Work

A deterministic double-exponential maximum power point tracking algorithm for PV string complex partial shading condition

Year: 2025

Type: article

Abstract: In photovoltaic (PV) systems, the inherent non-linear relationship between duty cycle and PV voltage poses a major challenge for effective Maximum Power Point Tracking (MPPT) remains underexplored in existing literature, leading to suboptimal tracking algorithms. This paper introduces the Double-Exponential (DEx) MPPT algorithm to mitigate this non-linearity. The proposed DEx MPPT algorithm reduces tracking points by 77 %, lowering GMPP tracking time while maintaining comprehensive coverage of the entire tracking region. For a 20-panels PV string with 906.2 V open circuit voltage, the DEx strategically allocates tracking points along complex P-V curves under partial shading conditions (PSCs). Extensive simulations show DEx outperforms deterministic and metaheuristic MPPT methods, achieving 0.138 s tracking time, 99.91 % tracking accuracy, and 98 % success rate. Moreover, DEx demonstrates effectiveness under fluctuating irradiance specified in the EN50530 dynamic test. Real-time tracking performance is further validated using a Typhoon HIL 404 hardware-in-the-loop system and TI-F28379D real-time microcontroller.

Source: Computers & Electrical Engineering

DOI: 10.1016/j.compeleceng.2025.110735

Authors: Jia Shun Koh, Rodney H.G. Tan, Nadia M.L. Tan, Wei Hong Lim

Institutions: UCSI University, Christian-Albrechts-Universität zu Kiel

Language: English

Topic: Photovoltaic System Optimization Techniques

Subfield: Renewable Energy, Sustainability and the Environment

Field: Energy

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $2,880