A novel maximum power point tracking method based on particle swarm optimization combined with one cycle control

K, Anoop; Nandakumar, M.

doi:10.1109/picc.2018.8384777

Cited by 16 publications

(9 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The constant current technique uses the same operational phenomenon as the constant voltage method and is also known as the fractional short circuit current (FSCC) method [86], [87]. It assumes that the MPP power is proportional to the ratio, which remains between 78% and 92% of the short circuit current ISC according to equation (21).…”

Section: ) Constant Current (Cc) Methodsmentioning

confidence: 99%

Investigation of MPPT Techniques Under Uniform and Non-Uniform Solar Irradiation Condition–A Retrospection

et al. 2020

View full text Add to dashboard Cite

A significant growth in solar photovoltaic (PV) installation has observed during the last decade in standalone and grid-connected power generation systems. The solar PV system has a non-linear output characteristic because of weather intermittency, which tends to have a substantial effect on overall PV system output. Hence, to optimize the output of a PV system, different maximum power point tracking (MPPT) techniques have been used. But, the confusion lies while selecting an appropriate MPPT, as every method has its own merits and demerits. Therefore, a proper review of these techniques is essential. A "Google Scholar" survey of the last five years (2015-2020) was conducted. It has found that overall seventy-one review articles are published on different MPPT techniques; out of those seventy-one, only four are on uniform solar irradiance, seven on non-uniform and none on hybrid optimization MPPT techniques. Most of them have discussed the limited number of MPPT techniques, and none of them has discussed the online and offline under uniform and hybrid MPPT techniques under non-uniform solar irradiance conditions all together in one. Unfortunately, very few attempts have made in this regard. Therefore, a comprehensive review paper on this topic is need of time, in which almost all the well-known MPPT techniques should be encapsulated in one paper. This article focuses on classifications of online, offline, and hybrid optimization MPPT algorithms, under the uniform and non-uniform irradiance conditions. It summarizes various MPPT methods along with their mathematical expression, operating principle, and block diagram/flow charts. This research will provide a valuable pathway to researchers, energy engineers, and strategists for future research and implementation in the field of maximum power point tracking optimization.

show abstract

Section: ) Constant Current (Cc) Methodsmentioning

confidence: 99%

Investigation of MPPT Techniques Under Uniform and Non-Uniform Solar Irradiation Condition–A Retrospection

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Partial Shading Conditions) Genelde MGNT metotlarının çoğu, tüm panellerin aynı atmosferik koşullarda çalıştığını varsaymaktadır. Ancak bu varsayım her zaman doğru olmayabilir [9]. Güneş panellerinin performansını etkileyen faktörlerden biri de gölgelemedir.…”

Section: Kısmi Gölgeleme Koşullarının Etkisi (Effect Ofunclassified

“…Her MGNT algoritması temelde verilen bir fonksiyonun maksimumunu bulan bir optimizasyon algoritmasıdır. Bu sebeple MGNT'nin hızı ve karmaşıklığı kullanılan araştırma yöntemine bağlıdır [9].…”

Section: Gi̇ri̇ş (Introduction)unclassified

Fotovoltaik Sistemlerde Maksimum Güç Noktası Takibinde Kullanılan Algoritmaların Kısmi Gölgeleme Koşulları Altında Karşılaştırılması

Gümüş

Demirtaş

2021

Politeknik Dergisi

View full text Add to dashboard Cite

Fotovoltaik (FV) sistemlerde verimliliği arttırmak için güç elektroniği dönüştürücüleri yardımıyla maksimum güç noktası takibi (MGNT) işlemi yapılmaktadır. Eşit dağılımlı ışıma koşullarında MGNT yapılırken geleneksel algoritmalardan biri olan değiştir ve gözle (D&G) algoritması oldukça verimlidir. Ancak kısmi gölgeleme koşulları meydana geldiğinde bu algoritma global maksimum güç noktasını bulamamakta ve yerel maksimum güç noktalarına takılmaktadır. Buna karşın parçacık sürü optimizasyonu (PSO) ve guguk kuşu optimizasyon (GKO) algoritması gibi doğadan esinlenen meta sezgisel algoritmalar global maksimum noktanın bulunmasında daha başarılı olmaktadır.Bu çalışmada MATLAB/SIMULINK’de FV dizi, DA-DA yükselten dönüştürücü ve yükten oluşan bir sistem geliştirilmiştir. Bu sistem kullanılarak kısmi gölgeleme koşulları altında D&G, PSO ve GKO algoritmalarıyla MGNT işlemi gerçekleştirilmiş ve bu algoritmaların karşılaştırmalı analizi yapılmıştır. Bu algoritmalar üç farklı kısmi gölgeleme konfigürasyonu ile takip hızı ve doğruluğu açısından birbiriyle kıyaslanmıştır. Simülasyonlar sonucunda, D&G algoritması yerel bir maksimum güç noktasına yakalanırken PSO ve GKO algoritmaları global maksimum güç noktasının bulunmasında başarılı olmuştur. PSO ve GKO algoritması birbiriyle kıyaslandığında ise GKO algoritmasının PSO algoritmasından daha hızlı bir şekilde global maksimum güç noktasına ulaştığı görülmüştür.

show abstract

“…It is a heuristic method for resolving MPPT optimization problem. The position of a particle represents the possible solution and the duty ration represents the solution space [53]. PSO is proven to give a better-fitted result with every iteration, which is based on the concept of bird flocking.…”

Section: Psomentioning

confidence: 99%

Artificial Intelligence Based MPPT Techniques for Solar Power System: A review

Yap

Sarimuthu

Lim

2020

Journal of Modern Power Systems and Clean Energy

175

View full text Add to dashboard Cite

In the last decade, artificial intelligence (AI) techniques have been extensively used for maximum power point tracking (MPPT) in the solar power system. This is because conventional MPPT techniques are incapable of tracking the global maximum power point (GMPP) under partial shading condition (PSC). The output curve of the power versus voltage for a solar panel has only one GMPP and multiple local maximum power points (MPPs). The integration of AI in MPPT is crucial to guarantee the tracking of GMPP while increasing the overall efficiency and performance of MPPT. The selection of AI-based MPPT techniques is complicated because each technique has its own merits and demerits. In general, all of the AI-based MPPT techniques exhibit fast convergence speed, less steady-state oscillation and high efficiency, compared with the conventional MPPT techniques. However, the AI-based MPPT techniques are computationally intensive and costly to realize. Overall, the hybrid MPPT is favorable in terms of the balance between performance and complexity, and it combines the advantages of conventional and AI-based MPPT techniques. In this paper, a detailed comparison of classification and performance between 6 major AI-based MPPT techniques have been made based on the review and MATLAB/Simulink simulation results. The merits, open issues and technical implementations of AI-based MPPT techniques are evaluated. We intend to provide new insights into the choice of optimal AI-based MPPT techniques. Index Terms-Maximum power point tracking (MPPT), artificial intelligence (AI), fuzzy logic control (FLC), artificial neural network (ANN), genetic algorithm (GA), swarm intelligence (SI), machine learning (ML).

show abstract

A novel maximum power point tracking method based on particle swarm optimization combined with one cycle control

Cited by 16 publications

References 10 publications

Investigation of MPPT Techniques Under Uniform and Non-Uniform Solar Irradiation Condition–A Retrospection

Investigation of MPPT Techniques Under Uniform and Non-Uniform Solar Irradiation Condition–A Retrospection

Fotovoltaik Sistemlerde Maksimum Güç Noktası Takibinde Kullanılan Algoritmaların Kısmi Gölgeleme Koşulları Altında Karşılaştırılması

Artificial Intelligence Based MPPT Techniques for Solar Power System: A review

Contact Info

Product

Resources

About