Abstract
Robust Data Reconciliation strategies provide unbiased variable estimates in the presence of a moderate quantity of measurement gross errors. Systematic errors which persist in time, as biases or drifts, overcome this quantity causing the deterioration of the estimates. This also occurs due to the presence of process leaks. The fast detection of those faults avoids the use of biased solutions of the data reconciliation procedure, and allows to perform quick corrective actions. In this work, a methodology for leak detection is incorporated into a robust data reconciliation procedure that detects and classifies systematic observation errors. The strategy makes use of the Robust Measurement Test, to detect outliers and leaks, and the Robust Linear Regression of the data contained in a moving window to distinguish between biases and drifts. The methodology is applied for two benchmarks extracted from the literature. Results highlight the performance of the proposed strategy.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Latin American Applied Research - An international journal
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
