PERFORMANCE ANALYSIS OF MPPT-QHBM ON SOLAR PANEL SYSTEMS UNDER UNCERTAIN IRRADIATION CONDITIONS

Authors

  • Suhiro Wongso Susilo Universitas Negeri Malang, Jl. Semarang No.5, Sumbersari, Malang, 65145 Indonesia
  • Aripriharta Aripriharta Universitas Negeri Malang, Jl. Semarang No.5, Sumbersari, Malang, 65145 Indonesia
  • Arya Kusumawardana Universitas Negeri Malang, Jl. Semarang No.5, Sumbersari, Malang, 65145 Indonesia
  • Langlang Gumilar Universitas Negeri Malang, Jl. Semarang No.5, Sumbersari, Malang, 65145 Indonesia
  • Muhammad Afnan Habibi Universitas Negeri Malang, Jl. Semarang No.5, Sumbersari, Malang, 65145 Indonesia
  • Sujito Sujito Universitas Negeri Malang, Jl. Semarang No.5, Sumbersari, Malang, 65145 Indonesia
  • Mohamad Rodhi Faiz Universitas Negeri Malang, Jl. Semarang No.5, Sumbersari, Malang, 65145 Indonesia
  • Saodah Omar Universiti Teknologi MARA, Jalan Ilmu 1/1, 40450 Shah Alam, Selangor, Malaysia

Keywords:

Queen Honeybee Migration, Photovoltaic, Solar Power Plant, Uncertain Irradiation, Monte Carlo

Abstract

The utilization of solar energy is an important solution in meeting renewable energy needs, especially in Indonesia which has high irradiation potential. However, photovoltaic (PV) systems face challenges in the form of irradiation fluctuations and partial shading effects that reduce efficiency. To overcome this, a reliable Maximum Power Point Tracking (MPPT) algorithm is needed. This study analyzes the performance of the Queen Honeybee Migration (QHBM) algorithm in tracking the maximum power point (MPP) under uncertain irradiation conditions in the Tulungagung region, using a Monte Carlo simulation approach. Simulations were conducted using MATLAB in two scenarios: standard conditions (1000 W/m²) and fluctuating conditions based on historical data. Results show that the QHBM achieves 99.98% efficiency with the fastest convergence time (5 iterations) under STC conditions, as well as an average efficiency of 98.99% (normal) and 97.86% (abnormal) under fluctuating conditions. In addition, the system successfully charged the battery, with SOC increasing by 0.038% (optimal) and 0.026% (volatile). The QHBM algorithm is proven to be adaptive to irradiation dynamics and superior to GWO, PSO, and P&O, making it a potentially effective solution for PV systems operating under changing irradiation conditions throughout the day

References

Enciso Contreras, E., Saldaña, J. G. B., Alejo, J. de la C., Torres, C. D. C. G., Bernal, J. A. J., & Vazquez, M. B. A. (2023). A feasibility analysis of a solar power plant with a direct steam generation system in Sonora, Mexico. Energies, 16(11), 4388. https://doi.org/10.3390/en16114388

Iakovleva, E., Guerra, D., Tcvetkov, P., & Shklyarskiy, Y. (2022). Technical and economic analysis of modernization of solar power plant: A case study from the Republic of Cuba. Sustainability, 14(2), 822. https://doi.org/10.3390/su14020822

Asian Development Bank. (2020). Energy sector assessment, strategy, and road map: Indonesia. Asian Development Bank.

Kementerian Energi dan Sumber Daya Mineral Republik Indonesia. (2012). Matahari untuk PLTS di Indonesia. https://www.esdm.go.id/id/media-center/arsip-berita/matahari-untuk-plts-di-indonesia

Simanjuntak, U. (2023). Draf CIPP targetkan 44 persen bauran energi terbarukan pada 2030. Institute for Essential Services Reform. https://iesr.or.id/draf-cipp-targetkan-44-persen-bauran-energi-terbarukan-pada-2030

Solargis Prospect. (2025). Solargis prospect Tulungagung. https://apps.solargis.com/prospect/map?show-registration=1&c=-8.066028,111.900558,11&s=-8.066144,111.90089&m=solargis-dni&l=true

Tiger. (2024). Kelebihan PLTS di Tulungagung. PT Reja Aton Energi. https://atonergi.com/kelebihan-plts-di-tulungagung/

