Title :
New Rules Generation From Measurement Data Using an Expert System in a Power Station
Author :
Sai, T.K. ; Reddy, K. Ashoka
Author_Institution :
C&I, NTPC, Hyderabad, India
Abstract :
The integration of artificial-intelligence techniques in traditional real-time systems is a promising approach to cope with the growing complexity of real-world applications. Real-time expert systems are online knowledge-based systems that combine analytical process models with conventional process control to monitor complex industrial processes and to assist in problem identification. The expert system interfaces with the external distributed control system (DCS) via an object linking and embedding for process control module which acquires measurement data and identifies processes alarms for diagnosis. This paper proposes generating new rules from the plant measurement data using a learning engine. We present an efficient algorithm that generates all significant rules based on the data. The association-based algorithms were compared and those best suited for this process application were selected. The application for the learning system is studied in a powerplant application This innovative approach should assist in sustainable growth of automation in the power sector.
Keywords :
control engineering computing; data acquisition; distributed control; expert systems; learning (artificial intelligence); power engineering computing; power station control; power system measurement; process control; sustainable development; DCS; analytical process model; artificial-intelligence technique; association-based algorithm; external distributed control system; industrial processing; learning engine; object linking; online knowledge-based system; plant measurement data acquisition; power plant application; power sector automation; power station; problem identification; process control; real-time expert system; sustainable growth; Association rules; Databases; Engines; Expert systems; Real-time systems; Algorithms; distributed control system; expert system; learning engine; power station;
Journal_Title :
Power Delivery, IEEE Transactions on
DOI :
10.1109/TPWRD.2014.2355595