Identification of Performance Indicators for Nuclear Power Plants

Report Date: 

September 2001




Performance indicators have been assuming an increasingly important role in the nuclear industry. An integrated methodology is proposed in this research for the identification and validation of performance indicators for assessing and predicting nuclear power plant overall performance (i.e., both economic and safety performance) in a systematic and quantitative way. The methodology consists of four steps: the selection of target sites/plants, the identification and refinement of candidate indicators, the collection of historical operating records of selected indicators, and the identification and evaluation of correlations between selected indicators and plant performance through data analysis. The methodology is centered upon individual plants, using plant-specific operation records to identify and validate plant-specific correlations. It can also be applied to multiple plants and the results from different plants can be compared to identify and analyze commonalities and differences in plant operations across-plant.

Case studies of the proposed methodology were performed at three target plants. A list of candidate performance indicators was identified through a sensitivity analysis on a quantitative model of nuclear power plant operation. The list was validated and supplemented through interviews with plant personnel and a refined, plant-specific list was obtained for each target plant. Historical operating records of candidate indicators in the lists were collected from target plants. Data analyses, including correlational analysis, multivariate regression analysis, and lead/lag time analysis, were performed using the historical data collected.

The methodology was originally intended for the identification of leading indicators, which can provide advance warnings of deterioration of performance before the direct outcome indicators are affected. A regression-based lead/lag time analysis method was proposed and applied in the case studies to evaluate lead/lag relationships between candidate indicators and plant performance. However, the method did not produce stable and reliable results by using the data currently available at the target plants and was not able to identify leading indicators with certainty. As a result, we shifted the focus of our data analysis to identifying correlations between candidate indicators and plant performance through correlational analysis and multivariate regression analysis.

Several findings are noteworthy: (1) Data analysis results were sensitive to the indicators and data points used, mainly due to the small number of data points (30~60) available for use in the analyses; (2) Data analysis results generally agreed with our knowledge and expectation, with a few exceptions; (3) Correlations showed large variations from plant to plant; (4) Correlations varied from time to time at most target plants; (5) The outcome indicators with smoother patterns (e.g., the INPO performance index) tended to correlate better with candidate indicators than the outcome indicators that measured relatively rare events and had sharp changes in their patterns (e.g., unplanned capability loss factor); (6) Work order backlogs stood out as important indicators for all three target plants; corrective maintenance generally showed a negative correlation with plant performance, while preventive maintenance generally showed a positive correlation with plant performance; (7) Supervisor availability and indicators measuring minor events were also identified to be important performance indicators.

A major finding of the project effort is that the data currently available at NPPs are not sufficient to validate and support formulation of industry-wide performance indicators. The data are too sparse and inconsistently formulated among different plants. In order for future analyses to produce more reliable results, the NPP operators need to adopt more extensive and more standardized data collection programs in order to improve the quality of the data set. Recommendations are provided on how these improvements should be made.


  • NSP

    Nuclear Systems Enhanced Performance


  • TR

RPT. No.: