District Heating Network (DHN) pipes can be affected by faults that endanger system reliability. Thus, this paper develops a novel modeling and diagnostic approach for the detection and identification of the most frequent faults that affect DHN pipes, i.e., water leakages, heat losses and pressure losses. The modeling approach exploits physics-based equations for calculating all DHN measurable variables, i.e., flow rate, temperature and pressure. The diagnostic approach detects and identifies pipe faults by coupling the modeling approach with an optimization algorithm. As a result, the diagnostic approach provides the health indices of each pipe of the DHN, which identify the faulty pipe, the fault type and its magnitude. The modeling approach proves to be extremely accurate since the Root Mean Square Error of the DHN variables is always lower than 0.02%. Furthermore, the modeling approach is exploited to infer general guidelines about the DHN health state to investigate the relationship between health indices and DHN characteristics. The novel diagnostic approach is verified by implanting six faults in the DHN of the campus of the University of Parma. All faults are correctly detected and identified, by also evaluating the exact fault magnitude.

A diagnostic approach for fault detection and identification in district heating networks

Lucrezia Manservigi
Primo
;
Hilal Bahlawan
Secondo
;
Enzo Losi;Pier Ruggero Spina
Penultimo
;
Mauro Venturini
Ultimo
2022

Abstract

District Heating Network (DHN) pipes can be affected by faults that endanger system reliability. Thus, this paper develops a novel modeling and diagnostic approach for the detection and identification of the most frequent faults that affect DHN pipes, i.e., water leakages, heat losses and pressure losses. The modeling approach exploits physics-based equations for calculating all DHN measurable variables, i.e., flow rate, temperature and pressure. The diagnostic approach detects and identifies pipe faults by coupling the modeling approach with an optimization algorithm. As a result, the diagnostic approach provides the health indices of each pipe of the DHN, which identify the faulty pipe, the fault type and its magnitude. The modeling approach proves to be extremely accurate since the Root Mean Square Error of the DHN variables is always lower than 0.02%. Furthermore, the modeling approach is exploited to infer general guidelines about the DHN health state to investigate the relationship between health indices and DHN characteristics. The novel diagnostic approach is verified by implanting six faults in the DHN of the campus of the University of Parma. All faults are correctly detected and identified, by also evaluating the exact fault magnitude.
2022
Manservigi, Lucrezia; Bahlawan, Hilal; Losi, Enzo; Morini, Mirko; Spina, Pier Ruggero; Venturini, Mauro
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/2500617
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