Adaptive and Interpretable MPPT Framework for Photovoltaic Systems under Partial Shading using Integrated AI Modules

International Journal of Electrical and Electronics Engineering

© 2025 by SSRG - IJEEE Journal

Volume 12 Issue 12

Year of Publication : 2025

Authors : Manoj B. Maurya, Nitin K. Dhote, Swapna Choudhary, Kirti Vaidya

10.14445/23488379/IJEEE-V12I12P115

How to Cite?

Manoj B. Maurya, Nitin K. Dhote, Swapna Choudhary, Kirti Vaidya, "Adaptive and Interpretable MPPT Framework for Photovoltaic Systems under Partial Shading using Integrated AI Modules," SSRG International Journal of Electrical and Electronics Engineering, vol. 12,  no. 12, pp. 186-198, 2025. Crossref, https://doi.org/10.14445/23488379/IJEEE-V12I12P115

Abstract:

Long-term widespread research has been undertaken on the need for strong and adaptable Maximum Power Point Tracking (MPPT) strategies for Photovoltaic (PV) systems owing to the far-reaching impact of PSCs, which seriously affects energy harvesting efficiency. Therefore, over and above substantial delays in the classic MPPT algorithms--that is, Perturb & Observe or Incremental Conductance–are largely considered classical MPPT methods, and their effectiveness becomes further limited owing to false convergence, slow adaptation, and limited generalization under dynamically changing shading patterns, resulting in sometimes low performance of such algorithms when enforced in the real-world environment. This paper proposes an integrated AI-powered MPPT framework created to tackle real-time power optimization issues owing to PSCs. The system comprises five tightly coupled modules: Contextual Hierarchical Transfer Graph Embedding (CHTGE) is implemented for transfer learning for a variety of environmental conditions by policy graphs on the premise of shading history combined with weather context. The role of Spatio-Temporal Feature Attention-based Indexing (STFAI) is to facilitate the detection of transient phenomena through the utilization of attention maps that are temporally aligned and derived from real-time multimodal sensor data. In the tertiary module, Differential Contextual Residual Optimization (DCRO) rectifies inaccuracies and achieves rapid stabilization through the application of residual corrections in a highly fluctuating environment. The output obtained using conventional Maximum Power Point Tracking (MPPT) methodologies is upgraded with multi-agent decision fusion with quantum-inspired adaptive logic (MADF-QAL). The Evolution-based Causal Disentanglement Networks (ECDN) provide fault localization and explainability through latent representation. There is an estimated improvement in the system performance by 28% in sensing the partial shading patterns, and a 33% reduction in the number of false triggers. Also, approximately 2.3 times quicker recovery from deviation in power, and 41% improvement in the decision-making process, and hence faster fault analysis. The proposed work suggests a framework of interpretable, resilient, and intelligent MPPT control under real-world operating scenarios.

Keywords:

Photovoltaic systems, Partial shading, Maximum Power Point Tracking, Artificial Intelligence, Causal inference.

References:

