An angle-based leak detection method using pressure sensors in water distribution networks

Abstract

Leak detection has significant implications for the long-term stable operation of water distribution networks (WDNs). This study presented a novel leak detection method by calculating the angular variance between a pressure vector and other vectors in the database, to evaluate the presence of an anomaly in a network. The top priority for this method was to establish a reliable dataset collected from the pressure sensors, which is generated by EPANET 2.2. Numerous node water demand data in normal conditions were generated by the Monte Carlo method, and leak conditions with various leak flows were simulated by creating leak holes in the pipes. Through learning the composite normal and abnormal data in a certain proportion, the angle-based outlier detection model was employed to identify abnormal events. This angle-based method was applied in an actual WDN and the identification performance for anomalies was compared with that of previous detection methods. The results indicated that the novel method proposed in this study could significantly improve the accuracy and efficiency of leak detection compared to the threshold-based and distance-based detection methods. HIGHLIGHTS The proposed method for leak detection in the WDN combines the hydraulic model with outlier detection.; Numerous normal and leak scenarios are simulated by the hydraulic model to calculate residuals.; A clustering algorithm is used to determine the optimal locations of pressure sensors.; The performance of the proposed method is compared with the distance-based method in the literature.;

Keywords

• angle-based algorithm
• hydraulic analysis
• leak detection
• leak flow rate
• leak simulation
• water distribution networks