Maximum Power Point Of Photovoltaic System Research Articles

Enhancing photovoltaic system maximum power point tracking with fuzzy logic-based perturb and observe method

Photovoltaic systems have emerged as a promising energy resource that caters to the future needs of society, owing to their renewable, inexhaustible, and cost-free nature. The power output of these systems relies on solar cell radiation and temperature. In order to mitigate the dependence on atmospheric conditions and enhance power tracking, a conventional approach has been improved by integrating various methods. To optimize the generation of electricity from solar systems, the maximum power point tracking (MPPT) technique is employed. To overcome limitations such as steady-state voltage oscillations and improve transient response, two traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb and observe (P&O), have been modified. This research paper aims to simulate and validate the step size of the proposed modified P&O and FLC techniques within the MPPT algorithm using MATLAB/Simulink for efficient power tracking in photovoltaic systems.

Comparison Between Perturb & Observe, Fuzzy Logic MPPT Technique, and The Artificial Neural Network Techniques at Different Temperature Conditions

Photovoltaic (PV) system performance is influenced by environmental factors such as irradiation, temperature, and shading effects, which we cannot control. However, to address this aspect, maximum power point tracking (MPPT) algorithms come into play. They keep an eye on the working point and modify it in real-time to comply with the Maximum Power Point (MPP), which assists in reducing the impact of environmental fluctuations. This, in turn, results in steady and ideal power generation from the PV system, increasing its dependability and efficiency regardless of the unpredictable climate conditions.This research compares three commonly used approaches for tracking the MPP in PV systems: the perturb and observe (P&O) method, fuzzy logic control (FLC), and Artificial Neural Networks (ANN). This comparison is based on a simulation using MATLAB/Simulink to carry out an in-depth analysis of these three methods. This simulation enables a complete examination of all the three methods, considering parameters like complexity, stability, and efficiency. This study intends to give a comprehensive review of the performance and applicability of the P&O method, fuzzy logic control, and ANN in PV systems by assessing these parameters.The present research adds useful insights to current information by thoroughly examining three popular MPPT approaches with simulations in MATLAB/Simulink. The findings can help researchers and professionals choose the best strategy for their PV systems based on their specific requirements. Furthermore, the comparison study highlights prospective possibilities for future research and MPPT technique improvements.

An Efficient Variable Step Solar Maximum Power Point Tracking Algorithm

The classic Photovoltaic system maximum power point tracking technique cannot concurrently take into account the dynamic response speed and steady-state accuracy when the light intensity changes. To address this issue, a new composite variable step MPPT control algorithm is developed in this study. Based on the three-stage variable step incremental conductance method, the algorithm adds the Kalman filtering algorithm to pre-process the photovoltaic cells output signal, and uses a new calculation approach to adjust the variable step coefficient. As a result, the perturbation step can be automatically modified according to changes in the external environment, which resolves the issues with poor dynamic reaction speed when the classic variable step algorithm started and the light changed. Compared to conventional MPPT control algorithms, the improved MPPT strategy can be easily realized using a hardware control system since it has a simplified control logic and requires less data to be calculated. In this study, the hardware circuit of the enhanced MPPT control algorithm is built using the ESP32 as the primary control chip. This chip can be utilized in conjunction with the Internet of Things to enable remote monitoring of the solar power system’s operational state. According to test results, the algorithm can instantly detect the maximum power point in all lighting circumstances with tracking accuracy of up to 99.6% and a reduction in dynamic response time of the system to 0.12 s.

A Photovoltaic System Maximum Power Point Tracking by using Artificial Neural Network

Wydawnictwo SIGMA-NOT wydaje czasopisma fachowe informujące swoich czytelników o najnowszych osiągnięciach naukowych i nowoczesnych rozwiązaniach technicznych w Polsce i na świecie, popularyzuje problemy techniczne oraz poszerza wiedzę i kulturę techniczną.

Circuit Parameter Range of Photovoltaic System to Correctly Use the MPP Linear Model of Photovoltaic Cell

The real-time linearization of a photovoltaic (PV) cell has been implemented well by the proposition of two maximum power point (MPP) linear models (MPP Thevenin cell model and MPP Norton cell model). However, there is no work to specially analyze the circuit parameter range (CPR) to correctly use them, which seriously impedes the development of the linear control theory involving them. To deal with this problem, in this paper, PV systems with three usual outputs are analyzed and the expressions of their CPR are proposed under ideal conditions. Meanwhile, these expressions are improved to match the practical application. They disclose the relationships between load (or bus voltage) and model parameters of the MPP Thevenin cell model (MPP-TCM) when the MPP of PV system always exists. They also reveal the constraints of load (or bus voltage) when the MPP-TCM is always available. Finally, by some simulation experiments, the accuracy of the expressions of the CPR is verified, the regular patterns of the CPR changing with weather are disclosed, and the comparison of the CPR for different PV systems are made. In this work, the relationships between MPP-TCM and circuit parameters are successfully found, disclosing the constraints among parameters when the MPP-TCM is used to implement the overall linearization of a PV system.

