Niels Kjølstad Poulsen

New developments in state estimation for nonlinear systems

Based on an interpolation formula, accurate state estimators for nonlinear systems can be derived. The estimators do not require derivative information which makes them simple to implement.

Modelling and Transient Stability of Large Wind Farms

The paper is dealing with modelling and short-term voltage stability considerations of large wind farms. A physical model of a large offshore wind farm consisting of a large number of windmills is implemented in the dynamic simulation tool PSS/E. Each windmill in the wind farm is represented by a physical model of grid-connected windmills. The windmill generators are conventional induction generators and the wind farm is ac-connected to the power system. Improvements of short-term voltage stability in case of failure events in the external power system are treated with use of conventional generator technology. This subject is treated as a parameter study with respect to the windmill electrical and mechanical parameters and with use of control strategies within the conventional generator technology. Stability improvements on the wind farm side of the connection point lead to significant reduction of dynamic reactive compensation demands. In case of blade angle control applied at failure events, dynamic reactive compensation is not necessary for maintaining the voltage stability.

Economic Model Predictive Control for building climate control in a Smart Grid

Model Predictive Control (MPC) can be used to control a system of energy producers and consumers in a Smart Grid. In this paper, we use heat pumps for heating residential buildings with a floor heating system. We use the thermal capacity of the building to shift the energy consumption to periods with low electricity prices. In this way the heating system of the house becomes a flexible power consumer in the Smart Grid. This scenario is relevant for systems with a significant share of stochastic energy producers, e.g. wind turbines, where the ability to shift power consumption according to production is crucial. We present a model for a house with a ground source based heat pump used for supplying thermal energy to a water based floor heating system. The model is a linear state space model and the resulting controller is an Economic MPC formulated as a linear program. The model includes forecasts of both weather and electricity price. Simulation studies demonstrate the capabilities of the proposed model and algorithm. Compared to traditional operation of heat pumps with constant electricity prices, the optimized operating strategy saves 25-35% of the electricity cost.

Recursive forgetting algorithms

Fn the first part of this paper, a general forgetting algorithm is formulated and analysed. It contains most existing forgetting schemes as special cases. Conditions are given ensuring that the basic convergence properties will hold. In the second part of the paper, the results are applied to a specific algorithm with selective forgetting. Here, the forgetting is non-uniform in time and space. The theoretical analysis is supported by a simulation example demonstrating the practical performance of this algorithm.

A fatigue approach to wind turbine control

Conventional design of wind turbine controllers is focused on speed and produced electric power. As fatigue loads is an important design consideration, the resulting design is evaluated also with respect to the fatigue loads inflicted on the turbine structure. This is normally done by performing simulations using tools like FLEX, HAWC or FAST, followed by rainflow counting in the resulting time series. This procedure constitutes an iterative design procedure involving realisations of the stress processes in order to obtain the time series needed for fatigue estimates. The focus of this paper is the elimination of the need for process realisation. To this end, known techniques for approximative fatigue load assesment based on the spectral moments of the inflicted stress histories are applied. Assuming a linearised system model, we present a novel scheme for efficient computation of these spectral moments. The scheme is applied to obtain rapid evaluation of cost functions including fatigue loads, hereby allowing efficient numerical optimisation of the controller. Three different controller design examples are given, all defined directly in terms of component life times.

Implementation of neural network based non-linear predictive control

Abstract This paper describes a control method for non-linear systems based on generalized predictive control. Generalized predictive control (GPC) was developed to control linear systems, including open-loop unstable and non-minimum phase systems, but has also been proposed to be extended for the control of non-linear systems. GPC is model based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model, a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis on an efficient quasi-Newton algorithm. The performance is demonstrated on a pneumatic servo system.

Active Fault Diagnosis Based on Stochastic Tests

Active Fault Diagnosis Based on Stochastic Tests The focus of this paper is on stochastic change detection applied in connection with active fault diagnosis (AFD). An auxiliary input signal is applied in AFD. This signal injection in the system will in general allow us to obtain a fast change detection/isolation by considering the output or an error output from the system. The classical cumulative sum (CUSUM) test will be modified with respect to the AFD approach applied. The CUSUM method will be altered such that it will be able to detect a change in the signature from the auxiliary input signal in an (error) output signal. It will be shown how it is possible to apply both the gain and the phase change of the output signal in CUSUM tests. The method is demonstrated using an example.

Active fault diagnosis in closed-loop systems

Abstract Active fault diagnosis (AFD) of parametric faults is considered in connection with closed loop feedback systems. AFD involves auxiliary signals applied on the closed loop system. A fault signature matrix is introduced in connection with AFD and it is shown that if a limited number of faults can occur in the system, a fault separation in the fault signature matrix can be obtained. Then the single elements in the matrix only depend of a reduced number of parametric faults. This can directly be applied for fault isolation. If it is not possible to obtain this separation, it is shown how the fault signature matrix can be applied for a dynamical fault isolation, i.e. fault isolation based on the dynamic characteristic of the fault signature matrix as function of the different parametric faults.

Grey-box Modelling of Pollutant Loads From a Sewer System

Abstract Using a compact measuring unit with on-line meters for UV absorption and turbidity, it is possible to determine concentrations of organic load (chemical oxygen demand (COD) and suspended solids (SS)) anywhere in a sewer system. When measurements of the flow are available as well, the pollutant mass flow at the measuring point can be calculated. The measured data are used to estimate different models describing the load of pollutants in the sewer. A comparison of the models shows that a grey-box model is most informative and best in terms measured by the multiple correlation coefficient. The grey-box model is a state-space model, where the state represents the actual amount of deposition in the sewer, and the output from the model is the pollutant mass flow to the wastewater treatment plant (WWTP). The model is formulated by means of stochastic differential equations. Harmonic functions are used to describe the dry weather diurnal load profiles. It is found that the accumulation of deposits in the sewer depends on previous rain events and flows. By means of on-line use of the grey-box models, it is possible to predict the amount of pollutants in a first flush at any time, and hence from the capacity of the plant to decide if and when the available detention basin is to be used for storage of wastewater. The mass flow models comprise an important improvement of the integrated control of sewer and WWTP including control of equalisation basins in the sewer system. Further improvements are expected by the introduction of an additive model where dry weather situations and storm situations are modelled separately before addition to the resulting model.

Early Detection of Parametric Roll Resonance on Container Ships

Parametric roll resonance on ships is a nonlinear phenomenon where waves encountered at twice the natural roll frequency can bring the vessel dynamics into a bifurcation mode and lead to extreme values of roll. Recent years have seen several incidents with dramatic damage to container vessels. The roll oscillation, which is subharmonic with respect to the wave excitation, may be completely unexpected and a system for detection of the onset of such resonance could warn the navigators before roll angles reach serious levels. Timely warning could make remedial actions possible, such as change the ships speed and course, to escape from the bifurcation condition. This paper proposes nonparametric methods to detect the onset of roll resonance and demonstrates their performance. Theoretical conditions for parametric resonance are revisited and are used to develop efficient methods to detect its onset. Spectral and temporal correlations of the square of roll with pitch (or heave) are demonstrated to be of particular interest as indicators. Properties of the indicators are scrutinized, and a change detector is designed for the Weibull-type of distributions that were observed from a time-domain indicator for phase correlation. Hypothesis testing for resonance is developed using a combination of detectors to obtain robustness. Conditions of forced roll and disturbances in real weather conditions are analyzed and robust detection techniques are suggested. The efficacy of the methodology is shown on experimental data from model tests and on data from a container ship crossing the Atlantic during a storm.