Miquel A. Cugueró

Leakage isolation in water distribution networks: A comparative study of two methodologies on a real case study

Leakages are present to some extent in all water-distribution systems. This paper compares two model based methodologies for leakage isolation in water distribution networks. Both are based on the pressure measurements and pressure sensitivity analysis of nodes in a network. Simulations of the network in presence and absence of leakage may provide an approximation of this sensitivity. The difference between both methodologies relies on how the information of the sensitivity matrix is handled. In the first approach, this information is binarised using a threshold. The resultant binary matrix is assumed as a signature matrix for leakages. One of the main issues in the binarisation process is the threshold selection. Even with the best selection of a threshold, binarisation implies loss of information. The second method is based on the use of the sensitivity matrix without any transformation in order to avoid loss of information. Results obtained on a real network (a District Metered Area (DMA) of Barcelona water distribution network) using both methods are compared. Finally, some discussions and conclusions about the limitations of both techniques and future work are presented.

Abnormal quality detection and isolation in water distribution networks using simulation models

This paper proposes a model based fault localisation method to deal with abnormal quality levels based on the chlorine measurements and chlorine sensitivity analysis in a water distribution network. A fault isolation algorithm which correlates on line the residuals, generated by comparing the available chlorine measurements with their estimation using a model, with the fault sensitivity matrix is used. The proposed methodology has been applied to a District Metered Area (DMA) in the Barcelona network.

Temporal/spatial model-based fault diagnosis vs. Hidden Markov models change detection method: Application to the Barcelona water network

This paper deals with a comparison of two different fault diagnosis frameworks. The first method is based on a temporal/spatial model-based analysis by exploiting a-priori information about the system under study, so fault detection is based on monitoring the residuals of combined spatial and time series models obtained from the network. The second method aims at characterizing and detecting changes in the probabilistic pattern sequence of data coming from the network. Relationships between data streams are modelled through sequences of linear dynamic time-invariant models whose trained coefficients are used to feed a Hidden Markov Model (HMM). When the pattern structure of incoming data cannot be explained by the trained HMM, a change is detected. Here, the performance obtained from this two distinct approaches is examined by using a dataset coming from the Barcelona water transport network.

Combining contaminant event diagnosis with data validation/reconstruction: Application to smart buildings

In this work, a combined sensor data validation/reconstruction and contaminant event diagnosis approach is proposed for Smart Building systems, implemented as a two-stage approach. In the first stage, sensor communication faults are detected and missing data is estimated, in order to provide a reliable dataset to perform contaminant event diagnosis in the second stage. For the first stage, the sensor validation and reconstruction technique is based on the combined use of spatial and time series models. On the one hand, spatial models take advantage of the physical relation between different variables in the system, whilst on the other hand, time series models take advantage of the temporal redundancy of the measured variables, using Holt-Winters time series models. For the second stage, contaminant event diagnosis is based on contaminant detection and isolation estimator schemes, using adaptive thresholds by assuming certain bounds on the measurement noise and the model uncertainty. In order to apply these diagnosis schemes, state-space models have been considered in order to model the contaminant dispersion over the indoor building environment, where the contaminant event is modelled as a fault in the process which needs to be detected and isolated. Finally, the proposed approach is successfully demonstrated for the Holmes House smart building scenario.

Inconsistent sensor data detection/correction: Application to environmental systems

In this paper, a data detection/correction approach is proposed for a real environmental monitoring system, in order to provide a reliable dataset when sensor faults occur. This is the case of communication faults that may prevent the acquisition of a complete dataset, which is of paramount importance in order to successfully apply further system tasks such as fault diagnosis. Sensor detection/correction method presented here is based on the combined used of spatial and time series models. Spatial models take advantage of the physical relation between different variables emplaced in the system (temperature sensors here) while time series models take advantage of the temporal redundancy of the measured variables, by means of Holt-Winters models here. The proposed approach is successfully applied to the rock collapse forecasting system in the Torrioni di Rialba located in Lombardy (Italy).

