Sensor Value Validation Based on Implicit Sensor Redundancy for Reliable Operation of Power Plants

Abstract

This paper presents an innovative sensor value validation technique that can systematically explore the embedded sensor redundancies in power plants and utilize them in validating sensor values. The proposed technique uses the relationships existing among the sensor values, which is called causal relations (CRs) in this paper. A data structure called a sensor redundancy graph (SRG) is formulated to explore systematically the sensor redundancies existing throughout the plants. In the SRG, CRs are represented as nodes and two CRs having a common sensor are connected with an edge. The criteria are also provided for testing whether a given sensor relationship expressed as a CR holds with the actual measurement values. If the relationship holds, the necessary condition for the validities of the sensors participated in the CR is satisfied. A technique that can examine the compatibility of the precisions among the sensors in a CR for testing the usability of the given CR is also developed based on sensor precision indices and statistical inference. The proposed technique is applied to flow meter data provided from an actual super critical steam power plant and its application potential is demonstrated.Index Terms-Casual relation, sensor precision, sensor redundancy, sensor redundancy graph, sensor value validation, validity level.

Enhanced PEMS Performance and Regulatory Compliance through Machine Learning

“…Building a PEMS always starts with identifying quantitative relationships among a number of sensors. sensor measurements and the constraint relations among them (Chow & Willsky, 1984;Lee & Park, 2005). Its principle is generally based on consistency checking between the observed behavior of the process provided by the sensors and the expected behavior given by a mathematical representation of the process.…”

Risk informed design modification of dynamic thermal rating system

“…It works by validating the sensor values through comparisons with its redundant measurement values. The voting system is used as it has proven to be effective in various fields of nuclear and power engineering where sensing activities are required [23–27]. This process produces the statuses (up or down) of the wind speed and wind angle sensing abilities.…”

Sensor fault detection by sparsity optimization

“…On the other hand, the threshold method validates sensors in a system by means of the nominal values, cf. [4]. However, many problems can arise from the choice of the threshold, such as false alarms caused by the system dynamic behavior.…”

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