ArticleScientific reports2024
Experimental validation of effective zebra optimization algorithm-based MPPT under partial shading conditions in photovoltaic systems.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- A novel reflective intelligence optimizer with machine learning (RIO-ML) for parameter estimation of photovoltaic models.Scientific reports · 2026Article
- A reinforcement learning-enhanced discrete zebra optimization algorithm for solving the traveling salesman problem.Scientific reports · 2026Article
- An improved grey wolf optimization-based MPPT algorithm for photovoltaic systems under partial shading conditions.Scientific reports · 2026Article
- A smart single stage solar battery driven desalination system with hybrid MPPT and optimal control for brackish water treatment.Scientific reports · 2025Article
- An Improved Elk Herd Optimization Algorithm for Maximum Power Point Tracking in Photovoltaic Systems Under Partial Shading Conditions.Biomimetics (Basel, Switzerland) · 2025Article
- Improved Zebra Optimization Algorithm with Multi Strategy Fusion and Its Application in Robot Path Planning.Biomimetics (Basel, Switzerland) · 2025Article
- A high-speed MPPT based horse herd optimization algorithm with dynamic linear active disturbance rejection control for PV battery charging system.Scientific reports · 2025Article
- A novel adaptive FOCV algorithm with robust IMRAC control for sustainable and high-efficiency MPPT in standalone PV systems: experimental validation and performance assessment.Scientific reports · 2024Article
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8 authors.
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Abstract
This study introduces a novel approach for analyzing photovoltaic (PV) systems that employ block lookup tables for speedy and efficient simulation. It introduces an innovative method for tracking the Global Maximum Power Point (GMPP) by utilizing Zebra Optimization Algorithm (ZOA). The suggested method was carefully evaluated under difficult Partial Shading Conditions (PSCs) and Dynamic Shading Conditions (DSCs) to determine its global and local search capability. ZOA's performance was examined in four scenarios and compared to four existing MPPT algorithms: Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), Flower Pollination Algorithm (FPA), and Whale Optimization Algorithm (WOA). ZOA surpassed its competitors with an average tracking time of 0.875 s and a tracking efficiency of 99.95% in PSCs. In comparison, ZOA increased tracking efficiency by up to 2%, increased resilience under varied circumstances, and produced a faster convergence speed-approaching the maximum Power Point 10-15% faster than the other algorithms. Furthermore, ZOA significantly decreased operating point variations. The algorithm's overall performance was tested using an experimental setup with a DSPACE board and a PV emulator. These findings demonstrate that ZOA is a highly efficient and dependable MPPT solution for PV systems, especially in severe PSCs.
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