Photovoltaic Boost Converter Control Operating in the MPPT and LPPT Modes

Santos, Cassia C. Chapetti dos; Moraes, Cassiano Ferro; Costa, Jean Patric da; Stein, Carlos M. O.; Carati, Emerson Giovani; Cardoso, Rafael

doi:10.1109/cobep/spec44138.2019.9065604

Cited by 5 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…As will be shown later, an inherent challenge of the P&O method arises when the system operates under partial shading conditions, potentially causing it to converge to local maxima instead of reaching its maximum efficiency [6,7]. This iterative process periodically updates itself, leading to oscillations around the MPP rather than stabilization at the precise MPP [42][43][44]. As will be shown later, an inherent challenge of the P&O method arises when the system operates under partial shading conditions, potentially causing it to converge to local maxima instead of reaching its maximum efficiency [6,7].…”

Section: Perturbation and Observation (Pando)mentioning

confidence: 99%

Development of an MPPT-Based Genetic Algorithm for Photovoltaic Systems versus Classical MPPT Techniques in Scenarios with Partial Shading

de Oliveira,

Brandt,

Salvadori

et al. 2024

Inventions

View full text Add to dashboard Cite

Photovoltaic (PV) systems face challenges in achieving maximum energy extraction due to the non-linear nature of their current versus voltage (IxV) characteristics, which are influenced by temperature and solar irradiation. These factors lead to variations in power generation. The situation becomes even more complex under partial shading conditions, causing distortion in the characteristic curve and creating discrepancies between local and global maximum power points. Achieving the highest output is crucial to enhancing energy efficiency in such systems. However, conventional maximum power point tracking (MPPT) techniques often struggle to locate the global maximum point required to extract the maximum power from the PV system. This study employs genetic algorithms (GAs) to address this issue. The system can efficiently search for the global maximum point using genetic algorithms, maximizing power extraction from the PV arrangements. The proposed approach is compared with the traditional Perturb and Observe (P&O) method through simulations, demonstrating its superior effectiveness in achieving optimal power generation.

show abstract

Section: Perturbation and Observation (Pando)mentioning

confidence: 99%

Development of an MPPT-Based Genetic Algorithm for Photovoltaic Systems versus Classical MPPT Techniques in Scenarios with Partial Shading

de Oliveira,

Brandt,

Salvadori

et al. 2024

Inventions

View full text Add to dashboard Cite

show abstract

“…Hal ini dikarenakan GA ketika proses trackingnya masih terdapat ripple sehingga hasilnya kurang maksimal. Berdasarkan kekurangan-kekurangan dari algoritma pendahulunya [10] maka dilakukan pembaruan dengan menggunakan algoritma QHBM yang dianggap lebih unggul pada pencapaian daya maksimal dan konvergensi yang cepat. QHBM sudah beberapa kali di aplikasikan dalam berbagai hal dan dalam prosesnya algoritma QHBM mampu melakukan pelacakan dengan baik dan kestabilan sistem yang bagus.…”

Section: Pendahuluanunclassified

Pelacakan Daya Maksimum Photovoltaic Dalam Keadaan Transisi Berbayang Menggunakan Algoritma MPPT Queen Honey Bee Migration (Qhbm)

Aripriharta,

Asnarindra,

Nibrosoma

et al. 2023

Transmisi J.Ilm. Tek. Elekt.

View full text Add to dashboard Cite

Dalam sistem fotovoltaik, diperlukan teknik untuk memaksimalkan daya listrik output. Salah satu teknik yang digunakan adalah metode Maximum Power Point Tracking (MPPT) yang menemukan titik koordinat daya maksimum (MPP) pada kurva PV. Namun, ada masalah baru dalam MPPT terkait dengan bayangan parsial pada permukaan PV yang tidak merata iradiasinya. Masalah ini dapat mengubah posisi MPP pada kurva PV dan mengganggu kestabilan pencarian MPP selama periode transisi bayangan. Sebuah paper memperkenalkan metode heuristik baru yang disebut Queen Honeybee Migration Algorithm (QHBM) untuk mengatasi masalah ini. Metode ini meniru proses migrasi lebah madu Riau dalam mencari tempat terbaik untuk membangun sarang baru dengan menggunakan tegangan dan arus output panel surya sebagai inisialisasi posisi Queen dan Scouts. Bobot Scout disesuaikan dengan konstanta yang berubah sesuai posisinya dari MPP yang baru. Queen menentukan arah migrasi dengan memilih Scout dengan bobot terendah dan bermigrasi perlahan ke titik MPP sampai beberapa iterasi, kemudian menetap dan membangun sarang di MPP. Simulasi menunjukkan bahwa QHBM memiliki kecepatan komputasi yang cepat dan kestabilan yang baik dalam mencapai konvergensi, serta mampu melacak daya maksimal dalam kondisi irradiasi tertinggi, dibandingkan dengan metode heuristik lain seperti Particle Swarm Optimization (PSO), Perturb and Observe (P&O), Genetic Algorithm (GA).Bottom of Form

show abstract

“…This system was developed and installed in the district of Fongshan by the Taiwan Power Company (Taipower), where the power factor is autonomously adjusted to absorb reactive energy when overvoltages occur in the distribution network [6]. In [7], the maximum power point tracking (MPPT) algorithm was applied, as well the limited power point tracking (LPPT) algorithm was adjusted considering external commands, the electrical grid dispatch capability. The adjustment was designed and implemented using discrete-event flowcharts.…”

Section: Introductionmentioning

confidence: 99%

Supervisory Layer for Improved Interactivity of Distributed Generation Inverters with Smart Grids

Carati

Barbosa

Cardoso

et al. 2021

JSAN

View full text Add to dashboard Cite

This work proposes an autonomous management system for distributed generation (DG) systems connected to the AC grid, using supervisory control theory (SCT). SCT is used to deal with discrete asynchronous events that modify the properties and operational conditions of these systems. The proposed management layer allows the smart inverters to interact with smart grid managers (SGMs), while guaranteeing operation compliance with the IEEE Standards. The implemented supervisor for the management layer is an automaton that performs the smart inverter manager (SIM) functions in the photovoltaic systems in discrete events. A DSP real-time verification was performed with Typhoon HIL 602+ to demonstrate the smart inverter’s operating dynamics connected to the grid. The results showed the fast response and robust operation of the smart inverter manager to the commands from the smart grid manager.

show abstract

Photovoltaic Boost Converter Control Operating in the MPPT and LPPT Modes

Cited by 5 publications

References 12 publications

Development of an MPPT-Based Genetic Algorithm for Photovoltaic Systems versus Classical MPPT Techniques in Scenarios with Partial Shading

Development of an MPPT-Based Genetic Algorithm for Photovoltaic Systems versus Classical MPPT Techniques in Scenarios with Partial Shading

Pelacakan Daya Maksimum Photovoltaic Dalam Keadaan Transisi Berbayang Menggunakan Algoritma MPPT Queen Honey Bee Migration (Qhbm)

Supervisory Layer for Improved Interactivity of Distributed Generation Inverters with Smart Grids

Contact Info

Product

Resources

About