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LUND UNIVERSITY LIBRARIES

Leakage Detection in Thermodynamic Systems

Hansson, Karl LU (2026) MVKM01 20261
Department of Energy Sciences
Abstract
Detecting small leakages in thermodynamic cooling systems is a critical challenge in modern industrial applications, particularly in large-scale systems such as HVDC installations. Even minor leakages can reduce efficiency, increase energy consumption, and lead to higher maintenance costs. Detecting small leakages is difficult, because of complex system behavior, measurement noise, and natural variations in operating conditions. This thesis investigates methods for leakage detection and leakage rate estimation in thermodynamic systems while minimizing false alarms. The work is carried out in collaboration with Hitachi Energy, with the goal of developing a general and adaptable method suitable for industrial implementation in Siemens TIA... (More)
Detecting small leakages in thermodynamic cooling systems is a critical challenge in modern industrial applications, particularly in large-scale systems such as HVDC installations. Even minor leakages can reduce efficiency, increase energy consumption, and lead to higher maintenance costs. Detecting small leakages is difficult, because of complex system behavior, measurement noise, and natural variations in operating conditions. This thesis investigates methods for leakage detection and leakage rate estimation in thermodynamic systems while minimizing false alarms. The work is carried out in collaboration with Hitachi Energy, with the goal of developing a general and adaptable method suitable for industrial implementation in Siemens TIA Portal. The proposed approach is based on physical relationships between mass, density, and temperature, combined with filtering and signal processing techniques. Both difference-based and regression-based estimation methods are evaluated, together with the effect of filtering parameters and window lengths. The methods are first developed and validated in a simulation environment using Simcenter Amesim, and later
tested on offline data from a real test system using Python. The results show that reliable detection and estimation of leakage rates is possible under clearly detectable conditions. A key finding is the trade-off between responsiveness and stability, where fast detection does not necessarily imply fast stabilization. The study demonstrates how different estimation methods and parameter settings influence detection performance and estimation accuracy. (Less)
Popular Abstract
Small leakages in heating and cooling systems gradually reduce system efficiency, increase operating costs and make the system less reliable. Detecting these leakages is difficult because they cause weak and slowly developing changes that can resemble normal operating variations. In this work, a baseline-based detection method is evaluated together with different approaches for estimating leakage rates.
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author
Hansson, Karl LU
supervisor
organization
course
MVKM01 20261
year
type
H2 - Master's Degree (Two Years)
subject
report number
ISRN LUTMDN/TMHP-26/5686-SE
ISSN
0282-1990
language
English
id
9246697
date added to LUP
2026-07-23 14:10:01
date last changed
2026-07-23 14:10:01
@misc{9246697,
  abstract     = {{Detecting small leakages in thermodynamic cooling systems is a critical challenge in modern industrial applications, particularly in large-scale systems such as HVDC installations. Even minor leakages can reduce efficiency, increase energy consumption, and lead to higher maintenance costs. Detecting small leakages is difficult, because of complex system behavior, measurement noise, and natural variations in operating conditions. This thesis investigates methods for leakage detection and leakage rate estimation in thermodynamic systems while minimizing false alarms. The work is carried out in collaboration with Hitachi Energy, with the goal of developing a general and adaptable method suitable for industrial implementation in Siemens TIA Portal. The proposed approach is based on physical relationships between mass, density, and temperature, combined with filtering and signal processing techniques. Both difference-based and regression-based estimation methods are evaluated, together with the effect of filtering parameters and window lengths. The methods are first developed and validated in a simulation environment using Simcenter Amesim, and later
tested on offline data from a real test system using Python. The results show that reliable detection and estimation of leakage rates is possible under clearly detectable conditions. A key finding is the trade-off between responsiveness and stability, where fast detection does not necessarily imply fast stabilization. The study demonstrates how different estimation methods and parameter settings influence detection performance and estimation accuracy.}},
  author       = {{Hansson, Karl}},
  issn         = {{0282-1990}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Leakage Detection in Thermodynamic Systems}},
  year         = {{2026}},
}