Global Maximum Power Point Research Articles

A communication-based method for PSC Detection and GMP tracking under PSC

This study proposes a novel communication-based method for tracking global maximum power point (GMP) under partial shading condition (PSC) for a rooftop photovoltaic (PV) systems. The proposed method uses communication between converters to detect the occurrence of PSC and finds the GMP. The method does not need to find all local maximum points (LMPs) to determine GMP. Consequently, the energy loss due to blind scan is avoided and the time of tracking GMP is reduced. In addition, the starting points for searching LMPs are defined which are very close to the LMPs. Furthermore, the system identifies repetitive shadow on the module and informs the operator to cope with the problem. The proposed method by using real-time measurements is immune to the change of module s parameters with respect to aging and environmental conditions(temperature and irradiance level). Simulation and experimental results demonstrate the effectiveness of the proposed method.

GMPP Tracking of Solar PV system using Spotted Hyena and Quadratic Approximation based Hybrid Algorithm under Partially shaded conditions

The demand for clean and renewable energy is increasing due to environmental concerns. Photovoltaic (PV) system is very popular from the renewable sources and its usage is increasing. The PV system power transfer efficiency depends on several factors such electrical characteristics of loads, solar irradiation, shading condition and panel temperature. The process of transferring maximum possible power to load from PV system by adjusting the system parameters is called maximum power point tracking (MPPT). Under the partially shaded conditions, multiple power peaks are created in the PV curves and makes the MPPT is a non-convex problem. It is a challenging problem to track the global maximum power point (GMPP) under partially shaded conditions. It is essential to recognize the GMPP and restart the string as early as possible to prevent the physical as well as economical damage. In this paper, a new hybrid algorithm for MPPT is proposed and called as QASHO, a combination of Spotted Hyena optimization (SHO) and Quadrature approximation (QA) technique. The QASHO is used to track the GMPP under different weather conditions such as partial shading. The simulation results are compared with Perturb and Observe (P&O) algorithm and Cuckoo Search optimization (CSO) algorithm and promised for better performance in tracking GMPP. The experimental results are provided to validate the proposed MPPT methodology.

An Improved PSO-Based MPPT Technique Using Stability and Steady State Analyses Under Partial Shading Conditions

Partial shading (PS) conditions create challenging issues for the maximum power point tracking (MPPT) algorithms in photovoltaic (PV) systems such as getting stuck in the local maximum power points (LMPP), slow tracking time and fluctuations in the generated power during tracking the global maximum power point (GMPP). To address these issues, this paper brings contributions by proposing an improved PSO-based MPPT technique tailored for PS conditions. First, to highlight the weakness of the traditional PSO-MPPT technique, its stability and steady state behavior in the PS conditions is analyzed in depth. Second, the required criteria to achieve a stable response are obtained. Finally, a novel technique to estimate the convex area of the power versus voltage (P-V) curve is presented where the GMPP is located using two voltage boundaries. The performance of the proposed MPPT technique is experimentally validated. The results highlight the capabilities of the proposed technique in finding the GMPP with a rapid convergence and small fluctuations. The proposed MPPT is applicable to most PV inverters under any PS conditions.

New ARMO-based MPPT Technique to Minimize Tracking Time and Fluctuation at Output of PV Systems under Rapidly Changing Shading Conditions

The presence of bypass diodes that mitigate the negative effects of partial shading (PS) conditions produces multiple peak characteristics at the output of a photovoltaic (PV) array. Conventional maximum power point tracking (MPPT) methods develop errors under certain circumstances and detect the local maximum power point (LMPP) instead of the global maximum power point (GMPP). Several artificial intelligence (AI)-based methods have been used to modify the performance of conventional controllers. However, these methods have either not completely solved the PS problem or resulted in considerably complicated and unreliable methodologies, as well as require further development to be used in high-speed applications. This study aims to design, develop, and verify a novel rapid, reliable, and cost-effective method called adaptive radial movement optimization (ARMO) to diminish the effect of the PS problem in the MPP detection for PV systems with additional dynamic applications. The main advantages of ARMO are its improved tracking speed and significant reduction in output fluctuations during the tracking period. An extensive experimental verification has been conducted to provide a fair evaluation of the proposed method compared with conventional and recently developed methods under similar conditions while being applied to a unique PV system and DC/DC converter.

