Scholarlink

<p>This paper introduces a novel adaptive global maximum power point tracking (AGMPPT) algorithm for precise detection of partial shading conditions (PSCs) and rapid tracking of the global maximum power point (GMPP). The P–V characteristics of photovoltaic (PV) systems under PSC exhibit multiple local maxima, rendering conventional MPPT methods ineffective. Intelligent techniques such as particle swarm optimization, Bat algorithm, and Cuckoo search can track the GMPP; however, their tracking accuracy and convergence speed heavily depend on particle count and initialization parameters. The proposed AGMPPT controller addresses these limitations by accurately identifying and distinguishing PSCs from uniform irradiance conditions. It then quickly and effectively tracks the GMPP among many local maxima. The AGMPPT technique also incorporates a dynamic step size incremental conductance method for fast-tracking of maximum points, resulting in reduced convergence time. The AGMPPT algorithm was simulated using the MATLAB/Simulink platform and experimentally validated on hardware with a d-SPACE DS1103 digital controller. Performance evaluation under various conditions demonstrates the AGMPPT’s superiority over existing MPPT techniques in precise PSC detection, faster GMPP tracking, improved transient response, and robustness to load variations.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Design and Analysis of an Adaptive Global Maximum Power Point Tracking Algorithm for Enhanced Partial Shading Detection and GMPP Tracking

  • 期刊论文

  • 发表日期：

    2025年2月4日

查看全文
  • Ankit Kumar Soni,
  • Kartick Chandra Jana,
  • Deepak Kumar Gupta,
  • Pradipta Kumar Pal,
  • Amit Kumar V. Jha

摘要

This paper introduces a novel adaptive global maximum power point tracking (AGMPPT) algorithm for precise detection of partial shading conditions (PSCs) and rapid tracking of the global maximum power point (GMPP). The P–V characteristics of photovoltaic (PV) systems under PSC exhibit multiple local maxima, rendering conventional MPPT methods ineffective. Intelligent techniques such as particle swarm optimization, Bat algorithm, and Cuckoo search can track the GMPP; however, their tracking accuracy and convergence speed heavily depend on particle count and initialization parameters. The proposed AGMPPT controller addresses these limitations by accurately identifying and distinguishing PSCs from uniform irradiance conditions. It then quickly and effectively tracks the GMPP among many local maxima. The AGMPPT technique also incorporates a dynamic step size incremental conductance method for fast-tracking of maximum points, resulting in reduced convergence time. The AGMPPT algorithm was simulated using the MATLAB/Simulink platform and experimentally validated on hardware with a d-SPACE DS1103 digital controller. Performance evaluation under various conditions demonstrates the AGMPPT’s superiority over existing MPPT techniques in precise PSC detection, faster GMPP tracking, improved transient response, and robustness to load variations.