State variable technique islanding detection using time-frequency energy analysis for DFIG wind turbine in microgrid...

Area of Science:

• Electrical Engineering
• Power Systems
• Renewable Energy Integration

Background:

• Islanding events in microgrids pose significant risks to grid stability and equipment.
• Accurate and rapid detection of islanding is essential for safe microgrid operation, especially with diverse distributed generation sources.

Purpose of the Study:

• To introduce a new combined technique for wind turbine islanding detection.
• To enhance the performance and reliability of islanding detection in microgrids.

Main Methods:

• Utilizing the trajectory of state variables and wavelet transform for signal analysis.
• Employing a signal selection method based on correlation between islanding and non-islanding signals.
• Using a neuro-fuzzy machine for pattern learning to eliminate threshold selection.

Main Results:

• The proposed technique effectively detects wind turbine islanding in a microgrid with various distributed generation types.
• Simulations covered diverse islanding and non-islanding scenarios, including motor starting and load switching.
• Key characteristics include passive-based operation, negligible None Detection Zone (NDZ), and fast detection times.

Conclusions:

• The developed technique demonstrates high performance and reliability for wind turbine islanding detection in microgrids.
• The combined approach offers a robust solution for safeguarding microgrid stability.
• The method is suitable for microgrid applications requiring fast and accurate islanding detection.