An interval NLPV parity equations approach for fault detection and isolation of a wind farm

In this paper, the problem of fault diagnosis of a wind farm is addressed using interval nonlinear parameter-varying (NLPV) parity equations. Fault detection is based on the use of parity equations assuming unknown but bounded description of the noise and modeling errors. The fault detection test is based on checking the consistency between the measurements and the model, by finding if the formers are inside the interval prediction bounds. The fault isolation algorithm is based on analyzing the observed fault signatures online and matching them with the theoretical ones obtained using structural analysis. Finally, the proposed approach is tested using the wind farm benchmark proposed in the context of the wind farm faultdetection-and-isolation/fault-tolerant-control competition.This work has been funded by the Spanish MINECO through the project CYCYT SHERECS (ref. DPI2011-26243), by the European Commission through contract i-Sense (ref. FP7-ICT-2009-6-270428), by AGAUR through the contracts FI-DGR 2013 (ref. 2013FIB00218) and FI-DGR 2014 (ref. 2014FI B1 00172) and by the DGR of Generalitat de Catalunya (SAC group Ref. 2014/SGR/374).Peer Reviewe

Fault diagnosis of a wind farm using interval parity equations

Trabajo presentado al 19th IFAC World Congress celebrado del 24 al 29 de agosto de 2014 en Cape Town (Sudafrica).In this paper, the problem of fault diagnosis of a wind farm is addressed using interval parity equations. Fault detection is based on the use of parity equations and unknown but bounded description of the noise and modeling errors. The fault detection test is based on checking the consistency between the measurements and the model by finding if the formers are inside the interval prediction 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 is tested using the wind farm benchmark proposed in the context of the wind farm FDI/FTC competition.This work has been funded by the Spanish MINECO through the project CYCYT SHERECS (ref. DPI2011-26243), by the European Commission through contract i-Sense (ref. FP7-ICT-2009-6-270428) and by AGAUR through the contract FI-DGR 2013 (ref. 2013FIB00218).Peer Reviewe

Analysis and design of quadratically bounded QPV control systems

© 2019. ElsevierA nonlinear system is said to be quadratically bounded (QB) if all its solutions are bounded and this is guaranteed using a quadratic Lyapunov function. This paper considers the QB analysis and state-feedback controller design problems for quadratic parameter varying (QPV) systems. The developed approach, which relies on a linear matrix inequality (LMIs) feasibility problem, ensures that the QB property holds for an invariant ellipsoid which contains a predefined polytopic region of the state space. An example is used to illustrate the main characteristics of the proposed approach and to confirm the validity of the theoretical results.Peer ReviewedPostprint (author's final draft

D-stable controller design for Lipschitz NLPV system

This paper addresses the design of a state-feedback controller for a class of nonlinear parameter varying (NLPV) systems in which the nonlinearity can be expressed as a parameter-varying Lipschitz term. The controller is designed to satisfy a D-stability specification, which is akin to imposing constraints on the closed-loop pole location in the case of LTI and LPV systems. The design conditions, obtained using a quadratic Lyapunov function, are eventually expressed in terms of linear matrix inequalities (LMIs), which can be solved efficiently using available solvers. The effectiveness of the proposed method is demonstrated by means of a numerical example.Postprint (author's final draft

A two-tank benchmark for detection and isolation of cyber attacks

This paper presents a benchmark for the detection and isolation of cyber attacks, which is a non-linear controlled interconnected system based on a two tank system. In this benchmark, a malicious attacker wants to remain hidden while stealing water by altering the signals of the sensors of the levels of the tanks. It is assumed that the attacker can steal water from the tanks using extraction pumps with pre-established flow rates and, depending on the theft and the type of sensor alteration, different attack scenarios are proposed.Postprint (published version

Context-Aware Performance Benchmarking of a Fleet of Industrial Assets

Industrial assets are instrumented with sensors, connected and continuously monitored. The collected data, generally in form of time-series, is used for corrective and preventive maintenance. More advanced exploitation of this data for very diverse purposes, e.g. identifying underperformance, operational optimization or predictive maintenance, is currently an active area of research. The general methods used to analyze the time-series lead to models that are either too simple to be used in complex operational contexts or too difficult to be generalized to the whole fleet due to their asset-specific nature. Therefore, we have conceived an alternative methodology allowing to better characterize the operational context of an asset and quantify the impact on its performance. The proposed methodology allows to benchmark and profile fleet assets in a context-aware fashion, is applicable in multiple domains (even without ground truth). The methodology is evaluated on real-world data coming from a fleet of wind turbines and compared to the standard approach used in the domain. We also illustrate how the asset performance (in terms of energy production) is influenced by the operational context (in terms of environmental conditions). Moreover, we investigate how the same operational context impacts the performance of the different assets in the fleet and how groups of similarly behaving assets can be determined

Articles indexats publicats per investigadors del Campus de Terrassa: 2015

Aquest informe recull els 284 treballs publicats per 218 investigadors/es del Campus de Terrassa en revistes indexades al Journal Citation Report durant el 2015Postprint (published version

Robust estimation and diagnosis of wind turbine pitch misalignments at a wind farm level

Wind turbine pitch misalignments provoke aerodynamic asymmetries which cause severe damage to the turbine. Hence, it is of interest to develop fault tolerant strategies to cope with pitch misalignments. Fault tolerant strategies require the information regarding the diagnosis and the estimation of the faults. However, most existing works focus only on open-loop misalignment diagnosis and do not provide robust fault estimates. In this work, we present a novel strategy to both estimate and diagnose pitch misalignments. The proposed strategy is developed at a wind farm level and it exploits altogether the information provided by the temporal and spatial relations of the turbines in the farm. Fault estimation is first addressed with a closed-loop switched observer. This observer is robust against disturbances and it adapts to the varying conditions along the wind turbine operation range. Fault diagnosis is then achieved via statistical-based decision mechanisms with adaptive thresholds. Both the observer and the decision mechanisms are designed to guarantee the desired performance. Introducing some restrictions over the number of simultaneous faulty turbines in the farm, the proposed approach is ameliorated via a bank of the aforementioned observers and decision mechanisms. Finally, the strategies are tested using a well-known wind farm benchmark

Robust data-driven leak localization in water distribution networks using pressure measurements and topological information

This article presents a new data-driven method for locating leaks in water distribution networks (WDNs). It is triggered after a leak has been detected in the WDN. The proposed approach is based on the use of inlet pressure and flow measurements, other pressure measurements available at some selected inner nodes of the WDN, and the topological information of the network. A reduced-order model structure is used to calculate non-leak pressure estimations at sensed inner nodes. Residuals are generated using the comparison between these estimations and leak pressure measurements. In a leak scenario, it is possible to determine the relative incidence of a leak in a node by using the network topology and what it means to correlate the probable leaking nodes with the available residual information. Topological information and residual information can be integrated into a likelihood index used to determine the most probable leak node in the WDN at a given instant k or, through applying the Bayes’ rule, in a time horizon. The likelihood index is based on a new incidence factor that considers the most probable path of water from reservoirs to pressure sensors and potential leak nodes. In addition, a pressure sensor validation method based on pressure residuals that allows the detection of sensor faults is proposed.This work has been partially funded by SMART Project (ref.num. EFA153/16 Interreg Cooperation Program POCTEFA 2014-2020), L-BEST Project (PID2020-115905RB-C21) funded by MCIN/ AEI /10.13039/501100011033 and AGAUR ACCIO RIS3CAT UTILITIES 4.0–P1 ACTIV 4.0. ref.COMRDI-16-1-0054-03.Peer ReviewedPostprint (published version