MPPT Modeling and Simulation in PV Systems Using the DNN Method

  • Edi Leksono Institut Teknologi Bandung
  • Robi Sobirin Institut Teknologi Bandung
  • Reza Fauzi Iskandar Institut Teknologi Bandung
  • Putu Handre Kertha Utama Institut Teknologi Bandung
  • Mochammad Iqbal Bayeqi Institut Teknologi Bandung
  • Muhammad Fatih Hasan Institut Teknologi Bandung
  • Irsyad Nashirul Haq Institut Teknologi Bandung
  • Justin Pradipta Institut Teknologi Bandung
DOI: https://doi.org/10.22146/jnteti.v12i4.7931
Keywords: PLTS, MPPT, DC/DC Converter, DNN

Abstract

The maximum power point tracking (MPPT) feature in solar power plants is an essential function in increasing the efficiency of electricity production. The incremental conductance (InC) algorithm controls MPPT, aiming to maximize the output power of photovoltaic (PV) panels and increase the efficiency of the solar power plant system. Even though the InC algorithm is simple and practical, this algorithm tends to lack support in precise switching speeds, is sensitive to the measurement precision level, and is inadequate to eliminate power oscillations due to tight switching cycles. The deep neural network (DNN) algorithm has the potential to answer the challenges of MPPT dynamics. DNN’s learning capabilities enable the controller to better recognize the dynamics of shifts in maximum power values, thereby providing more appropriate contact actuation. The input for the DNN is the duty ratio produced by the InC algorithm. The DNN algorithm was implemented on three DC-to-DC power converter topologies, namely buck, boost, and buck-boost, to determine MPPT performance under standard tests and actual environmental conditions. DNN has demonstrated the ability to reduce oscillation effects, speed up steady-state time, and increase efficiency. In actual environmental conditions, the results showed that the buck converter consistently produced the highest power, followed by the boost and the buck-boost converters. Regarding performance efficiency, the buck converter achieved the highest efficiency at 94.58%, followed by the boost converter at 90.79%. Conversely, the buck-boost converter had the lowest performance efficiency, with an efficiency of 79.34%.

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Published
2023-11-17
How to Cite
Edi Leksono, Robi Sobirin, Reza Fauzi Iskandar, Putu Handre Kertha Utama, Mochammad Iqbal Bayeqi, Muhammad Fatih Hasan, Irsyad Nashirul Haq, & Justin Pradipta. (2023). MPPT Modeling and Simulation in PV Systems Using the DNN Method. Jurnal Nasional Teknik Elektro Dan Teknologi Informasi, 12(4), 265-273. https://doi.org/10.22146/jnteti.v12i4.7931
Issue
Vol 12 No 4: November 2023
Section
Articles

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