In the above titled paper (ibid., vol. 24, no. 4, pp. 1641-1648, Nov. 09), the biography of Narayana Prasad Padhy was incorrectly printed. It should be as presented here.

This paper describes the modelling and performance effects of induction generator-based wind farms on the oscillation stability of power systems. For groups of 5, 10 and 20 induction generators, it is established that the basic dynamic properties of each group of generators are similar to those of a third-order equivalent machine model whether the generators are evenly loaded or unevenly loaded. The paper analyses the impact of induction-based wind generation on oscillation stability for multi-machine systems as applied to the standard IEEE 30 bus system. The effects of load model representation, system loading, generator output and transmission line network parameters are considered. In general, the multi-machine system is shown to be well damped under all normal operating conditions.

The trend towards large multi-MW wind turbines has given new impetus to the development of wind turbine controllers. Additional objectives are being placed on the controller making the specification of the control system more complex. A new toolbox, which assists with most of the control design cyle, has been developed. Its purpose is to assist and guide the con tr l system designer through the design cycle, thereby enabling faster design. With the choice of control strategy unrestri cted, the toolbox is sufficiently flexible to support the design process for the aforementioned more complex specifications.

Two tradeoffs in the effectiveness of automatic voltage regulators (AVRs) and power system stabilizers (PSSs) are investigated together for the first time. The first is the effect of a high-gain fast response AVR on decreasing power system oscillation stability as well as increasing transient stability, and vice versa. The second is that a PSS can reduce transient stability by overriding the voltage signal to the exciter as well as increasing oscillation stability, and vice versa. In essence, the actions of the AVR and PSS devices are dynamically interlinked. A novel Bode frequency response framework for dynamic analysis of AVR and PSS performance and tradeoffs is presented. Bode frequency response also assists with the determination of suitable generator locations for PSSs and the assessment of robustness under changing power system operating conditions.

Gaussian process (GP) is a Bayesian nonparametric regression model, showing good performance in various applications. Similar to other computational models, Gaussian process frequently encounters the matrix-inverse problem during its model-tuning procedure. The matrix inversion is generally of O(N 3 ) operations where N is the matrix dimension. We proposed using the O(N 2 )-operation quasi-Newton BFGS method to approximate/replace the exact inverse of covariance matrix in the GP context. As inspired during a paper revision, in this paper we show that by using the random-scaling technique, the accuracy and effectiveness of such a BFGS matrix-inverse approximation could be further improved. These random-scaling BFGS techniques could be widely generalized to other machine-learning systems which rely on explicit matrix-inverse.

This paper investigates new ways of inferring nonlinear dependence from measured data. The existence of unique linear and nonlinear sub-spaces which are structural invariants of general nonlinear mappings is established and necessary and sufficient conditions determining these sub-spaces are derived. The importance of these invariants in an identification context is that they provide a tractable framework for minimising the dimensionality of the nonlinear modelling task. Specifically, once the linear/nonlinear sub-spaces are known, by definition the explanatory variables may be transformed to form two disjoint sub-sets spanning, respectively, the linear and nonlinear sub-spaces. The nonlinear modelling task is confined to the latter sub-set, which will typically have a smaller number of elements than the original set of explanatory variables. Constructive algorithms are proposed for inferring the linear and nonlinear sub-spaces from noisy data.

Gaussian process (GP) regression is a Bayesian nonparametric model showing good performance in various applications. However, its hyperparameter-estimating procedure may contain numerous matrix manipulations of O(N3) arithmetic operations, in addition to the O(N2)-level storage. Motivated by handling the real-world large dataset of 24000 wind-turbine data, we propose in this paper an efficient and economical Toeplitz-computation scheme for time-series Gaussian process regression. The scheme is of O(N2) operations and O(N)-level memory requirement. Numerical experiments substantiate the effectiveness and possibility of using this Toeplitz computation for very large datasets regression (such as, containing 10000~100000 data points).

In the last few years some articles have emphasized certain fundamental inconsistencies underlying feedback control theory. The paper of Willems [1] Georgiou and Smith [2], later the works of Makila [3],[4], of Leithead et al. [5] have stressed the inconsistency of standard formalisms of linear time-invariant systems when the signals are double sided and the systems are open loop unstable. We establish a framework for a consistent time domain and frequency domain representation of discrete time linear time-invariant systems and, furthermore, that supports the consistent analysis of discrete time linear time-invariant feedback systems when signals are double sided and the systems are open loop unstable.

