A New MPPT based on Queen Honey Bee Migration (QHBM) in Stand-alone Photovoltaic

Wibowo, Kusmayanto Hadi; Aripriharta, Aripriharta; Fadlika, Irham; Horng, Gwo-Jiun; Wibawanto, Slamet; Saputra, Fitrah Wicaksono Yunianto

doi:10.1109/i2cacis.2019.8825025

Cited by 5 publications

(7 citation statements)

References 15 publications

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“…QHBM sudah beberapa kali di aplikasikan dalam berbagai hal dan dalam prosesnya algoritma QHBM mampu melakukan pelacakan dengan baik dan kestabilan sistem yang bagus. Ketika lebah ratu menemukan lokasi yang cocok, mereka akan berhenti berpindah dan mulai membangun sarang di tempat tersebut [13]. Dalam penelitian ini akan dilakukan uji pelacakan daya, kestabilan sistem, kecepatan konvergensi, dan besar nilai error yang dihasilkan masing-masing MPPT dalam keadaan transisi berbayang.…”

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.

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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

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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.

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“…We have found a bio-inspired optimization method called queen honey bee migration (QHBM), which has proven to be feasible for WSN routing optimization [5], even, it can be used for maximum power tracking (MPPT) [55]. This method was adopted from the behavior of young queens on honeybee colony who wander to make their own hives.…”

Section: Figure 1 Cluster-based Data Transmissionmentioning

confidence: 99%

A New Bio-Inspired for Cooperative Data Transmission of IoT

et al. 2020

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The original queen honey bee migration (QHBM) was developed for independent action on solving efficient mobile routing in WSN. In this paper, we enhanced the original QHBM using Binary testing injection on the cooperative node's selection on IoT system. We also added a new cost function for making nodes' coalition, implementing the threshold value for modified QHBM (mQHBM for short), and demonstrating the mQHBM-CMIMO in fair comparison with another previous algorithms. Our research portrayed that mQHBM can perform better than its competitors such as Fuzzy-BT, Neuro Fuzzy and PSO in terms of network lifetime and the end to end delay.

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“…We demonstrated QHBM with simulation and implementation on hardware, which is quite successful under normal conditions [26]. However, QHBM also encountered practical problems when implemented to deal with shadow problems or sudden changes in irradiation.…”

Section: Introductionmentioning

confidence: 95%

“…Heuristic or metaheuristic, which is sometimes called soft computing or artifcial intelligence techniques for MPPT [1], consists of, i.e., artifcial neural network (ANN) [4][5][6], fuzzy [7][8][9][10], particle swarm optimization (PSO) [7,[11][12][13][14][15][16][17][18][19], gray wolf optimization (GWO) [20][21][22][23], cuckoo search (CS) [23,24], and queen honey bee migration (QHBM) [25,26]. Moreover, MPPT with the original QHBM has only been tested on a normal system [26]. Heuristic MPPTs have fast convergence speeds, small oscillations, and good tracking rates in both normal and shaded conditions.…”

Section: Introductionmentioning

confidence: 99%

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The Performance of a New Heuristic Approach for Tracking Maximum Power of PV Systems

Aripriharta

Wibowo

Fadlika

et al. 2022

Applied Computational Intelligence and Soft Computing

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This paper presents a new heuristic method for maximum power point tracking (MPPT) in PV systems under normal and shadowing situations. The proposed method is a modification of the original queen honey bee migration (QHBM) to shorten the computation time for the maximum power point (MPP) in PV systems. QHBM initially uses random target locations to search for targets, in this case, MPP. So, we adjusted it to be able to do MPP point quests quickly. We accelerated the mQHBM learning process from the original randomly. We had fairly compared the mQHBM with several heuristics. Simulations were carried out with 2 scenarios to test the mQHBM. Based on the simulation results, it was found that mQHBM was able to exceed the capabilities of other methods such as original QHBM, particle swarm optimization (PSO) and perturb and observe (P&O), ANN, gray wolf (GWO), and cuckoo search (CS) in terms of MPPT speed and overshoot. However, the accuracy of mQHBM cannot exceed QHBM, ANN, and GWO. But still, mQHBM is better than PSO and P&O by about 15% and 18%, respectively. This experiment resulted in a gap of about 2% faster in speed, 0.34 seconds better in convergence time, and 0.2 fewer accuracies.

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A New MPPT based on Queen Honey Bee Migration (QHBM) in Stand-alone Photovoltaic

Cited by 5 publications

References 15 publications

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

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

A New Bio-Inspired for Cooperative Data Transmission of IoT

The Performance of a New Heuristic Approach for Tracking Maximum Power of PV Systems

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