Implementation of Fuzzy logic control based MPPT for Photovoltaic system with Silicon Carbide (SiC) boost DC-DC converter

In recent days, photovoltaic (PV) system is the most promising renewable energy technologies and the PV cell has to operate at the optimum operating point to deliver maximum power. In order to obtain maximum power from PV, a maximum power point controller is required. This paper presents the simulation and hardware implementation of fuzzy logic (FL) maximum power point (MPPT) controller with FPGA technology for photovoltaic system. The MPPT algorithm is implemented for a Silicon carbide (SiC) MOSFET based boost DC-DC converter which provides fast switching, low losses and high voltage gain. The proposed MPPT algorithm is implemented on a SPARTAN/FPGA board platform based on the model developed and executed in MATLAB/SIMULINK. The entire system designed and implemented to hardware was successfully tested on a laboratory prototype PV array. The experimental results show the effectiveness and feasibility of the proposed controller and the results were satisfactory.

A Deep Reinforcement Learning-Based MPPT Control for PV Systems under Partial Shading Condition.

On the issues of global environment protection, the renewable energy systems have been widely considered. The photovoltaic (PV) system converts solar power into electricity and significantly reduces the consumption of fossil fuels from environment pollution. Besides introducing new materials for the solar cells to improve the energy conversion efficiency, the maximum power point tracking (MPPT) algorithms have been developed to ensure the efficient operation of PV systems at the maximum power point (MPP) under various weather conditions. The integration of reinforcement learning and deep learning, named deep reinforcement learning (DRL), is proposed in this paper as a future tool to deal with the optimization control problems. Following the success of deep reinforcement learning (DRL) in several fields, the deep Q network (DQN) and deep deterministic policy gradient (DDPG) are proposed to harvest the MPP in PV systems, especially under a partial shading condition (PSC). Different from the reinforcement learning (RL)-based method, which is only operated with discrete state and action spaces, the methods adopted in this paper are used to deal with continuous state spaces. In this study, DQN solves the problem with discrete action spaces, while DDPG handles the continuous action spaces. The proposed methods are simulated in MATLAB/Simulink for feasibility analysis. Further tests under various input conditions with comparisons to the classical Perturb and observe (P&O) MPPT method are carried out for validation. Based on the simulation results in this study, the performance of the proposed methods is outstanding and efficient, showing its potential for further applications.

Photovoltaic maximum power point tracking based on IWD-SVM

Photovoltaic system maximum power point tracking (MPPT) has great potential for improvement of power generation. To optimise MPPT, this paper presents a prediction model based on an intelligent water drops optimisation support vector machine (IWD-SVM) for maximum power point working voltage. The Intelligent Water Drops (IWD) algorithm is used to optimise the penalty factor and kernel function parameters of the SVM, thus improving the training efficiency of the learning machine. Based on the optimisation algorithm, the SVM is used to model the PV array, the prediction results are compared to verify the accuracy and effectiveness of the IWD-SVM model. In addition, the IWD-SVM model is compared with the traditional neural network prediction results, which further verifies the validity of the proposed IWD-SVM model.

A novel P&OT-Neville’s interpolation MPPT scheme for maximum PV system energy extraction

Photovoltaic (PV) system posses an optimal operating pointing, termed as Maximum Power Point (MPP). Using Maximum Power Point Tracking (MPPT) algorithm, MPP of PV system has to be tracked continuously in any climatic conditions. In general, traditional Perturb and Observe (P&OT) MPP tracker is widely used among existing controllers. But, P&OT fails to harvest maximum power from solar panel, in addition oscillations around MPP results in low efficiency of the PV system. The contradiction involved in the traditional controller can be addressed as P&OT operates with a fixed step size. Hence, with large step size MPP can be reached quickly but the magnitude of oscillations around MPP are high. Similarly, when P&OT operated with tiny step size magnitude of oscillations can be reduced at the same time PV system consumes much time to reach MPP. In order to eliminate the contradiction involved with traditional MPPT scheme and effectively optimize PV system energy, this paper put forwards a hybrid MPPT scheme based on P&OT and Neville interpolation. The proposed scheme is executed in two stages. In the first stage, P&OT is operated with a large step size till the voltage reaches near to maximum point. In the second stage, Neville interpolation is used to find the maximum power point. The performance of the proposed scheme is compared with Golden Section Search (GSS) and P&OT MPPT controllers. With the proposed scheme the convergence time required to reach MPP is improved greatly. Experimental prototype is designed and developed to verify the performance of the proposed scheme. Experimental and simulation results provide enough evidence to show superiority of the proposed scheme.Article History: Received December 15th 2017; Received in revised form July 16th 2018; Accepted September 12th 2018; Available onlineHow to Cite This Article: Bhukya, M. N. and Kota, V. R. (2018) A Novel PandOT-Neville’s Interpolation MPPT Scheme for Maximum PV system energy extraction. International Journal of Renewable Energy Development, 7(3), 251-262https://dx.doi.org/10.14710/ijred.7.3.251-26

