Modelling uncertainty for leak localization in Water Networks

The performance and success of model-based leak localization methods applied to water distribution networks (WDN) highly depends on the uncertainty of the system considered. This work proposes an original method of modeling the effect of uncertainties in these networks. The proposed method is based on the collection of real data in the water network in the absence of leaks. The discrepancy (residual) between the measured data and the one provided by a simulator of the network in normal operation is used to extrapolate the possible residuals in the different leak scenarios. In addition, indicators for assessing the effect of uncertainty in the performance of leak localization methods based on residual correlation analysis are provided. The error in terms of correlation intervals and leak localzation assessment between the proposed approximation and the real one is studied by means a simplified model of the WDN of Hanoi (Vietnam).Postprint (published version

Robust optimization based energy dispatch in smart grids considering demand uncertainty

In this study we discuss the application of robust optimization to the problem of economic energy dispatch in smart grids. Robust optimization based MPC strategies for tackling uncertain load demands are developed. Unexpected additive disturbances are modelled by defining an affine dependence between the control inputs and the uncertain load demands. The developed strategies were applied to a hybrid power system connected to an electrical power grid. Furthermore, to demonstrate the superiority of the standard Economic MPC over the MPC tracking, a comparison (e.g average daily cost) between the standard MPC tracking, the standard Economic MPC, and the integration of both in one-layer and two-layer approaches was carried out. The goal of this research is to design a controller based on Economic MPC strategies, that tackles uncertainties, in order to minimise economic costs and guarantee service reliability of the system.Postprint (author's final draft

Fault tolerant model predictive control of open channels

Automated control of water systems (irrigation canals, navigation canals, rivers etc.) relies on the measured data. The control action is calculated, in case of feedback controller, directly from the on-line measured data. If the measured data is corrupted, the calculated control action will have a different effect than it is desired. Therefore, it is crucial that the feedback controller receives good quality measurement data. On-line fault detection techniques can be applied in order to detect the faulty data and correct it. After the detection and correction of the sensor data, the controller should be able to still maintain the set point of the system. In this paper this principle using the sensor fault masking is applied to model predictive control of open channels. A case study of a reach of the northwest of the inland navigation network of France is presented. Model predictive control and water level sensor masking is applied.Peer ReviewedPostprint (published version

Modelling uncertainty for leak localization in Water Networks

The performance and success of model-based leak localization methods applied to water distribution networks (WDN) highly depends on the uncertainty of the system considered. This work proposes an original method of modeling the effect of uncertainties in these networks. The proposed method is based on the collection of real data in the water network in the absence of leaks. The discrepancy (residual) between the measured data and the one provided by a simulator of the network in normal operation is used to extrapolate the possible residuals in the different leak scenarios. In addition, indicators for assessing the effect of uncertainty in the performance of leak localization methods based on residual correlation analysis are provided. The error in terms of correlation intervals and leak localzation assessment between the proposed approximation and the real one is studied by means a simplified model of the WDN of Hanoi (Vietnam).Peer ReviewedPostprint (published version

Optimal energy dispatch in a smart micro-grid system using economic model predictive control

The problem of energy dispatch in heterogeneous complex systems such as smart grids cannot be efficiently addressed using classical control or ad-hoc methods. This paper discusses the application of Economic Model Predictive Control (EMPC) to the management of a smart micro-grid system connected to an electrical power grid. The considered system is composed of several subsystems, namely some photovoltaic (PV) panels, a wind generator, a hydroelectric generator, a diesel generator, and some storage devices (batteries). The batteries are charged with the energy from the PV panels, wind and hydroelectric generators, and they are discharged whenever the generators produce less energy than needed. The subsystems are interconnected via a DC Bus, from which load demands are satisfied. Modeling smart grids components is based on the generalized flow-based networked systems paradigm, and assuming energy generators to be stable, load demands and energy prices are known. This study shows that EMPC is economically superior to a two-layer hierarchical MPC.Peer ReviewedPostprint (author's final draft

Robust optimization based energy dispatch in smart grids considering simultaneously multiple uncertainties: load demands and energy prices

Solving the problem of energy dispatch in a heterogeneous complex system is not a trivial task. The problem becomes even more complex considering uncertainties in demands and energy prices. This paper discusses the development of several Economic Model Predictive Control (EMPC) based strategies for solving an energy dispatch problem in a smart micro-grid. The smart grid components are described using control-oriented model approach. Considering uncertainty of load demands and energy prices simultaneously, and using an economic objective function, leads to a non-linear non-convex problem. The technique of using an affine dependent controller is used to convexify the problem. The goal of this research is the development of a controller based on EMPC strategies that tackles both endogenous and exogenous uncertainties, in order to minimize economic costs and guarantee service reliability of the system. The developed strategies have been applied to a hybrid system comprising some photovoltaic (PV) panels, a wind generator, a hydroelectric generator, a diesel generator, and some storage devices interconnected via a DC Bus. Additionally, a comparison between the standard EMPC, and its combination with MPC tracking in single-layer and two-layer approaches was also carried out based on the daily cost of energy production.Postprint (published version

Detección de fallos con validación probabilística

Presentamos una estrategia general para el diseño de un bloque de detección de fallos con validación probabilística (PCV- Procesado, clasificación, validación). Se propone un esquema general de PCV, que permite diseñar un bloque de detección de fallos con validación probabilística en el porcentaje máximo de fallos no detectados (impuesto como condición de diseño) y en el porcentaje de falsas alarmas (obtenido a posteriori). En cada iteración del algoritmo secuencial, una solución candidata se valida probabilísticamente mediante un conjunto de muestras generadas aleatoria- mente. Presentamos un marco general en el que la solución candidata puede violar las restricciones para un reducido número de elementos del conjunto de validación. Este esquema generalizado muestra significativas ventajas, en particular en términos de la óbtención de la solución probabilísticaPeer ReviewedPostprint (author’s final draft

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.Peer ReviewedPostprint (author's final draft

Clustering techniques applied to sensor placement for leak detection and location in water distribution networks

This work presents an optimization strategy that maximizes the leak locatability performance of water distribution networks (WDN). The goal is to characterize and determine a sensor configuration that guarantees a maximum degree of locatability while the sensor configuration cost satisfies a budgetary constraint. The method is based on pressure sensitivity matrix analysis and an exhaustive search strategy. In order to reduce the size and the complexity of the problem the present work proposes to combine this methodology with clustering techniques. The strategy developed in this work is successfully applied to determine the optimal set of pressure sensors that should be installed in a district metered area (DMA) in the Barcelona WDN.Peer ReviewedPostprint (published version

Robust fault detection based on adaptive threshold generation using interval LPV observers

In this paper, robust fault detection based on adaptive threshold generation of a non-linear system described by means of a linear parameter-varying (LPV) model is addressed. Adaptive threshold is generated using an interval LPV observer that generates a band of predicted outputs taking into account the parameter uncertainties bounded using intervals. An algorithm that propagates the uncertainty based on zonotopes is proposed. The design procedure of this interval LPV observer is implemented via pole placement using linear matrix inequalities. Finally, the minimum detectable fault is characterized using fault sensitivity analysis and residual uncertainty bounds. Two examples, one based on a quadruple-tank system and another based on a two-degree of freedom helicopter, are used to assess the validity of the proposed fault detection approach.Postprint (published version