Optimizing Small Hydropower Systems in Water Distribution Systems Based on Long-Time-Series Simulation and Future Scenarios

2015 ◽  
Vol 141 (10) ◽  
pp. 04015021 ◽  
Author(s):  
Robert Sitzenfrei ◽  
Wolfgang Rauch
Keyword(s):  
Time Series ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Future Scenarios ◽  
Small Hydropower ◽  
Long Time Series ◽  
Long Time ◽  
Time Series Simulation

Event Detection in Water Distribution Systems from Multivariate Water Quality Time Series

2012 ◽  
Vol 46 (15) ◽  
pp. 8212-8219 ◽  
Author(s):  
Lina Perelman ◽  
Jonathan Arad ◽  
Mashor Housh ◽  
Avi Ostfeld
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Event Detection ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems

scholarly journals Time-Series-Based Leakage Detection Using Multiple Pressure Sensors in Water Distribution Systems

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3070 ◽  
Author(s):  
Yu Shao ◽  
Xin Li ◽  
Tuqiao Zhang ◽  
Shipeng Chu ◽  
Xiaowei Liu
Keyword(s):  
Time Series ◽  
Distribution Systems ◽  
Water Distribution ◽  
Time Window ◽  
Water Distribution Systems ◽  
Pressure Sensors ◽  
Leak Detection ◽  
Window Length ◽  
Threshold Selection ◽  
Scanning Time

Leak detection is nowadays an important task for water utilities as leakages in water distribution systems (WDS) increase economic costs significantly and create water resource shortages. Monitoring data such as pressure and flow rate of WDS fluctuate with time. Diagnosis based on time series monitoring data is thought to be more convincing than one-time point data. In this paper, a threshold selection method for the correlation coefficient based on time series data is proposed based on leak scenario falsification, to explore the advantages of data interpretation based on time series for leak detection. The approach utilizes temporal varying correlation between data from multiple pressure sensors, updates the threshold values over time, and scans multiple times for a scanning time window. The effect of scanning time window length on threshold selection is also tested. The performance of the proposed method is tested on a real, full-scale water distribution network using synthetic data, considering the uncertainty of demand and leak flow rates, sensor noise, and so forth. The case study shows that the scanning time window length of 3–6 achieves better performance; the potential of the method for leak detection performance improvement is confirmed, though affected by many factors such as modeling and measurement uncertainties.

scholarly journals Novelty detection for time series data analysis in water distribution systems using support vector machines

2010 ◽  
Vol 13 (4) ◽  
pp. 672-686 ◽  
Author(s):  
Stephen R. Mounce ◽  
Richard B. Mounce ◽  
Joby B. Boxall
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Support Vector Regression ◽  
Distribution Systems ◽  
Water Distribution ◽  
Time Series Data ◽  
Water Distribution Systems ◽  
Kernel Functions ◽  
Series Data ◽  
Support Vector

The sampling frequency and quantity of time series data collected from water distribution systems has been increasing in recent years, giving rise to the potential for improving system knowledge if suitable automated techniques can be applied, in particular, machine learning. Novelty (or anomaly) detection refers to the automatic identification of novel or abnormal patterns embedded in large amounts of “normal” data. When dealing with time series data (transformed into vectors), this means abnormal events embedded amongst many normal time series points. The support vector machine is a data-driven statistical technique that has been developed as a tool for classification and regression. The key features include statistical robustness with respect to non-Gaussian errors and outliers, the selection of the decision boundary in a principled way, and the introduction of nonlinearity in the feature space without explicitly requiring a nonlinear algorithm by means of kernel functions. In this research, support vector regression is used as a learning method for anomaly detection from water flow and pressure time series data. No use is made of past event histories collected through other information sources. The support vector regression methodology, whose robustness derives from the training error function, is applied to a case study.

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