A novel P&OT-Neville’s interpolation MPPT scheme for maximum PV system energy extraction

Photovoltaic (PV) system posses an optimal operating pointing, termed as Maximum Power Point (MPP). Using Maximum Power Point Tracking (MPPT) algorithm, MPP of PV system has to be tracked continuously in any climatic conditions. In general, traditional Perturb and Observe (P&OT) MPP tracker is widely used among existing controllers. But, P&OT fails to harvest maximum power from solar panel, in addition oscillations around MPP results in low efficiency of the PV system. The contradiction involved in the traditional controller can be addressed as P&OT operates with a fixed step size. Hence, with large step size MPP can be reached quickly but the magnitude of oscillations around MPP are high. Similarly, when P&OT operated with tiny step size magnitude of oscillations can be reduced at the same time PV system consumes much time to reach MPP. In order to eliminate the contradiction involved with traditional MPPT scheme and effectively optimize PV system energy, this paper put forwards a hybrid MPPT scheme based on P&OT and Neville interpolation. The proposed scheme is executed in two stages. In the first stage, P&OT is operated with a large step size till the voltage reaches near to maximum point. In the second stage, Neville interpolation is used to find the maximum power point. The performance of the proposed scheme is compared with Golden Section Search (GSS) and P&OT MPPT controllers. With the proposed scheme the convergence time required to reach MPP is improved greatly. Experimental prototype is designed and developed to verify the performance of the proposed scheme. Experimental and simulation results provide enough evidence to show superiority of the proposed scheme.Article History: Received December 15th 2017; Received in revised form July 16th 2018; Accepted September 12th 2018; Available onlineHow to Cite This Article: Bhukya, M. N. and Kota, V. R. (2018) A Novel PandOT-Neville’s Interpolation MPPT Scheme for Maximum PV system energy extraction. International Journal of Renewable Energy Development, 7(3), 251-260https://dx.doi.org/10.14710/ijred.7.3.251-260

A Prediction Model-Guided Jaya Algorithm for the PV System Maximum Power Point Tracking

This paper proposes a novel model-free solution algorithm, the natural cubic-spline-guided Jaya algorithm (S-Jaya), for efficiently solving the maximum power point tracking (MPPT) problem of PV systems under partial shading conditions. A photovoltaic (PV) system which controls the power generation with its operating voltage is considered. As the same as the generic Jaya algorithm, the S-Jaya is free of algorithm-specific parameters. A natural cubic-spline-based prediction model is incorporated into the iterative search process to guide the update of candidate solutions (operating voltage settings) in the S-Jaya and such extension is capable of improving the tracking performance. Simulation studies and experiments are conducted to validate the effectiveness of the proposed S-Jaya algorithm for better addressing PV MPPT problems considering a variety of partial-shading conditions. The performance of the proposed algorithm is benchmarked against the generic Jaya and the particle swarm optimization, which has been widely considered in the model-free MPPT, to demonstrate its advantages. Results of simulation studies and experiments demonstrate that the S-Jaya algorithm converges faster and provides a higher overall tracking efficiency.

Direct solar photovoltaic charging of a high voltage nickel metal hydride traction battery

The electrification of vehicle power trains using batteries and fuel cells is an important technological step forward in the effort to improve the efficiency and reduce the tailpipe emissions of vehicles. The production of electricity and hydrogen in a renewable fashion, such as using solar energy, can provide a clean and sustainable energy source for electric-powered vehicles. In the present work we develop a charging system to prove the concept of direct, efficient, solar-powered charging for battery-electric vehicles using solar photovoltaic-powered charging of the high-voltage nickel-metal hydride (NiMH) battery that is used in the GM 2-mode hybrid system. We utilize a protocol for high-voltage battery charging that was developed in an earlier study that included a DC–DC converter to boost the low-voltage produced by the PV array to the high voltage needed to charge the battery. However, no power conversion was used in the present study. Rather, we use a high-voltage solar array capable of outputting a wide range of voltages, from approximately 250 to 400V in 50V increments, at the photovoltaic system maximum power point. By varying the solar system output voltage we measured the solar energy to battery charging efficiency under a variety of conditions to determine the optimal photovoltaic system configuration. The system and methods developed in this work can be used to efficiently charge a range of battery electric vehicles by adapting the PV system output to the battery charging voltage.

Recent Developments in Maximum Power Point Tracking Technologies for Photovoltaic Systems

In photovoltaic (PV) system applications, it is very important to design a system for operating of the solar cells (SCs) under best conditions and highest efficiency. Maximum power point (MPP) varies depending on the angle of sunlight on the surface of the panel and cell temperature. Hence, the operating point of the load is not always MPP of PV system. Therefore, in order to supply reliable energy to the load, PV systems are designed to include more than the required number of modules. The solution to this problem is that switching power converters are used, that is called maximum power point tracker (MPPT). In this study, the various aspects of these algorithms have been analyzed in detail. Classifications, definitions, and basic equations of the most widely used MPPT technologies are given. Moreover, a comparison was made in the conclusion.