# Application and Optimization of Algorithms for Pressure Wave Evaluation Based on Measurement Data
## 6 Conclusions  
In the case of large spontaneous leakages in a DHN, a shutdown is imminent. Fast separation of damaged network parts is important to secure the supply for customers. To this end, leakage localization is most important, as detection and separation can be handled well by monitoring the refill mass flow and using already available EAs. Leakage localization is a demanding task that can be tackled by different means. Here, evaluation of the negative pressure wave was considered. In previous publications, it has been shown that attribution of the EA works in a suitable way if the PDTP can be determined most precisely, such as in [8, 10, 11]. The scope of this paper was the determination of the PDTPs. There are several algorithms available to evaluate pressure sensor data. The aim was to assess the algorithms based on measurement data for all the events recorded in a real DHN to find the best-performing algorithms for practical application. As a novelty, self-developed algorithms and already existing algorithms normally used for other applications were applied here for leakage localization. Further, the influence of the time frame used for evaluation was investigated. It should be noted that the assessment of the algorithms was conducted for a small number of events of a real DHN in operation. Thus, the results can change if further events become available. Nevertheless, the achieved results were the best that are currently possible, especially as conducting experiments is difficult, expensive, and connected to certain risks. Nine different algorithms were evaluated and applied to pressure data of twenty-two real events. Five of them were self-developed, and four were selected from the literature. The algorithms were explained in detail and then applied to 22 real events. Instead of rating the PDTPs found by the algorithms directly, they were used in the second evaluation step for the attribution of EAs (cp. Figure 3). Possible positive or negative interactionsbetween the PDTPs and attribution were included, and the influence of the algorithms on total leakage localization was assessed. For localization of the leakage, the PDTPs were used in an established framework presented in [10] to generate a ranking of the EAs. To find the most suitable algorithm overall for the events, a PC in the range of 0 to 100% was used to compare the algorithms. In the case of each event, refill flow rose in a time range of several seconds up to minutes, and the change covered 10 to 400 m\({}^{3}\)/h with gradients from 9 to 3530 m\({}^{3}\)/h. Altogether, the events were unique. The LDTP was set to be at the very beginning of the refill flow rise. Data at a maximum of 120 s before and after the LDTP were used for determining the PDTP by the algorithms. First, all possible evaluation time frames \([T1,\ T2]\) were evaluated, and the PC was determined for each algorithm and all the events. It was shown that the evaluation time frame \([T1,\ T2]\) had a significant influence on the PC. In 15 out of 22 events, there was an optimal time frame that resulted in a PC of 100% in the case of the BCP algorithm. The KSS, CPD, and MAN algorithms had the highest PC values on average, with 98.5% for the optimal time frames. The DFN algorithm was the worst with a PC value on average of 89.8%. Furthermore, with 60%, the affected EA was in the highest rank for all events and all algorithms in the case of optimal time frames. The affected EA was in the first place at least once for one event and all algorithms. However, for a few events, no algorithm ranked the affected EA in the first place. The time frame could be optimized for historical events, as the affected EAs were known. For future events, it was not possible to use optimal time frames for practical application, as the affected EA is not known. Hence, the time frame had to be set before applying the algorithms. Thus, the overall best time frame for each algorithm was determined.