Improved Global Maximum Power Point Tracking Technique for a Partially Shaded Solar Photovoltaic Array Using Capacitor Transient Effect

Improved Global Maximum Power Point Tracking Technique for a Partially Shaded Solar Photovoltaic Array Using Capacitor Transient Effect

Probabilistic Global Maximum Power Point Tracking Algorithm for Continuously Varying Partial Shading Conditions on Autonomous PV Systems

Probabilistic Global Maximum Power Point Tracking Algorithm for Continuously Varying Partial Shading Conditions on Autonomous PV Systems

A Novel ANN-Based GMPPT Method for PV Systems Under Complex Partial Shading Conditions

In this paper, a new two-stage global maximum power point tracking (GMPPT) algorithm based on artificial neural network (ANN) is proposed. The novel ANN architecture is presented first, which requires fewer sampling points than other ANN-based GMPPT approaches, thereby reducing both the tracking time and power loss involved in the tracking. In addition, it does not require costly illuminance or temperature sensors and can be realized using a low-cost digital signal controller. According to the simulation results, the proposed method has the best performance among all methods in terms of tracking speed, tracking accuracy, and tracking loss for all the three tested shading patterns (SPs). The simulated results of 252 SPs show that the performance indexes (PIs) of the proposed method are the best among all the compared methods, which are: the average tracking time 0.18 seconds, average power loss 0.01 W. In addition, the proposed method can correctly predict the GMPP positions of all 252 SPs. Furthermore, the PIs of the proposed method are also the best among all the compared methods according to the experimental results, which are: the tracking speed 0.21 seconds, tracking accuracy 99.66%, and tracking loss 12.58 W, all the above are average values.

New Global Maximum Power Point Tracking Technique Based on Indirect PV Array Voltage Control for Photovoltaic String Inverters With Reduced Number of Sensors

New Global Maximum Power Point Tracking Technique Based on Indirect PV Array Voltage Control for Photovoltaic String Inverters With Reduced Number of Sensors

Real-Time Implementation of a Hybrid ESC Approach for Maximising the Extracted Photovoltaic Power Under Partial Shading Conditions

Abstract Solar energy, an available and renewable resource, can be efficiently transformed into electrical energy through the use of photovoltaic (PV) cells. The primary emphasis lies in the significance of maximising power output for economic considerations. In terms of optimising power generation, the implementation of maximum power point tracking (MPPT) techniques is imperative. A range of approaches, such as super twisting (ST) control and modified extremum seeking control (ESC-mod), are explored for their potential in enhancing the efficiency of power-generation systems. The novelty is a combination of these methods; the modified ESC has the role of finding the optimum voltage value of the global maximum power point (MPP) during the partial shading, while the super-twisting improves the performance of the system. The efficacy of the MPPT algorithm is assessed across diverse conditions, encompassing scenarios with load variations and fluctuating irradiances (uniform and non-uniform). The experimental setup involves essential components such as a PV generator, a boost converter and a resistive load. This comprehensive testing aims to evaluate the algorithm’s performance under varying circumstances, providing insights into its adaptability and effectiveness across different operational conditions. The system is modelled, simulated using Matlab–Simulink and implemented using a dSPACE 1104 card. Simulation results indicate that ST control is faster in reaching the permanent regime, but ESC-mod provides smoother performance in the permanent regime. The integration of both ST control and ESC-mod methods proves advantageous by diminishing the response time in the seeking process while concurrently ensuring a consistent and smooth operation in the permanent regime. This combined approach has undergone practical implementation and testing across diverse conditions, encompassing both optimal, healthy states and shaded environments. The results affirm the method’s ability to deliver efficient and stable performance across a spectrum of operating conditions.