In this paper we present a new steering controller for cars equipped with 4-wheel steer-by-wire. The controller commands the front and rear steering angles with the objective of tracking reference yaw rate and sideslip signals corresponding to the desired vehicle handling behaviour. The structure of the controller is based on a simplified model of the lateral dynamics of 4-wheel steering cars. We show that the proposed structure facilitates the design of a robust steering controller valid for varying vehicle speed. The controller, which has been designed using classical techniques according to the Individual Channel Design (ICD) methodology, incorporates an anti-windup scheme to mitigate the effects of the saturation of the rear steering actuators. We analyse the robust stability of the resulting non-linear control system and present simulation results illustrating the performance of the controller on a detailed non-linear vehicle model.

Gaussian processes prior model methods for data analysis are applied to wind turbine time series data to identify both rotor speed and rotor acceleration from a poor measurement of rotor speed. In so doing, two issues are addressed. Firstly, the rotor speed is extracted from a combined rotor speed and generator speed measurement. A novel adaptation of Gaussian process regression based on two independent processes ratherthan a single process is presented. Secondly, efficient algorithms for the manipulation of large matrices are required. The Toeplitz nature of the matrices is exploited to derive novel fast algorithms for the Gaussian process methodology that are memory efficient.

In this paper we study a new steering controller for cars equipped with 4-wheel steer-by-wire. The controller commands front and rear steering angles with the objective of tracking reference yaw rate and sideslip signals corresponding to the desired vehicle handling behaviour. The controller compensates for changes in vehicle dynamics with forward speed and incorporates an anti-windup scheme to mitigate the effects of the saturation or failure of the rear steering actuators. We analyse the robust stability and performance of the resulting non-linear control system.

Gaussian process (GP) regression is a Bayesian non-parametric regression model, showing good performance in various applications. However, it is quite rare to see research results on log-likelihood maximization algorithms. Instead of the commonly used conjugate gradient method, the Hessian matrix is first derived/simplified in this paper and the trust-region optimization method is then presented to estimate GP hyperparameters. Numerical experiments verify the theoretical analysis, showing the advantages of using Hessian matrix and trust-region algorithms. In the GP context, the trust-region optimization method is a robust alternative to conjugate gradient method, also in view of future researches on approximate and/or parallel GP-implementation.

While there exists a substantial literature on the identification of Hammerstein and Wiener models, the identification of Wiener-Hammerstein models has received considerably less attention yet this is a model class of very great practical importance. This paper proposes an elegant approach to estimating Wiener-Hammerstein systems from measured data.

A framework for the consistent time-domain and frequency-domain representation of discrete-time linear time-invariant systems is established. As required, the class of signals constitute a linear space and the class of systems constitute an algebra of linear operators mapping the class of signals into itself. Furthermore, the inverses of the return difference operators exist and they themselves belong to the chosen class of systems. The formalism, amongst other things, supports the consistent analysis of discrete-time linear time-invariant feedback systems when signals are double-sided and the systems are open-loop unstable.

In this paper we present a tutorial overview of some of the issues that arise in the design of switched linear control systems. A benchmark regulation problem is then presented. Copyright © 2003 John Wiley & Sons, Ltd.

In this note we present an easily verified condition for determining a minimal state space realisation of a class of nonlinear systems using transfer function (input-output) formation only. This result has important implications for systems theory. Examples are given to demonstrate the power of this result.

Gaussian processes provide an approach to nonparametric modelling which allows a straightforward combination of function and derivative observations in an empirical model. This is of particular importance in identification of nonlinear dynamic systems from experimental data. 1) It allows us to combine derivative information, and associated uncertainty with normal function observations into the learning and inference process. This derivative information can be in the form of priors specified by an expert or identified from perturbation data close to equilibrium. 2) It allows a seamless fusion of multiple local linear models in a consistent manner, inferring consistent models and ensuring that integrability constraints are met. 3) It improves dramatically the computational efficiency of Gaussian process models for dynamic system identification, by summarising large quantities of near-equilibrium data by a handful of linearisations, reducing the training set size - traditionally a problem for Gaussian process models.

Recent years have witnessed an enormous growth of interest in dynamic systems that are characterised by a mixture of both continuous and discrete dynamics, commonly referred to as hybrid or switching systems. Nevertheless, there is a notable lack of suitable benchmark problems on which to assess and compare competing analysis and design methods. The present paper provides a collection of detailed benchmark design and analysis tasks which, while somewhat simplified in nature, reflect the scale and complexity of the tasks encountered in at least one important application domain, namely wind turbine regulation.

We investigate the reconstruction of nonlinear systems from locally identified linear models. It is well known that the equilibrium linearisations of a system do not uniquely specify the global dynamics. Information about the dynamics near to equilibrium provided by the equilibrium linearisations is therefore combined with other information about the dynamics away from equilibrium provided by suitable measured data. That is, a hybrid local/global modelling approach is considered. A non-parametric Gaussian process prior approach is proposed for combining in a consistent manner these two distinct types of data. This approach seems to provide a framework that is both elegant and powerful, and which is potentially in good accord with engineering practice.