# Application and Optimization of Algorithms for Pressure Wave Evaluation Based on Measurement Data
## 6 Conclusions
Therefore, the PC values of each algorithm for all the events and all possible time frames were summarized and illustrated in nine heat maps in Figure 6. Each algorithm had an individual overall best time frame \([T1,\ T2]\). Furthermore, the mean PC value was calculated for all the possible time frames. The PDN algorithm had the highest mean PC value with 61.2%. In contrast, the LNR algorithm had the worst mean PC value with 48.4%. The PDN algorithm showed the smallest standard deviation. This algorithm was the most insensitive to change in the time frame compared to the other algorithms. To this end, CPD was the most affected by change in the evaluation time frame. After the determination of the overall best time frame \([T1,\ T2]\) for each algorithm, they were applied with their individual overall best time frames to all 22 real events. The affected EA was in the first place at least once for one event and algorithm. Hence, every algorithm achieved another PC for an event, and multiple algorithms were necessary to determine the correct PDTP and localize the leakage. However, this is not applicable in practice, as well. As mentioned before, the optimization of the time frame and selection of the best-performing algorithm were only possible in cases of known leakages. Based on the mean value of each algorithm, the BCP algorithm was the most suitable within its overall best time frame with a PC of 77.3%. This algorithm should be applied in practice, and the overall best time frame should be set to be \([T1,\ T2]=[-100,\ 30]\). In 9 of 22 real events, the affected EAs were ranked in the first three places. The BCP algorithm was selected because it had the highest PC value on average. Based on its used Bayesian methods to estimate change-points, it is also suitable if the pressure data are changing. Future work will focus on improved algorithms to determine PDTPs from measurement data. Since each event is unique and further real events are unknown, the chosen algorithm should be maximally robust. For now, the chosen BCP algorithm will be implemented in practice with its overall best time frame. In the case of the next real event, the algorithm will be able to show its full potential. Regarding events, a more detailed investigation should be performed, as totally different evaluability levels, on average, were found in the range between 38.5 and 86.9%. Furthermore, the large drop in PC values when changing from the optimal to the overall best time frame should also be investigated. As a selection of algorithms based on real events, ranking might change with new insights, and the described evaluation of the algorithms can be reconducted in the case of new events. **Author Contributions:** Conceptualization, K.V. and B.R. ; Methodology, K.V. and Y.Y. ; Software, K.V. ; Validation, B.R. ; Formal Analysis, F.K. ; Investigation, K.V. and Y.Y. ; Resources, B.R. ; Data Curation, K.V. ; Writing--Original Draft Preparation, K.V. ; Writing--Review and Editing, B.R. and F.K. ; Visualization, K.V. ; Supervision, F.K. ; Project Administration, B.R. ; Funding Acquisition, F.K. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the Federal Ministry of Economic Affairs and Climate Action (BMWK), grant numbers 03 ET1624 B and C.  
**Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable. **Acknowledgments:** We would like to thank the Federal Ministry of Economic Affairs and Climate Action (BMWK) and the Project Management Juelich (Ptl) for funding this research and development project as a part of the Federal Government's 6 th Energy Research Programme (funding code: 03 ET1624 B and C). **Conflicts of Interest:** The authors declare no conflict of interest.