District Heating Networks (DHNs), which dispatch thermal energy from a heat source to end-users by means of a heat carrier, are composed of pipes that can be affected by faults that endanger system reliability. Thus, reliable diagnostic approaches have to be employed to evaluate the health state of the DHN. In the framework of the ENERGYNIUS research project, the authors of this paper developed a diagnostic approach aimed at detecting and identifying the most frequent faults that affect DHN pipes, i.e., water leakages, heat losses and pressure losses. The diagnostic approach detects and identifies pipe faults by coupling a DHN model with an optimization algorithm. As a result, the health indices of each pipe of the DHN, the fault position, its type and magnitude are provided. This study investigates the capability of the diagnostic approach by using two datasets, in which challenging faults were hypothetically implanted in the DHN of the campus of the University of Parma. The diagnostic approach successfully detected and identified both faults, by also accurately assessing fault magnitude. In addition, the relative error with which each DHN variable is predicted is lower than 0.06 %.
Fault diagnosis in district heating networks
E Losi;L Manservigi
;P R SpinaPenultimo
;M VenturiniUltimo
2022
Abstract
District Heating Networks (DHNs), which dispatch thermal energy from a heat source to end-users by means of a heat carrier, are composed of pipes that can be affected by faults that endanger system reliability. Thus, reliable diagnostic approaches have to be employed to evaluate the health state of the DHN. In the framework of the ENERGYNIUS research project, the authors of this paper developed a diagnostic approach aimed at detecting and identifying the most frequent faults that affect DHN pipes, i.e., water leakages, heat losses and pressure losses. The diagnostic approach detects and identifies pipe faults by coupling a DHN model with an optimization algorithm. As a result, the health indices of each pipe of the DHN, the fault position, its type and magnitude are provided. This study investigates the capability of the diagnostic approach by using two datasets, in which challenging faults were hypothetically implanted in the DHN of the campus of the University of Parma. The diagnostic approach successfully detected and identified both faults, by also accurately assessing fault magnitude. In addition, the relative error with which each DHN variable is predicted is lower than 0.06 %.File | Dimensione | Formato | |
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