Comparative Analysis of Dynamic and Steady State Performances of Hill Climbing and Incremental Conductance MPPT Controllers for PV Systems
Publish Year: 1403
نوع سند: مقاله ژورنالی
زبان: English
View: 58
This Paper With 8 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_JREE-11-3_006
تاریخ نمایه سازی: 31 تیر 1403
Abstract:
The integration of photovoltaic (PV) solar energy into the utility grid is expanding progressively to meet increasing energy demand. A crucial aspect of optimizing the output of PV systems involves implementing efficient maximum power point tracking (MPPT) controllers, necessary due to the nonlinear characteristics of these systems. This study conducts a simulation-based comparative analysis of two prominent MPPT techniques: hill climbing (HC) and incremental conductance (INC) methods. The emphasis is on the dynamic response and steady-state efficiency of these controllers. Using a modeled ۵۰۰ kW PV array alongside both MPPT techniques and other DC stage components, simulation tests were conducted in Matlab/Simulink under standard test conditions (STC) and varying meteorological conditions. The simulation results indicate that both techniques successfully tracked the MPP. However, the INC algorithm exhibits superior speed, precision, and efficiency, particularly in scenarios involving sudden fluctuations in irradiance and temperature. Furthermore, investigating the effect of perturbation step size on dynamic response and steady-state efficiency provided valuable insights for enhancing MPPT controller performance.
Keywords:
Authors
Noussaiba Mennai
Department of Electrical Engineering, LES Laboratory, University of ۲۰ August ۱۹۵۵, Skikda, Algeria.
Ammar Medoued
Department of Electrical Engineering, LES Laboratory, University of ۲۰ August ۱۹۵۵, Skikda, Algeria.
Youcef Soufi
Deportment of Electrical Engineering, LABGET Laboratory, University of Larbi Tebessi, Tebessa, Algeria.
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :