Jordi Saludes

Fault Diagnosis of Wind Turbines using a Set-membership Approach

Abstract In this paper, the problem of fault diagnosis in wind turbines will be addressed applying a set-membership approach. Fault detection is based on the use of parity equations and unknown but bound description of the noise and modelling errors. Fault detection test is based on checking consistency between measurements and the model by finding if there is parameter in the feasible parameter set (approximated by a zonotope) that is compatible with modelling and noise bounds. The fault isolation algorithm is based on analyzing the observed fault signatures on-line and matching them with the theoretical ones obtained using structural analysis. Finally, the proposed approach will be tested using the wind turbine benchmark proposed in the context of the WT FDI competition.

A class of uncertain linear interval models for which a set based robust simulation can be reduced to few pointwise simulations

Set based robust simulation of uncertain models allows us to analyze robustness properties of a system from a time domain perspective. In this work, a well known result from the study of the wrapping effect in the field of validated solution of ODEs is used to characterize a class of interval uncertain models with nominal linear behavior for which a robust simulation can be reduced to two conventional pointwise simulations. As a side effect, this work raises the relevance of the study of the wrapping effect for the time domain study of robustness properties of models.

Marcinkiewicz--Zygmund inequalities

We study a generalization of the classical Marcinkiewicz-Zygmund inequalities. We relate this problem to the sampling sequences in the Paley-Wiener space and by using this analogy we give sharp necessary and sufficient computable conditions for a family of points to satisfy the Marcinkiewicz-Zygmund inequalities.

Short-term demand forecasting for real-time operational control of the Barcelona water transport network

This paper focuses on the forecast of the hourly water demand data of distinct pressure floors of the Barcelona water transport network. Several methods to forecast the hourly water demand are studied and compared with the aim of being applied for the operational control of the Barcelona water transport network. The short-term forecast of the intraday series has a main feature: the double periodicity (daily and hourly). To address this issue several extensions of the classical time-series forecasting methods are proposed: seasonal ARIMA, structural models and the exponential methods without external information. The paper focuses on the daily and hourly forecasts. In the hourly forecast, the exponential smoothing method is the most accurate. On the hand, the seasonal ARIMA and the exponential smoothing are similar in the daily time scale.

Avoiding possible instability in robust simulation of stable parametric uncertain time-invariant systems

When the problem of robustly simulating a time-invariant uncertain system is faced, a one step simulation is often chosen due to its lower complexity. However, this approach does not take into account the time invariant nature of the system. This typically results in very conservative simulations, moreover, in dramatic cases it can result in unstable simulations for even stable systems. One such case is presented to illustrate the problem with a solution getting a compromise between complexity of the simulation and fidelity to the time invariant nature of the system.

Leak Detection, Isolation and Estimation in Pressurized Water Pipe Networks using LPV Models and Zonotopes

In this paper, a leak detection, isolation and estimation methodology in pressurized water pipe networks is proposed. The methodology is based on computing residuals which are obtained comparing measured pressures (heads) in selected points of the network with their estimated values by means of a Linear Parameter Varying (LPV) model and zonotopes. The structure of the LPV model is obtained from the non-linear mathematical model of the network. The proposed detection method takes into account modelling uncertainty using zonotopes. The isolation and estimation task employs an algorithm based on the residual fault sensitivity analysis. Finally, a typical water pipe network is employed to validate the proposed methodology. This network is simulated using EPANET software. Parameters of LPV model and their uncertainty bounded by zonotopes are estimated from data coming from this simulator. A leak scenario allows to assess the effectiveness of the proposed approach.

Simulation of discrete linear time-invariant fuzzy dynamic systems

In this paper, a new approach to simulation of discrete linear time-invariant dynamic systems with parameters and initial conditions described by fuzzy numbers is proposed. This approach solves the problem of fuzzy simulation by determining at each iteration a nested family of intervals, corresponding to different levels of certainty (@a-cuts), that enclose the possible system states using optimisation. The originality of this approach consists in considering uncertain parameters as time-invariant during the simulation process. The optimisation problems associated with the approach proposed must be solved globally in order to guarantee that the minimum volume box enclosing the region of system states for each level of certainty has been derived. Then, a global optimisation algorithm, based on the theory of moments and linear matrix inequality relaxations suitable for the type of problems (polynomial programs) that appear in the fuzzy simulation algorithm, is used. Finally, two real engineering application examples allow assessing the performance of the approach proposed.

The GF Mathematics Library

This paper is devoted to present the Mathematics Grammar Library, a system for multilingual mathematical text processing. We explain the context in which it originated, its current design and functionality and the current development goals. We also present two prototype services and comment on possible future applications in the area of artificial mathematics assistants.

Comparison of two non-linear model-based control strategies for autonomous vehicles

This paper presents the comparison of two nonlinear model-based control strategies for autonomous cars. A control oriented model of vehicle based on a bicycle model is used. The two control strategies use a model reference approach. Using this approach, the error dynamics model is developed. Both controllers receive as input the longitudinal, lateral and orientation errors generating as control outputs the steering angle and the velocity of the vehicle. The first control approach is based on a non-linear control law that is designed by means of the Lyapunov direct approach. The second approach is based on a sliding mode-control that defines a set of sliding surfaces over which the error trajectories will converge. The main advantage of the sliding-control technique is the robustness against non-linearities and parametric uncertainties in the model. However, the main drawback of first order sliding mode is the chattering, so it has been implemented a high order sliding mode control. To test and compare the proposed control strategies, different path following scenarios are used in simulation.

ARIMA Models for Data Consistency of Flowmeters in Water Distribution Networks

Abstract This paper presents a signal analysis methodology to check the coherence of the flowmeters data in a water distribution network telecontrol system. The proposed methodology is based on a new daily ARIMA forecasting model. Finally, the proposed methodology has been applied to the Barcelona water distribution network providing very good results by comparison with other ARIMA forecasting models proposed previously in Quevedo et al. (2006).