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SENSOR SYSTEMS FOR AN ON-LINE DIAGNOSTIC EXPERT SYSTEM IN FOSSIL POWER PLANTS

Young-Jin Kim
Materials Science and Engineering Program, University of California, San Diego, La Jolla, CA 92093, USA

William H. Wood III
Berkeley Expert Systems Technology Laboratory, Department of Mechanical Engineering, University of California at Berkeley, Berkeley, California 94720, USA

Alice M. Agogino
Berkeley Expert Systems Technology Laboratory, Department of Mechanical Engineering, University of California at Berkeley, Berkeley, California 94720, USA

Sinopsis

This paper outlines a framework for performing sensor validation in HEATXPRTTM, a data-driven on-line expert system for diagnosing heat rate degradation problems in fossil power plants (1) (2). Sensor validation plays a vital role in the ability of the overall system to correctly determine the state of a plant monitored by imperfect sensors. Two subsystems: Algorithmic (ASV) and Heuristic (HSV) Sensor Validation, separate activity according to the degree of plant knowledge required. ASV provides an estimate of continuous and discrete probability distributions for each of over a thousand sensor inputs. ASV also produces a confidence measure for each sensor based on previous behavior. HSV exploits deeper knowledge about parameter interaction within the plant to cull sensor faults from the data stream. HEATXPRTTM, provided with an updated sensory input stream containing only actual process deviations, then determines the most likely sources of heat rate performance degradation and calls for necessary operator action

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