Scholarlink

<p>The demand for photovoltaic (PV) systems is rapidly increasing. However, environmental conditions can lead to power losses. In this study, a hybrid and concurrent structure of artificial intelligence-based MPPT algorithms is proposed to track the global maximum power point under partial shading. The proposed method utilizes the dragonfly algorithm (DA), cuckoo search (CS) algorithm, and particle swarm optimization technique (PSO). These algorithms are applied to a PV system, and their individual performances and hybrid combinations are compared. From the obtained results, it is observed that the hybrid structure provides higher MPPT efficiency and faster convergence compared to individual algorithms. In particular, the DA&amp;CS combination achieved the best MPPT efficiency with 99.23%, 99.04%, and 99.07% compared to other algorithms under both full irradiation and partial shading conditions. Thus, the proposed method offers an effective solution for more efficient global maximum power point (GMPP) tracking with fast convergence under partial shading by eliminating the disadvantages of algorithms such as exploration and steady-state oscillations.</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

Hybrid artificial intelligence-based MPPT algorithm for partially shaded PV systems

  • 期刊论文

  • 发表日期：

    2026年2月17日

查看全文
  • Elif Baldan,
  • Hüseyin Erişti

摘要

The demand for photovoltaic (PV) systems is rapidly increasing. However, environmental conditions can lead to power losses. In this study, a hybrid and concurrent structure of artificial intelligence-based MPPT algorithms is proposed to track the global maximum power point under partial shading. The proposed method utilizes the dragonfly algorithm (DA), cuckoo search (CS) algorithm, and particle swarm optimization technique (PSO). These algorithms are applied to a PV system, and their individual performances and hybrid combinations are compared. From the obtained results, it is observed that the hybrid structure provides higher MPPT efficiency and faster convergence compared to individual algorithms. In particular, the DA&CS combination achieved the best MPPT efficiency with 99.23%, 99.04%, and 99.07% compared to other algorithms under both full irradiation and partial shading conditions. Thus, the proposed method offers an effective solution for more efficient global maximum power point (GMPP) tracking with fast convergence under partial shading by eliminating the disadvantages of algorithms such as exploration and steady-state oscillations.