Application of Data Mining Techniques for Sensor Drift Analysis to Optimize Nuclear Power Plant performance
S.Narasimhan1, Rajendran2

1S.Narasimhan, IT & Instrumentation, Bharatiya nabhikiya Vidyut Nigam Limited, BHAVINI, Kalpakkam, India.
2Dr.Rajendran, Department of ECE , VELS Institute of Science, Technology and Advanced Studies(VISTAS) Chennai, India.

Manuscript received on October 15, 2019. | Revised Manuscript received on 25 October, 2019. | Manuscript published on November 10, 2019. | PP: 3087-3095 | Volume-9 Issue-1, November 2019. | Retrieval Number: A9139119119/2019©BEIESP | DOI: 10.35940/ijitee.A9139.119119
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: The Power Plants are engineered and instrumented to ensure safety in all modes of operation. Hence they should be continuously monitored and maintained with necessary Instrumentation to identify performance degradation and the root causes to avoid calling for frequent maintenance. The degraded performance of Instrumentation & Control systems may also lead to plant outages. Different studies have suggested that a well maintained instrumentation with errors and response times within the permissible limits may increase the availability minimizing outages. The I&C systems are designed for monitoring, control and safety actions in case of an event in a power plant. The sensors used are single, redundant, triplicated or diverse based on the type of application. Where safety is of prime concern, triplicated and 2/3 voting logic is employed for initiating safety actions. Diverse instruments are provided for protecting the plant from any single abnormal event. Redundant sensors are used to improve plant availability. Wherever 2/3 logics are used, the sensors shall uniformly behave and the drifts across the sensor may lead to crossing the threshold, initiating a protective action. Instead of waiting for the regular preventive maintenance schedule for recalibrating the sensors, the drift in the sensors are analyzed by developing a combined overall online monitoring parameter which will give an early warning to the operator the need for recalibration of the redundant sensors. This paper deals with development of one such parameter through data mining techniques for a representative process in a nuclear power plant.
Keywords: Calibration/Drift Measurement, Data-driven models, Data Collection, Data Mining, Data Pre-processing,
Scope of the Article: Data Mining