Study and analysis of partial shading effect on power production of a photovoltaic string controlled by three different MPPT techniques: P&O, PSO and ANN

Partial shading occurs when some of the solar panels are exposed to reduced irradiation. Partial shading can lead to creating peaks and troughs in power production. The goal of this study is to compare the effect of partial shading on the capacity of maximum power point tracking (MPPT) methods, to find the global maximum power point. To this end, the study focuses on performance simulation and discussion of Perturb and Observe (P&O), Particle Swarm Optimization (PSO), and Artificial Neural Network (ANN) controls. Analysing the three MPPT controller's results, in terms of accuracy, the ANN and PSO controls showed high performance. On the other hand, the P\&O control showed lower accuracy, particularly under partial shading. For the speed of reaction, the P&O and ANN controls proved to be the fastest, while the PSO control showed a slightly longer response time. However, it is important to note that ANN approach presents added complexity in terms of conception.

A Novel Improved Grey Wolf Algorithm Based Global Maximum Power Point Tracker Method Considering Partial Shading

A Novel Improved Grey Wolf Algorithm Based Global Maximum Power Point Tracker Method Considering Partial Shading

Particle Swarm Optimization (PSO) based Photovoltaic MPPT Algorithm under the Partial Shading Condition

Performance of solar photovoltaic (PV) system degrades considerably when the surface of the PV array is covered with shadows indicated by the reduction in the generated power. Partial shading condition causes the PV array to produce multiple peaks in PV’s characteristic curve causing the non-optimal power generation of the conventional maximum power point (MPPT) algorithm. Therefore, we proposed an optimization of PV system through the design of particle swarm optimization (PSO) algorithm in the MPPT in order to enhance the power extraction during shaded conditions. The evaluation of the performance of MPPT were carried out by comparing tracking time, tracking error and efficiency obtained by the PSO along with the two most used algorithms namely perturb and observe (P&O) and incremental conductance (IC). A partial shading generator function was designed to generate shading patterns randomly. The PV system consists of ten PV modules integrated with DC dummy load through DC-DC boost converter was simulated using MATLAB/SIMULINK. Simulation results show that the proposed PSO could obtain the global maximum power point (GMPP) while tracking performance of the P&O and IC MPPT both were stuck on the local maxima. Moreover, the optimization of PSO MPPT gave a promising result that the efficiency of the PV system was improved by around 4.66% on the dynamic shading conditions, while the MPPT’s tracking time is slowed down by 0.0025 s and 0.0105 s when its compared to the P&O and IC MPPT respectively.

Enhanced particle swarm optimization and P&O for MPPT of photovoltaic systems under partial shading conditions

Although Photovoltaic Technologies are largely deployed as a renewable energy source, several factors affect their performance. The major factors that affect PV performance are changes in irradiance and temperature. Maximum PowerPoint Tracking of PV output is essential in giving the maximum photovoltaic outputs at variable levels. Instantaneous variation in irradiance and temperature increases the complexity of tracking maximum power points. Partial shading conditions resulting from shade from trees, tall buildings, and Cloud formation amongst others greatly affect PV systems, especially in large Photovoltaic systems. Under the Partial shading condition, P-V curves become more complex as it is characterized by multiple peaks. The conventional PSO is associated with less accuracy in tracking the Global Maximum Power Point (Global MPP) and slow convergence time in obtaining the Global MPPT and oscillations. In this thesis, a modified Particle Swarm Optimization based Maximum Power Point Tracking technique is designed in MATLAB/Simulink to track the global Maximum Power Point of a Photovoltaic system under partial shading. The proposed modified PSO combines conventional PSO and P&O methods. The particle position in the PSO method is given as the duty cycle value d of the DC-DC converter. Conventional PSO equations are used to update the velocities and duty cycle. Thereafter, the maximum velocity and duty cycle are perturbed to reduce convergence time. The designed PV system was simulated in a MATLAB environment for 10 different irradiation levels and results were compared with results from related works. The average convergence time was 0.99 seconds and efficiency was up to 99.8% with the proposed model which performed better than conventional methods.

