Novel MPPT algorithm based on honey bees foraging characteristics for solar power generation systems.

Area of Science:

• Renewable Energy Systems
• Biomimetic Algorithms
• Photovoltaic Power Optimization

Background:

• Maximum Power Point Tracking (MPPT) is crucial for photovoltaic (PV) energy harvesting.
• Existing MPPT algorithms face challenges under varying irradiance conditions, particularly partial shading.
• There is a need for efficient, adaptive MPPT strategies inspired by natural phenomena.

Purpose of the Study:

• To introduce a novel Honey Bee Dancing (HBD) algorithm for MPPT in solar energy systems.
• To evaluate the HBD algorithm's performance under uniform and partial shaded conditions.
• To compare the HBD algorithm against established MPPT methods like OPLR and P&O.

Main Methods:

• Developed an HBD algorithm simulating bee foraging and communication for locating the maximum power point.
• Implemented and tested the HBD algorithm alongside OPLR and P&O algorithms.
• Conducted experimental verification under uniform irradiance conditions (UIC) and partial shaded conditions (PSC).

Main Results:

• The HBD algorithm demonstrated superior performance in both UIC and PSC compared to OPLR and P&O.
• Under PSC, HBD achieved higher output power (218W vs. 189.2W/74.8W) and efficiency (99% vs. 86%/34%) with faster convergence (4.7ms).
• The HBD algorithm eliminates the need for PV panel specifications, complex calculations, and additional sensors.

Conclusions:

• The proposed HBD algorithm offers a highly efficient and economically beneficial solution for solar MPPT.
• This bio-inspired approach effectively optimizes solar power generation by mimicking natural foraging behaviors.
• The HBD algorithm presents a robust and simplified alternative for PV energy harvesting systems.