Enhancing Agent Intelligence through Data Mining: A Power Plant Case Study

In this paper, the methodology for an intelligent assistant for power plants is presented. Multiagent systems technology and data mining techniques are combined to enhance the intelligence of the proposed application, mainly in two aspects: increase the reliability of input data (sensor validation and false measurement replacement) and generate new control monitoring rules. Various classification algorithms are compared. The performance of the application, as tested via simulation experiments, is discussed.

[1]  V. Chatziathanasiou,et al.  An Agent Based Prototype for Optimizing Power Plant Operation , 2009 .

[2]  Shijia Gao,et al.  Conceptual modeling and development of an intelligent agent-assisted decision support system for anti-money laundering , 2009, Expert Syst. Appl..

[3]  Belle R. Upadhyaya,et al.  Sensor validation for power plants using adaptive backpropagation neural network , 1990 .

[4]  R. Vahidov.  Intermediating user-DSS interaction with autonomous agents , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[5]  Pericles A. Mitkas,et al.  Agent intelligence through data mining , 2006, Multiagent systems, artificial societies, and simulated organizations.

[6]  Ian Witten,et al.  Data Mining , 2000 .

[7]  Guus Schreiber,et al.  Knowledge Engineering and Management: The CommonKADS Methodology , 1999 .

[8]  Steven A. Benson,et al.  A comprehensive slagging and fouling prediction tool for coal-fired boilers and its validation/application , 2007 .

[9]  Miguel A. Sanz-Bobi,et al.  DADICC: Intelligent system for anomaly detection in a combined cycle gas turbine plant , 2008, Expert Syst. Appl..

[10]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[11]  Paul M. Frank,et al.  Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: A survey and some new results , 1990, Autom..

[12]  Eleni E. Mangina.  Application of Intelligent Agents in Power Industry: Promises and Complex Issues , 2003, CEEMAS.

[13]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[14]  John G. Cleary,et al.  K*: An Instance-based Learner Using and Entropic Distance Measure , 1995, ICML.

[15]  Christer Carlsson,et al.  Past, present, and future of decision support technology , 2002, Decis. Support Syst..

[16]  Sunil Vadera,et al.  A Probabilistic Model for Information and Sensor Validation , 2006, Comput. J..

[17]  C. Lucas,et al.  Development of a multi-agent information management system for Iran power industry. A case study , 2001, 2001 IEEE Porto Power Tech Proceedings (Cat. No.01EX502).

[18]  Michal Pechoucek,et al.  Industrial deployment of multi-agent technologies: review and selected case studies , 2008, Autonomous Agents and Multi-Agent Systems.

[19]  James L. Crowley,et al.  Agent based middleware infrastructure for autonomous context-aware ubiquitous computing services , 2007, Comput. Commun..

[20]  Vasilis Chatziathanasiou,et al.  Intelligent system for identification and replacement of faulty sensor measurements in Thermal Power Plants (IPPAMAS: Part 1) , 2009, Expert Syst. Appl..

[21]  David W. Aha,et al.  Instance-Based Learning Algorithms , 1991, Machine Learning.

[22]  Nikolaos M. Avouris,et al.  The Role of Domain Knowledge in a Large Scale Data Mining Project , 2002, SETN.

[23]  Geoff Holmes,et al.  Generating Rule Sets from Model Trees , 1999, Australian Joint Conference on Artificial Intelligence.