Intelligent decision support algorithm for distribution system restoration

Area of Science:

• Electrical Engineering
• Artificial Intelligence
• Power Systems

Background:

• Electric utility revenue relies on distribution systems, necessitating rapid fault restoration.
• Load uncertainties cause parameter variations in distribution networks, complicating restoration.
• Existing methods may not adequately address dynamic network conditions and uncertainties.

Purpose of the Study:

• To develop an intelligent algorithm for analyzing and restoring electric distribution systems.
• To incorporate uncertainty handling and sequence comparison for improved restoration strategies.
• To meet smart grid requirements for automated restoration during partial or full network blackouts.

Main Methods:

• A hybrid fuzzy-grey relation algorithm was developed.
• The algorithm accounts for uncertainties in distribution network parameters.
• Simulation studies were performed on a typical distribution system to rank restoration plans.

Main Results:

• The proposed algorithm effectively analyzes and ranks distribution system restoration plans.
• Simulation results demonstrate the utility of the hybrid fuzzy-grey relation approach.
• The method provides an automated restoration plan suitable for smart grid applications.

Conclusions:

• The hybrid fuzzy-grey relation algorithm offers an effective solution for electric distribution system restoration.
• The algorithm's ability to handle uncertainties enhances grid reliability and resilience.
• This approach aligns with smart grid objectives for automated network recovery.