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An active fault tolerant control (AFTC) method is proposed for discrete-time piecewise affine (PWA) systems. Only actuator faults are considered. The AFTC framework contains a supervisory scheme, which selects a suitable controller in a set of controllers such that the stability and an acceptable performance of the faulty system are held. The design of the supervisory scheme is not considered here. The set of controllers is composed of a normal controller for the fault-free case, an active fault detection and isolation controller for isolation and identification of the faults, and a set of passive fault tolerant controllers (PFTCs) modules designed to be robust against a set of actuator faults. In this research, the piecewise nonlinear model is approximated by a PWA system. The PFTCs are state feedback laws. Each one is robust against a fixed set of actuator faults and is able to track the reference signal while the control inputs are bounded. The PFTC problem is transformed into a feasibility problem of a set of LMIs. The method is applied on a large-scale live-stock ventilation model.

This document is a delivery in the project NORCOWE. It is part ofwork package WP3.2.2. The main goal is to establish the presentstate-of-the-art for wind farm control for both research and practice.The main approach will be to study the literature. This will of causebe much more efficient for the research part than for the practicepart. It is however not the intention to do company interviews orsimilar. This report is structured into a section for each WF controlobjective. These sections then includes the important controlproject issues: choice of input and output, control method, andmodelling used for controller design and simulation respectively.A short section then discusses published literature fromindustry. Finally a conclusion is given discussing establishedresults, open challenges and necessary research. An appendixpresent a method for optimising the energy in a one row windturbine farm based on a dynamic programming type of method.

In this paper, a fault tolerant control method for linear parameter varying (LPV) systems using a virtual actuator and a virtual sensor is proposed. The basic idea of the method is to insert a reconfiguration block, which consists of an LPV virtual actuator and an LPV virtual sensor, between the plant and the nominal controller such that the fault tolerant goal is achieved without re-designing the nominal controller. The role of the reconfiguration block is to transform the signals from the faulty system such that its behavior is similar to the nominal system from the point of view of the controller and to transform the output of the controller for the faulty system such that the stability and performance goals are preserved. In this paper, we consider the weak fault-hiding goal and stability of the closed loop system. Input to state stabilizing LPV gains of the virtual actuator and sensor are found by solving linear matrix inequalities (LMIs). We show that separate design of these gains guarantees the closed loop input to state stability of the closed loop reconfigured system.

A novel control approach is proposed to optimize the fatigue distribution of wind turbines in a large-scale offshore wind farm on the basis of an intelligent agent theory. In this approach, each wind turbine is considered to be an intelligent agent. The turbine at the farm boundary communicates with its neighbouring downwind turbines and organizes them adaptively into a wind delivery group along the wind direction. The agent attributes and the event structure are designed on the basis of the intelligent agent theory by using the unified modelling language. The control strategy of the intelligent agent is studied using topology models. The reference power of an individual wind turbine from the wind farm controller is re-dispatched to balance the turbine fatigue in the power dispatch intervals. In the fatigue optimization, the goal function is to minimize the standard deviation of the fatigue coefficient for every wind turbine. The optimization is constrained such that the average fatigue for every turbine is smaller than what would be achieved by conventional dispatch and such that the total power loss of the wind farm is restricted to a few percent of the total power. This intelligent agent control approach is verified through the simulation of wind data from the Horns Rev offshore wind farm. The results illustrate that intelligent agent control is a feasible way to optimize fatigue distribution in wind farms, which may reduce the maintenance frequency and extend the service life of large-scale wind farms.Copyright © 2012 John Wiley & Sons, Ltd.

In this paper, we consider the problem of recongurability of peicewise ane(PWA) systems. Actuator faults are considered. A system subject to a fault is considered as recongurable if it can be stabilized by a state feedback controller and the optimal cost of the performance of the systems is admissible. Sucient conditions for recongurability are derived in terms of feasibility of a set of Linear Matrix Inequalities (LMIs). The method is implemented on a large scale livestock hybrid ventilation model which was obtained during previous research

In wind farms, individual turbines disturb the wind field by generating wakes that influence other turbines in the farm.From a control point of view, there is an interest in dynamic optimization of the balance between fatigue and production,and an understanding of the relationship between turbines manifested through the wind field is hence required. This paperdevelops models for this relationship. The result is based on two new contributions: the first is related to the estimation ofeffective wind speeds, which serves as a basis for the second contribution to wind speed prediction models.Based on standard turbine measurements such as rotor speed and power produced, an effective wind speed, whichrepresents the wind field averaged over the rotor disc, is derived. The effective wind speed estimator is based on acontinuous–discrete extended Kalman filter that takes advantage of nonlinear time varying turbulence models. The esti-mator includes a nonlinear time varying wind speed model, which compared with literature results in an adaptive filter.Given the estimated effective wind speed, it is possible to establish wind speed prediction models by system identification.As the prediction models are based on the result related to effective wind speed, it is possible to predict wind speeds atneighboring turbines, with a separation of over 700 m, up to 1 min ahead reducing the error by 30% compared with apersistence method. The methodological results are demonstrated on data from an off-shore wind farm. Copyright © 2011John Wiley & Sons, Ltd.

Development of dynamic wind flow models for wind farms is part of the research in European research FP7 project AEOLUS. The objective of this report is to provide decentralized dynamic wind flow models with parameters. The report presents a structure for decentralized flow models with inputs from a set of spatially distributed measurements from wind turbines. The information has to be communicated only within neighboring wind turbines. This will both reduce the calculation load by distributing them on all turbines and make the infrastructure more robust against faults and uncertainties. Moreover, the presented flow model is formulated such that it is able to update the parameters in an adaptive on-line procedure. The report also investigates the effect of wind direction changes on the flow model and proposes a fusion algorithm which improves the wind predictions by fusing the predictions of available local models. The results of this report are especially useful, but not limited, to design a decentralized wind farm controller, since in centralized controller design one can also use the model and update it in a central computing node.

An algorithm for active fault detection and isolation is proposed. In order to observe the failure hidden due to the normal operation of the controllers or the systems, an optimization problem based on minimization of test signal is used. The optimization based method imposes the normal and faulty models predicted outputs such that their discrepancies are observable by passive fault diagnosis technique. Isolation of different faults is done by implementation a bank of Extended Kalman Filter (EKF) where the convergence criterion for EKF is confirmed by Genetic Algorithm (GA). The method is applied to the two tank bench mark example in presence of two faults.

A method for active diagnosis of hybrid systems is proposed. The main idea is to predict the future output of both normal and faulty model of the system; then at each time step an optimization problem is solved with the objective of maximizing the difference between the predicted normal and faulty outputs constrained by tolerable performance requirements. As in standard model predictive control, the first element of the optimal input is applied to the system and the whole procedure is repeated until the fault is detected by a passive diagnoser. It is demonstrated how the generated excitation signal can be used as a test signal for sanity check at the commissioning or for detection of faults hidden by regulatory actions of the controller. The method is tested on the two tank benchmark example. ©2009 IEEE.