Multi-Objective Power Supply Restoration in Distribution Networks Based on Graph Calculation and Information Collected by Multi-Source Sensors
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View abstract on PubMed
Summary
This summary is machine-generated.This study enhances fault recovery in complex power grids using intelligent sensors and graph models. The new strategy boosts the overall recovery rate by over 20% through dynamic adjustments.
Area Of Science
- Electrical Engineering
- Power Systems Analysis
- Network Optimization
Background
- Increasing complexity of distribution networks necessitates improved fault restoration efficiency and reliability.
- Traditional sensors (CT, PT) and intelligent sensors (D-PMU) provide multi-source data for fault analysis.
- Graph calculation models offer a framework for studying complex network problems.
Purpose Of The Study
- To address the fault recovery problem in multi-objective distribution networks.
- To develop a dynamic and adaptive fault recovery strategy.
- To enhance the efficiency and reliability of power restoration processes.
Main Methods
- Proposed a power flow calculation model and operation constraints adaptable to topology changes within a graph calculation framework.
- Utilized minimum spanning tree theory to define blackout ranges and recovery path sets.
- Configured intelligent sensors (D-PMU) for comprehensive fault information collection, ensuring coverage of connected load vertices.
- Established a topological evolution model to account for repeated transfers in outage areas and explore recovery strategies.
- Validated the model using a distribution network in Shaanxi Province.
Main Results
- The proposed strategy dynamically adjusts recovery through methods including single and double transfers, repeat transfers in outage areas, and load shedding.
- Experimental results demonstrated a significant improvement in the overall fault recovery rate, exceeding 20%.
- The model effectively handles topology changes and complex outage scenarios.
Conclusions
- The developed fault recovery strategy enhances the efficiency and reliability of power distribution networks.
- The integration of intelligent sensors and graph theory provides a robust framework for dynamic fault restoration.
- The proposed methods offer a practical solution for improving power grid resilience in the face of increasing complexity.
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