A Novel Algorithmic Framework for Precise PV Curve Reconstruction and Global MPP Detection Under Non-Uniform Irradiance
- DOI
- 10.2991/978-94-6239-750-7_22How to use a DOI?
- Keywords
- Solar PV; Irradiation; Shading; Maximum Power
- Abstract
Accurate modeling of photovoltaic (PV) Modules under non-uniform irradiance is essential for improving maximum power point tracking (MPPT), fault detection and power forecasting. existing tools either assume uniform irradiation or lack explicit sub module bypass diode modelling which is required to reproduced multi-peak PV Characteristics for (i) a single module using the De Soto five parameter model. (ii)partially shaded modules using explicit by pass-diode behavior. The implementation reproduces stepped I-V curves and multiple local maxima as observed in real PV installations. The mathematical model is derived exactly from the implementation source files, and all results shown are generated using this library. Comparative simulations demonstrate the capability of proposed framework to accurately locate global and local MPPs under diverse shading patterns making it useful for MPPT research and PV design studies.
- Copyright
- © 2026 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Asad Zai AU - Khamma Kanwar PY - 2026 DA - 2026/08/31 TI - A Novel Algorithmic Framework for Precise PV Curve Reconstruction and Global MPP Detection Under Non-Uniform Irradiance BT - Proceedings of the International Conference on Advanced Design, Manufacturing, and Sustainable Energy Systems (ICADMSES 2026) PB - Atlantis Press SP - 304 EP - 322 SN - 2589-4943 UR - https://doi.org/10.2991/978-94-6239-750-7_22 DO - 10.2991/978-94-6239-750-7_22 ID - Zai2026 ER -