Distributed Architecture Technologies
IT Professional
Using Archon, Part 2: Electricity Transportation Management
IEEE Expert: Intelligent Systems and Their Applications
A Distributed Approach for Coordination of Traffic Signal Agents
Autonomous Agents and Multi-Agent Systems
Machine learning for frequency estimation of power systems
Applied Soft Computing
Diagnosis using a first-order stochastic language that learns
Expert Systems with Applications: An International Journal
Artificial intelligence for monitoring and supervisory control of process systems
Engineering Applications of Artificial Intelligence
Fault diagnosis using dynamic trend analysis: A review and recent developments
Engineering Applications of Artificial Intelligence
Combined use of supervised and unsupervised learning for power system dynamic security mapping
Engineering Applications of Artificial Intelligence
Distributed data mining and agents
Engineering Applications of Artificial Intelligence
Fault detection and fuzzy rule extraction in AC motors by a neuro-fuzzy ART-based system
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
Alarm clustering for intrusion detection systems in computer networks
Engineering Applications of Artificial Intelligence
Inference in distributed data clustering
Engineering Applications of Artificial Intelligence
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Power industry around the world is facing several changes since deregulation with constant pressure put on improving security, reliability and quality of the power supply. Computational fault analysis and diagnosis of power networks have been active research topics with several theories and algorithms proposed. This paper proposes a distributed diagnostic algorithm for fault analysis in power networks. Distributed architecture for power network fault analysis (DAPFA) is an intelligent, model-based diagnostic algorithm that incorporates a hierarchical power network representation and model. The architecture is based on the industry's substation automation implementation standards. The structural and functional model is a multi-level representation with each level depicting a more complex grouping of components than its predecessor in the hierarchy. The distributed functional representation contains the behavioral knowledge related to the components of that level in the structural model. The diagnostic algorithm of DAPFA is designed to perform fault analysis in pre-diagnostic and diagnostic levels. Pre-diagnostic phase provides real-time analysis while the diagnostic phase provides the final diagnostic analysis. The diagnostic algorithm incorporates knowledge-based and model-based reasoning mechanisms with one of the model levels represented as a network of neural nets. The relevant algorithms and techniques are discussed. The resulting system has been implemented on a New Zealand sub-system and the results are analyzed.