Leakages in District Heating Networks-Model-Based Data Set Quality Assessment and Localization.

Area of Science:

• Engineering
• Energy Systems
• Data Analysis

Background:

• Large spontaneous leakages in district heating networks (DHNs) necessitate immediate isolation to prevent heat supply interruptions.
• Previous analysis of 22 real events showed limitations in accurately localizing these leakages using measurement data.

Purpose of the Study:

• To investigate the reasons for inaccurate leakage localization in DHNs.
• To develop a novel data evaluation model (MoFoDatEv) for improved analysis of large spontaneous leakages.

Main Methods:

• Developed MoFoDatEv incorporating prior knowledge and a simplified physical model of DHN response to leakages.
• Determined the timing and duration of pressure drops caused by leakages.
• Optimized parameters and evaluation timeframes for individual events.
• Assessed data quality, categorized events, and identified correlations between pressure and refill mass flow.

Main Results:

• MoFoDatEv successfully determines the time point and duration of pressure drops from leakages.
• Event categorization revealed correlations between pressure and refill mass flow.
• A minimum leakage size for successful evaluation was deduced.
• The model demonstrated capability for direct leakage localization, integrating previous methods.

Conclusions:

• MoFoDatEv offers enhanced insights into DHN leakages, improving both analysis and localization.
• The model's effectiveness was validated using both artificial and real-world measurement data.
• Successful evaluation requires a minimum leakage size, a key finding for practical application.