A Novel Method to Detect Partial Shadow Effects in Perovskite-Based Simulated Solar Cell System Faults

Area of Science:

• Electrical Engineering
• Renewable Energy Systems
• Materials Science

Background:

• Photovoltaic (PV) systems require expert intervention for fault diagnosis, often necessitating plant shutdowns.
• PV systems are expensive with low efficiency (around 20%), making shutdowns economically detrimental.
• Remote locations of solar farms increase costs associated with expert deployment and maintenance.

Purpose of the Study:

• To develop a non-disruptive method for detecting partial shading faults in solar cells.
• To enhance the operational safety and economic efficiency of photovoltaic power plants.
• To provide a cost-effective solution for fault detection in remote solar installations.

Main Methods:

• Implementation of a fuzzy detection algorithm for fault identification.
• Simulation-based analysis to validate the proposed detection method.
• Focus on detecting partial shading, a common cause of PV system underperformance.

Main Results:

• The fuzzy detection method effectively identifies partial shading faults in solar cells.
• Simulation results confirm the high efficiency of the proposed fault detection technique.
• The method allows for fault diagnosis without requiring a complete power plant shutdown.

Conclusions:

• The fuzzy detection method offers a viable solution for real-time fault diagnosis in PV systems.
• This approach mitigates economic losses and safety risks associated with traditional fault management.
• The study highlights the potential for improved operational continuity and profitability in solar energy generation.