, which varies depending on cell temperature, the insolation level and array voltage. A maximum power point tracker (MPPT) is needed to operate the PV array at its maximum power point. The objective of this paper is to build MPPT to charge a 12-volts lead acid battery by using a (TBP1275) 74 - watts PV panel. A technique for efficiency extracting the maximum output power from a solar panel under varying meteorological conditions is presented. The methodology is based on connecting a pulse width modulated dc/dc SEPIC converter, which is controlled by a micro controller based unit. The main difference between the method used in the proposed MPPT systems and other technique used in the past is that PV array output power is used to directly control the dc/dc converter thus reducing the complexity of the system. The resulting system has high efficiency, low cost and can be easily modified. This approach ensured maximum power transfer under all conditions by using micro controller for calculation. The tracking capability of the proposed technique has been verified experimentally with a 74 W solar panel at different insolation levels.

K-Nearest Neighbors hybrid method for maximum power point tracking under partial shading for photovoltaic power systems

Implementation of the novel temperature controller and incremental conductance MPPT algorithm for indoor photovoltaic system

Maximization of power from a solar photovoltaic (SPV) module is of special interest as the efficiency of the SPV module is very low. The present work describes the maximum power point tracker (MPPT) for the SPV module connected at variable load conditions. A personal computer (PC) is used for control of the MPPT algorithm. The power tracker is developed and tested successfully in the laboratory. The simulations are carried out in Matlab/Simulink. Data acquisition, monitoring, and control are done by the dSPACE software. Different types of algorithms are studied. The algorithm implemented here is “perturb and observe” in a microcontroller-based PC to extract maximum power from the SPV module. The measured parameters such as panel voltage, current, and power are obtained with and without MPPT systems and have been tested on the SPV Module at various load conditions, different temperatures and insolation labels. The comparative results with and without MPPT are shown; the results show that the tracking capability with MPPT improves as compared with without MPPT.

Solar panels are a renewable energy power plant that uses sunlight as its main energy source. The power generated by solar panels are determined by the size of the solar panels, solar radiation and temperature. The power of the solar panels is also determined by the output voltage of the solar panels. To get the maximum output power at any time, it is necessary to adjust the output voltage of the solar panel. This study proposes controlling the maximum output power of solar panels, also known as maximum power point tracking (MPPT) by adjusting the output voltage of the solar panels using a buck converter. The buck converter output voltage regulation at the maximum power point of the solar panel is designed with the Perturbation and Observation (PO) algorithm which is implemented using an Arduino Mega 2560. This MPPT control system is applied to 4x50 Watt-Peak (WP) solar panels which are connected in parallel. The experimental results show that the proposed MPPT control system with the PO algorithm has worked well as expected. This can be seen from the output power generated by the solar panels already around the maximum power point at any change in solar radiation and temperature.