Bappeda Tulungagung. (2025). Solar cell di Pantai Sine. Badan Perencanaan Pembangunan Daerah Tulungagung. https://bappeda.tulungagung.go.id/post/solar-cell-di-pantai-sine

Alghamdi, H., et al. (2023). Smart optimization of semiconductors in photovoltaic-thermoelectric systems using recurrent neural networks. International Journal of Energy Research, 1–18. https://doi.org/10.1155/2023/6927245

Amal, Z. (2024). Advanced perturb and observe algorithm for maximum power point tracking in photovoltaic systems with adaptive step size. Journal of Automation, Mobile Robotics and Intelligent Systems, 55–60. https://doi.org/10.14313/JAMRIS/3-2024/22

Almutairi, A., Abo-Khalil, A. G., Sayed, K., & Albagami, N. (2020). MPPT for a PV grid-connected system to improve efficiency under partial shading conditions. Sustainability, 12(24), 10310. https://doi.org/10.3390/su122410310

Touti, E., Rafikiran, S., Aoudia, M., Alrougy, I. M., Khan, B., & Ali, A. (2024). A new single switch universal supply voltage DC-DC converter for PV systems with MGWM-AFLC MPPT controller. Scientific Reports, 14(1), 12103. https://doi.org/10.1038/s41598-024-62171-3

Jamshidi, F., Salehizadeh, M. R., Yazdani, R., Azzopardi, B., & Jately, V. (2023). An improved sliding mode controller for MPP tracking of photovoltaics. Energies, 16(5), 2473. https://doi.org/10.3390/en16052473

Chatterjee, K., Chattopadhyay, T., & S. K. (2024). Advancing solar power efficiency: Innovations in material science and system optimization for enhanced solar energy conversion. Climate, Economics and Social Impact, 2(1), 1–15. https://doi.org/10.56868/cesi.v2i1.13

Kamil, I., Bagaskoro, M. C., & Susilo, S. W. (2024). Rancang bangun PLTS on-grid sebagai penunjang kelistrikan. Jurnal Energi Terbarukan, 2(September), 73–82.

Xu, L., Cheng, R., & Yang, J. (2020). A new MPPT technique for fast and efficient tracking under fast varying solar irradiation and load resistance. International Journal of Photoenergy, 2020, 1–18. https://doi.org/10.1155/2020/6535372

Maniar, T. B. (2024). Application of DC-DC converter for grid connected inverter using PV cell. Journal of Electrical Systems, 20(7s), 2613–2620. https://doi.org/10.52783/jes.4097

Dey, B. K., Khan, I., Mandal, N., & Bhattacharjee, A. (2016, October). Mathematical modelling and characteristic analysis of solar PV cell. In 2016 IEEE 7th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) (pp. 1–5). IEEE. https://doi.org/10.1109/IEMCON.2016.7746318

Aripriharta, A., Surya, Y. S., Susilo, S. W., Mustika, S. N., & S.- -. (2023). Perancangan sistem kontrol dan pengawasan smart PJUTS berbasis mikrokontroler. Jurnal Media Elektro, XII(1), 38–48. https://doi.org/10.35508/jme.v0i0.10082

Premkuma, M., et al. (2020). Certain study on MPPT algorithms to track the global MPP under partial shading on solar PV module/array. International Journal of Computing and Digital Systems, 8(4), 405–416. https://doi.org/10.12785/ijcds/080409

Pandey, A., & Pattnaik, S. (2022). Design and analysis of extendable switched-inductor and capacitor-divider network based high-boost DC-DC converter for solar PV application. IEEE Access, 10(June), 66992–67007. https://doi.org/10.1109/ACCESS.2022.3185107

Harmini, H., & Nurhayati, T. (2020). Desain inverter solar power pada sistem fotovoltaik. Elektrika, 12(1), 1. https://doi.org/10.26623/elektrika.v12i1.1863

Pokorádi, L. (2022). Monte-Carlo simulation-based accessibility analysis of temporal systems. Symmetry, 14(5), 983. https://doi.org/10.3390/sym14050983

Salazar-Peña, N., Tabares, A., & González-Mancera, A. (2023). Sequential stochastic and bootstrap methods to generate synthetic solar irradiance time series of high temporal resolution based on historical observations. Solar Energy, 264, 112030. https://doi.org/10.1016/j.solener.2023.112030