Comparing MPPT Algorithms for Improved Partial-Shaded PV Power Generations

olar energy, accepted as an alternative energy source, is attracting commercial interest and scholars and researchers for improving efficiency and lowering the losses within the system. One of these significant losses is due to partial and complex shading. This study concentrates on reducing losses to enhance the efficiency of solar systems. Maximum Power Point Tracking (MPTT) uses several alternative algorithms for efficient operations. We have selected four algorithms supporting MPPT, namely P&O, PSO, Adaptive cuckoo, and Dragonfly. These algorithms are applied on photovoltaic (PV) systems in four different scenarios: uniform irradiance, partial shading, complex partial shading, and multiple local maximum power points. According to this study, results show that the algorithms' performance vary significantly based on these scenarios. It has been shown that PSO has the longest tracking time compared to other but tracks the maximum power best when exposed to uniform irradiance. In contrast, DFO takes the shortest tracking time and performs best in I-V curves but do not have a maximum power point at the knee. Both adaptive cuckoo and PSO perform well in tracking the global maximum power point, particularly in partial shadings. The study provides insights into the strengths and weaknesses of each algorithm in different scenarios and can guide the selection of an appropriate algorithm for a given PV system.

PERFORMANCE ANALYSIS OF P&O AND PSO MPPT ALGORITHMS FOR PV SYSTEMS UNDER PARTIAL SHADING

Photovoltaic (PV) modules are devices that transform photon energy into electrical energy. The output power of the PV modules is influenced by the intensity of solar radiation and the ambient temperature. Non-uniform shading can cause variations in the extent of sunlight absorbed by PV modules, resulting in a decrease in power output. Maximum power point tracking (MPPT) techniques are employed to optimize the power output of PV modules by operating them at their maximum power point (MPP). The main objective of the study is to investigate the performance analysis of Perturb and Observe (P&O) and Particle Swarm Optimization (PSO) MPPT strategies in uniform and partially shaded conditions with equally and unequally different irradiance differences. Simulation studies were conducted on the PV circuit model using Matlab/Simulink, and the results were evaluated. MPPT algorithms are compared based on their tracking efficiency and convergence speed when solar radiation conditions vary. The findings of the simulation indicate that the P&O is unable to determine global MPP and gets trapped in one of the local MPPs. However, the PSO is very effective in tracking MPP under different partial shading patterns with more than 96% tracking efficiency. In the first partial shading configuration where the sunlight intensity of the PV modules is uniformly distributed, the PSO technique has reduced steady-state oscillations around the MPP. However, the P&O technique demonstrates superior response time and convergence speed in comparison to the PSO technique.

A new approach to solve the problem of partial shading in a photovoltaic system

<p>This paper introduces a novel global maximum power point (GMPP) tracking method that addresses the challenges of efficiency and power quality degradation in photovoltaic (PV) systems caused by inadequate tracking of the GMPP. The proposed approach employs a cuckoo search algorithm with proportional, integral, and derivative (CSPID). A bio-inspired optimization technique, to effectively track the GMPP under varying weather conditions. To demonstrate its effectiveness, the CSPID algorithm is comprehensively evaluated against two well-established methods, particle swarm optimization (PSO), and cuckoo search algorithm traditional (CSA). The evaluation includes three different scenarios with gradual changes in irradiance and temperature, these tests show the ability of the algorithm to handle the condition of partial shading. The results reveal that the CSPID method achieves an average tracking time of 0.098s and an average tracking efficiency of 99.62%, thereby significantly improving the efficiency and quality of photovoltaic energy production.</p>