[1] K. Krishnaram et al., “A Novel Hybrid MPPT Technique for a PV System Operated under Partial Shading Conditions with Three Phase Interleaved Boost Converter,” Iranian Journal of Science and Technology, Transactions of Electrical Engineering, vol. 49, no. 3, pp. 1447-1465, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Saqib Asgar Kanth, Baziga Youssuf, and Sheikh Javed Iqbal, “Sovereign Butterfly Optimization and Flyback Converter Integration for Robust Photovoltaic Systems under Partial Shading,” Journal of The Institution of Engineers (India): Series B, vol. 106, no. 6, pp. 1973-1991, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Mary Beula Aron, and Josephine Rathinadurai Louis, “A Novel Intelligent Optimization-based Maximum Power Point Tracking Control of Photovoltaic System under Partial Shading Conditions,” Analog Integrated Circuits and Signal Processing, vol. 118, no. 3, pp. 489-503, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Muhammad Abu Bakar Siddique et al., “Performance Validation of Global MPPT for Efficient Power Extraction Through PV System under Complex Partial Shading Effects,” Scientific Reports, vol. 15, no. 1, pp. 1-24, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Man-liang Wang et al., “Improved White Shark Optimizer based Maximum Power Point Tracking Algorithm for Photovoltaic Systems under Partial Shading Conditions,” Journal of Electrical Engineering and Technology, vol. 20, no. 3, pp. 1293-1306, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Lahcen El Iysaouy et al., “Performance Enhancements and Modelling of Photovoltaic Panel Configurations during Partial Shading Conditions,” Energy Systems, vol. 16, no. 3, pp. 1143-1164, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Mohamed Zaki et al., “Hybrid Global Search with Enhanced INC MPPT under Partial Shading Condition,” Scientific Reports, vol. 13, no. 1, pp. 1-17, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Feriel Abdelmalek et al., “Experimental Validation of Effective Zebra Optimization Algorithm-based MPPT under Partial Shading Conditions in Photovoltaic Systems,” Scientific Reports, vol. 14, no. 1, pp. 1-21, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Chakarajamula Hussaian Basha et al., “A Novel on Design and Implementation of Hybrid MPPT Controllers for Solar PV Systems under Various Partial Shading Conditions,” Scientific Reports, vol. 14, no. 1, pp. 1-21, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Dokala Janandra Krishna Kishore et al., “A New Metaheuristic-based MPPT Controller for Photovoltaic Systems under Partial Shading Conditions and Complex Partial Shading Conditions,” Neural Computing and Applications, vol. 36, no. 12, pp. 6613-6627, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Sunkara Sunil Kumar, and K. Balakrishna, “A Novel Design and Analysis of Hybrid Fuzzy Logic MPPT Controller for Solar PV System under Partial Shading Conditions,” Scientific Reports, vol. 14, no. 1, pp. 1-17, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Layachi Zaghba et al., “Improving Photovoltaic Energy Harvesting Systems with Hybrid Fuzzy Logic-PI MPPT Optimized by PSO under Normal and Partial Shading Conditions,” Electrical Engineering, vol. 107, no. 4, pp. 4897-4919, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Muhannad J. Alshareef, “A Novel War Strategy Optimization Algorithm based Maximum Power Point Tracking Method for PV Systems under Partial Shading Conditions,” Scientific Reports, vol. 15, no. 1, pp. 1-27, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Hameed Ali Mohammed, Rosmiwati Mohd-Mokhtar, and Hazem Ibrahim Ali, “An Optimal Adaptive Neuro-Fuzzy Inference System for Photovoltaic Power System Optimization under Partial Shading Conditions,” Energy Systems, pp. 1-24, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[15] S. Antony Raj et al., “CGFSSO: The Co-Operative Guidance Factor based Salp Swarm Optimization Algorithm for MPPT under Partial Shading Conditions in Photovoltaic Systems,” International Journal of Information Technology, pp. 1-16, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Md Ehtesham Jahid, Sheeraz Kirmani, and Manaullah, “Application of Flower Pollination Algorithm and its Comparative Analysis for MPPT of Solar Panels under Partial Shading Conditions,” Journal of The Institution of Engineers (India): Series B, vol. 106, no. 6, pp. 1829-1842, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Abdel-Raheem Youssef, Mostafa M. Hefny, and Ahmed Ismail M. Ali, “Investigation of Single and Multiple MPPT Structures of Solar PV-System under Partial Shading Conditions Considering Direct Duty-Cycle Controller,” Scientific Reports, vol. 13, no. 1, pp. 1-21, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Radhia Garraoui et al., “A Novel ANN-DISM MPPT Controller for Solar Applications under Partial Shading with Two-Phase Interleaved Boost Converter,” Arabian Journal for Science and Engineering, vol. 50, no. 21, pp. 17637-17651, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Njimboh Henry Alombah et al., “Multiple-to-Single Maximum Power Point Tracking for Empowering Conventional MPPT Algorithms under Partial Shading Conditions,” Scientific Reports, vol. 15, no. 1, pp. 1-28, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[20] T. Nagadurga et al., “Global MPPT Optimization for Partially Shaded Photovoltaic Systems,” Scientific Reports, vol. 15, no. 1, pp. 1-30, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Hamid Ouatman et al., “Enhancing PV System Grid Stability through Reliable Flexible Power Point Tracking under Partial Shading,” Electrical Engineering, vol. 107, no. 4, pp. 4637-4649, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Ankit Kumar Soni et al., “Design and Analysis of an Adaptive Global Maximum Power Point Tracking Algorithm for Enhanced Partial Shading Detection and GMPP Tracking,” Arabian Journal for Science and Engineering, vol. 50, no. 21, pp. 17519-17536, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Muhammad Abu Bakar Siddique et al., “An Adapted Model Predictive Control MPPT for Validation of Optimum GMPP Tracking under Partial Shading Conditions,” Scientific Reports, vol. 14, no. 1, pp. 1-30, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Shahriar Farajdadian, and Seyed Mohammad Hassan Hosseini, “DMPPT Control of Photovoltaic Systems under Partial Shading Conditions based on Optimized Neural Networks,” Soft Computing, vol. 28, no. 6, pp. 4987-5014, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[25] K. Padmanaban, A. Shunmugalatha, and M.S. Kamalesh, “Experimental Investigation of Efficiency Enhancement in Solar Photovoltaic Systems under Partial Shading Conditions using Discrete Time Slime Mould Optimization,” Journal of Electrical Engineering and Technology, vol. 19, no. 4, pp. 2387-2400, 2024.
[CrossRef] [Google Scholar] [Publisher Link]