Chen, D.-G., & Chen, J. D. (Eds.). (2017). Monte-Carlo simulation-based statistical modeling. Springer Singapore. https://doi.org/10.1007/978-981-10-3307-0

Aripriharta, A., et al. (2022). The performance of a new heuristic approach for tracking maximum power of PV systems. Applied Computational Intelligence and Soft Computing, 2022, 1996410. https://doi.org/10.1155/2022/1996410

Aripriharta, A., et al. (2024). The enhanced self-lift Luo converter with QHBM for maximum power extraction on PV charging station. Frontiers in Energy Systems and Power Engineering, 5(2), 65–80. https://doi.org/10.17977/um049v5i2p65-80

Aripriharta, A., Hao, W. Z., Muladi, Horng, G. J., & Jong, G. J. (2020). A new bio-inspired approach for cooperative data transmission of IoT. IEEE Access, 8, 161884–161893. https://doi.org/10.1109/ACCESS.2020.3021507

Wibowo, K. H., Aripriharta, Fadlika, I., Horng, G. J., Wibawanto, S., & Saputra, F. W. Y. (2019, June). A new MPPT based on queen honey bee migration (QHBM) in stand-alone photovoltaic. In 2019 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS) (pp. 123–128). IEEE. https://doi.org/10.1109/I2CACIS.2019.8825025

Aripriharta, A., Asnarindra, E., Nibrosoma, A. D., Gumilar, L., & Habibi, M. A. (2023, July). Pelacakan daya maksimum fotovoltaik dalam keadaan transisi berbayang menggunakan algoritma MPPT Queen Honey Bee Migration (QHBM). Transmisi: Jurnal Ilmiah Teknik Elektro, 25(3), 85–94. https://doi.org/10.14710/transmisi.25.3.85-94

Aripriharta, A., et al. (2023). Comparison of queen honey bee colony migration with various MPPTs on photovoltaic system under shaded conditions. EUREKA: Physics and Engineering, 2023(4), 52–62. `https://doi.org/10.21303/2461-426.2023.002836

Mukti, E. W., Risdiyanto, A., Kristi, A. A., & Darussalam, R. (2023, December). Particle swarm optimization (PSO) based photovoltaic MPPT algorithm under the partial shading condition. Jurnal Elektronika dan Telekomunikasi, 23(2), 99. `https://doi.org/10.5591/jet.552

Sekar, K., Arasan, E., & Chandrasekaran, K. (2023, June). Grey wolf optimization and fed fast terminal sliding mode controllers based on interleaved boost converters for symmetric PV systems under asymmetric partial shading. Symmetry, 15(7), 1339. `https://doi.org/10.339/sym15071339

Asnil, A., Nazir, R., Krismadinata, K., & Sonni, M. N. (2024, May). Performance analysis of an incremental conductance MPPT algorithm for photovoltaic systems under rapid irradiance changes. TEM Journal, 13(2), 1087–1094. `https://doi.org/10.18421/TEM13223

Wirateruna, E. S., Afroni, M. J., & Ayu, A. F. (2023, May). Implementation of PSO algorithm on MPPT PV system using Arduino Uno under PSC. International Journal of Artificial Intelligence and Robotics, 5(1), 13–20. `https://doi.org/10.25139/ijair.v5i1.6029Elahi, M., Ashraf, H. M., & Kim, C.-H. (2022, February). An improved partial shading detection strategy based on chimp optimization algorithm to find global maximum power point of solar array system. Energies, 15(4), 1549. `https://doi.org/10.3390/en1504159

Charu, K., Thakur, P., & Ansari, F. (2023). Performance analysis of conventional MPPT techniques for a solar PV system. Journal of Graphic Era University, 11(Cc), 117–132. `https://doi.org10.13052/jgeu0975-1416.1121

Indonesia Solar Map. (2023). Data solar iradiasi Tulungagung. Retrieved from https://indonesiasolarmap.com/map

Global Solar Atlas. (2025). Data Tulungagung, Jawa Timur -08.029315°, 111.862592°. Retrieved from https://globalsolaratlas.info/detail?c=-8.069961,111.90144,11&m=site&s=-8.06996,111.90144&pv=small,0,12,0.42

TrinaSolar. (2022). Backsheet monocrystalline module product: TSM-DE09R range: 415–435W. Retrieved from https://www.trinasolar.com/us

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Published

20-08-2025

Issue

Vol. 5 No. 8 (2025)

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Articles