Metaheuristics-based maximum power point tracking for PV systems: a review

<span lang="EN-US">Over the years, numerous maximum power point tracking (MPPT) methods have been developed to extract the maximum available power from PV arrays. They are generally categorized as conventional or metaheuristic methods. The most employed conventional methods include perturb and observe (P&O), hill climbing (HC), and incremental conductance (INC), due to their simplicity and ease of implementation. However, under partial shading condition (PSC), none of them can effectively locate a global maximum power point (GMPP) out of many local maximum power points (LMPPs). This results in significant power loss during PSC, prompting the development of various metaheuristic-based MPPT methods to address the problem. This paper reviews 38 existing metaheuristic-based MPPTs and 27 metaheuristic methods that have not yet been applied to any MPPT operation up to date. Metaphorically, these methods are divided into four categories: (i) evolutionary-based, (ii) physics-based, (iii) swarm-based, and (iv) human-based. The different MPPTs are compared in terms of complexity, converter topology, and PSC tracking capability. This paper is intended to serve as a one-stop resource for any researcher, practitioner, or advanced student seeking to develop a new metaheuristic-based MPPT method.</span>

Global Maximum Power Point Tracking of Photovoltaic Systems Using Bio-inspired Algorithm-Based MPPT and One-Cycle Control

Global Maximum Power Point Tracking of Photovoltaic Systems Using Bio-inspired Algorithm-Based MPPT and One-Cycle Control

A comprehensive comparative study on intelligence based optimization algorithms used for maximum power tracking in grid-PV systems

For maximum power point tracking (MPPT) in the solar Photovolatic (PV) system, the meta-heuristic optimization techniques have been widely applied in the last few decades. This is due to the fact that traditional MPPT methodologies are unable to monitor the global MPP in the face of shifting environmental factors. Hence, it is essential to use an intelligence based controlling algorithm for MPPT controlling. The main purpose of this study is to investigate and assess the effectiveness of three cutting-edge and distinctive optimization algorithms for MPPT controlling, including Mongoose Optimization (MO), Prairie Dog Optimization Algorithm (PDOA), and hybrid PDOA + MO. It also aims to select the most effective and sophisticated optimization algorithm to meet the grid systems' energy requirements. This research's original contribution is the implementation and performance evaluation of three alternative meta-heuristic models for MPPT controlling. The goal of this effort is to maximize the energy yield from photovoltaic systems in order to meet the energy demands of grid systems. Three different controlling strategies, including MO + MPPT, PDOA + MPPT, and MO + PDOA + MPPT, are used in this work to achieve this goal. To evaluate the effectiveness and improved performance outcomes, a number of parameters have been taken into account in this work, including time, error, power, THD, and others. Furthermore, using a comprehensive simulation and comparison study, the outcomes of the MO, PDOA, and hybrid PDOA + MO techniques have also been tested and confirmed in this work. Comparisons are also made between the peak, settling, and increasing times of the present and proposed regulatory models. The results and waveforms generated demonstrate that the hybrid PDOA + MO performs better than the other controlling models in terms of enhanced efficiency of 99.5 %, low rising time of 1.6 s, low peak time of 1.05 s, minimal settling time of 1.24 s, error rate of 0.48, response time of 0.005 s, and tracking time of 0.0019 s

An improved 0.8Voc model‐based global maximum power point tracking algorithm for photovoltaic system

AbstractIn this paper, a low‐cost and high‐speed partial shading condition (PSC) detection scheme and global maximum power point tracking (GMPPT) technique are presented. The proposed technique detects the PSC using a low‐cost voltage sensor without setting any threshold parameter. The proposed GMPPT algorithm utilizes the 0.8 model along with the load‐line observation to skip the unwanted interval of – curve. Moreover, in case of dynamic shading, a smart scanning mechanism is used along with the load‐line observation to reduce the voltage iterations which leads to an improvement in tracking speed. The proposed technique is implemented in a Matlab/Simulink environment and compared with the conventional 0.8 method and accurate PSC detection method. The results indicate the following advantages of the proposed algorithm: (1) iteration free and accurate detection of PSC and uniform irradiance condition, (2) enhancement in average tracking time by 67.44% and 69.48%, and (3) improvement in average transient efficiency by 17.36% and 21.25% compared with 0.8 method and accurate PSC detection method. Besides, the experimental results depict that the proposed method enhances the average tracking time by 68.6% and 72.69% and improves the average transient efficiency by 17.62% and 15.06% compared with 0.8 method and accurate PSC detection method, respectively.