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Intelligent decision support algorithm for distribution system restoration.

Reetu Singh¹, Shabana Mehfuz², Parmod Kumar³

¹Jamia Millia Islamia, New Delhi, Delhi India.

|August 12, 2016

Summary

This summary is machine-generated.

This study introduces a hybrid fuzzy-grey relation algorithm for rapid electric distribution system restoration. The intelligent method effectively handles uncertainties and ranks restoration plans for improved grid reliability.

Keywords:

Distribution system Fuzzy logic Grey system theory Power system restoration Transformer loading

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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.