@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}},
}