Authors:
Michael Negnevitsky
1
;
Nikita Tomin
2
;
Daniil Panasetsky
2
;
Ulf Haeger
3
;
Nikolay Voropai
2
;
Christian Rehtanz
3
and
Victor Kurbatsky
2
Affiliations:
1
University of Tasmania, Australia
;
2
Melentiev Energy Systems Institute, Russian Federation
;
3
TU Dortmund, Germany
Keyword(s):
Blackout, Pre-emergency Control, Voltage Stability, Multi-agent Control System, Artificial Neural Networks.
Related
Ontology
Subjects/Areas/Topics:
Agent Models and Architectures
;
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Cooperation and Coordination
;
Data Manipulation
;
Distributed and Mobile Software Systems
;
Enterprise Information Systems
;
Evolutionary Computing
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Hybrid Intelligent Systems
;
Knowledge Discovery and Information Retrieval
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Machine Learning
;
Methodologies and Methods
;
Multi-Agent Systems
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Software Engineering
;
Symbolic Systems
;
Theory and Methods
Abstract:
A neural multi-agent-based approach for system monitoring and preventing large-scale emergencies in
power systems is presented in this paper. The automatic emergency control process is represented as a
neural multi-agent system with hierarchical architecture. The proposed system consist of two main parts: the
alarm trigger, a Kohonen neural network-based system for early detection of possible alarm states in a
power system, and the competitive–collaborative multi-agent control system. For demonstration purposes,
we investigated conventional and neural multi-agent automatic control schemes. Results are presented and
discussed.