scholarly journals Enhancing nuclear power plant performance through the use of artifical intelligence. First annual report

1989 ◽  
Author(s):  
M. Johnson ◽  
A. Maren ◽  
L. Miller ◽  
R. Uhrig ◽  
B. Upadhyaya
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Annual Report ◽  
Plant Performance ◽  
Artifical Intelligence

scholarly journals Enhancing nuclear power plant performance through the use of artifical intelligence

1989 ◽  
Author(s):  
M. Johnson ◽  
A. Maren ◽  
L. Miller ◽  
R. Uhrig ◽  
B. Upadhyaya
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Plant Performance ◽  
Artifical Intelligence

scholarly journals Data Driven Modelling of Nuclear Power Plant Performance Data as Finite State Machines

Modelling ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 43-62
Author(s):  
Kshirasagar Naik ◽  
Mahesh D. Pandey ◽  
Anannya Panda ◽  
Abdurhman Albasir ◽  
Kunal Taneja
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Finite State Machine ◽  
State Machine ◽  
Plant Performance ◽  
Series Data ◽  
Cyberphysical Systems ◽  
Finite State ◽  
Machine Representation

Accurate modelling and simulation of a nuclear power plant are important factors in the strategic planning and maintenance of the plant. Several nonlinearities and multivariable couplings are associated with real-world plants. Therefore, it is quite challenging to model such cyberphysical systems using conventional mathematical equations. A visual analytics approach which addresses these limitations and models both short term as well as long term behaviour of the system is introduced. Principal Component Analysis (PCA) followed by Linear Discriminant Analysis (LDA) is used to extract features from the data, k-means clustering is applied to label the data instances. Finite state machine representation formulated from the clustered data is then used to model the behaviour of cyberphysical systems using system states and state transitions. In this paper, the indicated methodology is deployed over time-series data collected from a nuclear power plant for nine years. It is observed that this approach of combining the machine learning principles with the finite state machine capabilities facilitates feature exploration, visual analysis, pattern discovery, and effective modelling of nuclear power plant data. In addition, finite state machine representation supports identification of normal and abnormal operation of the plant, thereby suggesting that the given approach captures the anomalous behaviour of the plant.

U.S. Nuclear Power Plant Performance Assessment Using the Versatile Economic Risk Tool (VERT)

2021 ◽  
Author(s):  
Jaden C. Miller ◽  
Spencer C. Ercanbrack ◽  
Chad L. Pope
Keyword(s):  
Risk Assessment ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Fault Tree ◽  
Plant Performance ◽  
Economic Risk ◽  
Reliability Data ◽  
Water Reactor ◽  
Tree Models

Abstract This paper addresses the use of a new nuclear power plant performance risk analysis tool. The new tool is called Versatile Economic Risk Tool (VERT). VERT couples Idaho National Laboratory’s SAPHIRE and RAVEN software packages. SAPHIRE is traditionally used for performing probabilistic risk assessment and RAVEN is a multi-purpose uncertainty quantification, regression analysis, probabilistic risk assessment, data analysis and model optimization software framework. Using fault tree models, degradation models, reliability data, and economic information, VERT can assess relative system performance risks as a function of time. Risk can be quantified in megawatt hours (MWh) which can be converted to dollars. To demonstrate the value of VERT, generic pressurized water reactor and boiling water reactor fault tree models were developed along with time dependent reliability data to investigate the plant systems, structures, and components that impacted performance from the year 1980 to 2020. The results confirm the overall notion that US nuclear power plant industry operational performance has been improving since 1980. More importantly, the results identify equipment that negatively or positively impact performance. Thus, using VERT, individual plant operators can target systems, structures, and components that merit greater attention from a performance perspective.

scholarly journals Neural network recognition of nuclear power plant transients. First annual report, April 15, 1992--April 15, 1993, Revision 1

1993 ◽  
Author(s):  
E.B. Bartlett ◽  
R. Danofsky ◽  
J. Adams ◽  
T. AlJundi ◽  
A. Basu ◽  
...  
Keyword(s):  
Neural Network ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Annual Report

scholarly journals The Impact of Climate Changes on the Thermal Performance of a Proposed Pressurized Water Reactor: Nuclear-Power Plant

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Said M. A. Ibrahim ◽  
Mohamed M. A. Ibrahim ◽  
Sami. I. Attia
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Thermal Performance ◽  
Nuclear Power ◽  
Cooling Water ◽  
Plant Performance ◽  
Pressurized Water Reactor ◽  
Pressurized Water ◽  
Water Reactor ◽  
The Impact

This paper presents a methodology for studying the impact of the cooling water temperature on the thermal performance of a proposed pressurized water reactor nuclear power plant (PWR NPP) through the thermodynamic analysis based on the thermodynamic laws to gain some new aspects into the plant performance. The main findings of this study are that an increase of one degree Celsius in temperature of the coolant extracted from environment is forecasted to decrease by 0.39293 and 0.16% in the power output and the thermal efficiency of the nuclear-power plant considered, respectively.

scholarly journals Application of Data Mining Techniques for Sensor Drift Analysis to Optimize Nuclear Power Plant Performance

2019 ◽  
Vol 9 (1) ◽  
pp. 3087-3095
Keyword(s):  
Data Mining ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Power Plants ◽  
Response Times ◽  
Protective Action ◽  
Plant Performance ◽  
Data Mining Techniques ◽  
Prime Concern

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.

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