Sensor Data Validation and Reconstruction in Water Networks: A Methodology and Software Implementation

In this paper, a data validation and reconstruction methodology that can be applied to the sensors used for real-time monitoring in water networks is presented. On the one hand, a validation approach based on quality levels is described to detect potential invalid and missing data. On the other hand, the reconstruction strategy is based on a set of temporal and spatial models used to estimate missing/invalid data with the model estimation providing the best fit. A software tool implementing the proposed data validation and reconstruction methodology is also presented. Finally, results obtained applying the proposed methodology on raw data of flow meters gathered from a real water network are also included to illustrate the performance of the proposed approach.

Big Data Analytics and Knowledge Discovery Applied to Automatic Meter Readers

The volume of data collected by a water utility is constantly growing. In this new era, data are important because guarantees the success of decisions based on the relevant values and underlying information extracted from noisy data. For instance, automatic meter reading (AMR) systems offer households and businesses the chance to understand and reduce their energy and water usage in much greater detail than previously possible, when meter readings were taken once a quarter, or even annually. Moreover, AMR could help utility firms to improve the accuracy of billing and cut visits to properties to read meters. However, with AMR, there is an exponential growth of data: an AMR produces 17500 readings per year, with a single reading every half hour. These data should be first processed in a real-time streaming, in order to be validated before being stored and translated into a metadata model which may be usable in multiple further applications. Thus, utilities have found scaling smart meter management systems difficult to handle. This motivates the use of Big Data technologies in this application domain. On the other hand, applying data analytics and knowledge discovery tools to AMR data combined with other streams of information (data coming from the billing system, call centre service and meteorological information) could help with fraud detection, maintenance requirements prediction, water/energy user consumption patterns determination and response generation to variations in the demand. This chapter presents novel algorithms and methodologies to carry out real-time streaming data processing, data analytics, data quality assessment and improvement, as well as prediction and visualization tasks, at extremely large scale and with diverse structured and unstructured data from multiple sources such as water, power, telecommunication and other utilities, as well as from social media. The algorithms and methodologies will be illustrated using real data coming from several water utilities.

Sensor Data Validation and Reconstruction

In a real water network, a telecontrol system must periodically acquire, store and validate sensor data to achieve accurate monitoring of the whole network in real time. For each sensor measurement, data are usually represented by one-dimensional time series. These values, known as raw data, need to be validated before further use to assure the reliability of the results obtained when using them. In real operation, problems affecting the communication system, lack of reliability of sensors or other inherent errors often arise, generating missing or false data during certain periods of time. These data must be detected and replaced by estimated data. Thus, it is important to provide the data system with procedures that can detect such problems and assist the user in monitoring and processing the incoming data. Data validation is an essential step to improve data reliability. The validated data represent measurements of the variables in the required form where unnecessary information from raw data has been removed. In this chapter, a methodology for data validation and reconstruction of sensor data collected from a water network is developed, taking into account not only spatial models, but also temporal models (time series of each sensor) and internal models of the several components in the local units (e.g., pumps, valves, flows, levels). The methodology is illustrated by means of its application to flow and level meters of the Catalonia Regional Water Network.

Uncertainty effect on leak localisation in a DMA

The leak localisation methodologies based on data and models are affected by both uncertainties in the model and in the measurements. This uncertainty should be quantified so that its effect on the localisation methods performance can be estimated. In this paper, a model-based leak localisation methodology is applied to a real District Metered Area using synthetic data. In the generation process of the data, uncertainty in demands is taken into account. This uncertainty was estimated so that it can justify the uncertainty observed in the real measurements. The leak localisation methodology consists, first, in generating the set of possible measurements, obtained by Monte Carlo Simulation under a certain leak assumption and considering uncertainty, and second, in falsifying sets of nodes using the correlation with a leak residual model in order to signal a set of possible leaky nodes. The assessment is done by means of generating the confusion matrix with a Monte Carlo approach.

Control-oriented Sensor/Actuator Location Measures for Active Noise Control

Abstract In this work, a combination of measures to quantify sensor and actuator allocation according to performance, robustness and controller implementation criteria are defined. Their computation can be made with standard software, both for SISO and MIMO systems. A test is run on a simulated acoustic tube which validates the optimal measure against the best closed loop performance and lower controller order combination.