Abstract
Hydroelectric plants are monitored by a high number of instruments that assess various quality characteristics of interest that have an inherent variability. The readings of these instruments generate time series of data on many occasions have correlation. Each project of a dam plant has characteristics that make it unique. Faced with the need to establish statistical control limits for the instrumentation data, this article makes an approach to multivariate statistical analysis and proposes a model that uses principal components control charts and statistical and to explain variability and establish a method of monitoring to control future observations. An application for section E of the Itaipu hydroelectric plant is performed to validate the model. The results show that the method used is appropriate and can help identify the type of outliers, reducing false alarms and reveal instruments that have higher contribution to the variability.
Highlights
The control chart is one of statistical quality control techniques most used and can be very useful in controlling the instrumentation of a dam
A series of criteria for choice is presented in Jackson (1991), in this work the choice was based on the percentage of variance explained and the ability to detect out of the limit values as compared with the control chart
A measure of the suitability of a model to a time series is given by measuring the mean square error (MSE), given by where is the observed value, is the predicted value, is the number of observations and is the number of parameters of the model or the number of independent variables used in the linear regression model
Summary
The control chart is one of statistical quality control techniques most used and can be very useful in controlling the instrumentation of a dam. The chart has a center line that represents the target of the quality characteristic value if there were no variability and the other two lines, the upper and lower control limits that are determined statistically. Diagnosed singular values in monitoring dam safety, with a case study on the hydroelectric plant in China, via multivariate analysis of principal components and graphic control Hotelling (GU et al, 2011). This article aims to propose a multivariate statistical model for monitoring instruments for monitoring dams via control charts and principal components analysis and seeks to separate the effect of environmental variables on the reading of instruments from other sources of variability by use of statistics and and establish control values for monitoring future observations.
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