Comparative analysis of select techniques and metrics for data reconciliation in smart energy distribution network
Reliability of each state of process in many chemical process industries largely relies upon water and vitality supplies. In this way, there is great necessity to have an improved and controlled smart energy distribution network (SEDN) in industries. In SEDNs, sensor information related to flow cont...
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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IWA Publishing
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/a218135b79194418b5600af57d32925b |
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| Sumario: | Reliability of each state of process in many chemical process industries largely relies upon water and vitality supplies. In this way, there is great necessity to have an improved and controlled smart energy distribution network (SEDN) in industries. In SEDNs, sensor information related to flow control and optimization serves as a basis for modelling of energy management systems. Therefore, it is important to ensure that sensor data are accurate and precise. However, they are affected by random noise and measurement biases, which compromise the quality of measurements. Data Reconciliation (DR) is one such approach popularly used in industries to reduce the adverse impact of random errors present in pipe flow measurements. In this study, Python-based simulations of weighted least squares (WLS) and principal component analysis (PCA) based DR techniques are implemented on the selected flow streams of SEDN, and reconciled estimates are obtained. The results show that Root Mean Square Error (RMSE) is the best performance metric since it is more sensitive to small changes in the measurement values and the reconciled estimates. Further, it is observed that PCA-DR performs better than WLS-DR in reducing the random error (and thereby achieving greater precision of measured values). HIGHLIGHTS
Application of data reconciliation (DR) techniques to treat random errors present in flow sensor data used by water distribution networks.;
Selection of best performing metric to evaluate data reconciliation (DR) techniques.;
Analyze the performance of selected DR techniques for small and large scale networks using Python-based simulation.; |
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