Scholarlink

<p>Attributable to the impact of partial shading conditions (PSC), the output characteristic curve of photovoltaic (PV) arrays is susceptible to multiple peaks, under which circumstances conventional maximum power point tracking (MPPT) techniques may prove to be ineffective. This article proposes a novel two-stage MPPT method, termed the improved Harris Hawk optimization (IHHO) and sigmoid function-based Perturb and Observe (SP&amp;O). The standard Harris Hawk Optimization (HHO) algorithm is improved by introducing a nonlinear dynamic energy factor based on the distance between the optimal individual and the current individual vector, which improves the balance between global optimization and local optimization. The dynamically changing population balances optimization accuracy and speed. Using the Sigmoid function to optimize the dynamic step size of P&amp;O, making the algorithm faster and more accurate, and eliminating steady-state oscillations. A comparative analysis is performed against VPSO-LF, Spline-MPPT, HHO, IGWO, and P&amp;O methods under PSC. The results demonstrate that IHHO-SP&amp;O achieves minimal power loss, rapid tracking speed, and no steady-state oscillation.</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

A new two-stage MPPT technique for enhancing the performance of PV system

  • 期刊论文

  • 发表日期：

    2025年4月25日

查看全文
  • Qingle Pang,
  • Feng Zhang,
  • Songyi Han,
  • Tai Zhou,
  • Yangjie Wang

摘要

Attributable to the impact of partial shading conditions (PSC), the output characteristic curve of photovoltaic (PV) arrays is susceptible to multiple peaks, under which circumstances conventional maximum power point tracking (MPPT) techniques may prove to be ineffective. This article proposes a novel two-stage MPPT method, termed the improved Harris Hawk optimization (IHHO) and sigmoid function-based Perturb and Observe (SP&O). The standard Harris Hawk Optimization (HHO) algorithm is improved by introducing a nonlinear dynamic energy factor based on the distance between the optimal individual and the current individual vector, which improves the balance between global optimization and local optimization. The dynamically changing population balances optimization accuracy and speed. Using the Sigmoid function to optimize the dynamic step size of P&O, making the algorithm faster and more accurate, and eliminating steady-state oscillations. A comparative analysis is performed against VPSO-LF, Spline-MPPT, HHO, IGWO, and P&O methods under PSC. The results demonstrate that IHHO-SP&O achieves minimal power loss, rapid tracking speed, and no steady-state oscillation.