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Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

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Effect of correlations on controllability transition in network control.

Sen Nie¹, Xu-Wen Wang¹, Bing-Hong Wang^1,2,3

¹Department of Modern Physics, University of Science and Technology of China, Hefei, Anhui 230026, P. R. China.

Scientific Reports

|April 12, 2016

Summary

Network controllability depends on degree correlation, especially in sparse networks. This finding is crucial for managing complex systems like power grids and ecological networks.

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Area of Science:

Complex systems analysis
Network science
Control theory

Background:

Network control is vital for preventing cascading failures in power grids and managing ecological systems.
Numerical control is achievable when control inputs surpass a specific transition point.

Purpose of the Study:

To investigate how degree correlation affects numerical controllability in networks.
To analyze this effect across various network structures, including real and modeled systems.

Main Methods:

Reconstruction of network topological structures from real and modeled systems.
Analysis of numerical controllability in undirected and directed networks.
Examination of degree correlation's influence on the control input transition point.

Main Results:

Degree correlation significantly impacts the control input transition point in moderately sparse undirected and directed networks.
This effect is not observed in dense networks for numerical controllability, unlike structural controllability.
In directed random and scale-free networks, correlation type dictates the influence of degree correlation.

Conclusions:

Degree correlation is a key factor influencing numerical controllability in sparse complex networks.
Understanding these correlations is essential for effective network control strategies.
The findings offer insights into controlling complex sparse networks, with implications for power grids and ecological systems.