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Power curve plays a vital role in design, operation, control, and condition monitoring of wind turbines. Especially, it provides necessary information for wind farm maintenance decisions, allowing for not only comparison among the same type of wind turbines installed in different places, but also the same one at different time stamps. A standardized power curve iterative modeling procedure of wind turbines is proposed based on actual supervisory control and data acquisition (SCADA) data for performance evaluation in an online manner with guaranteed smoothness. The data are firstly preprocessed for anomaly detection by following normalization correction and then sliced into different wind speed bins. The least‐square B‐spline approximation method is iteratively implemented for power curve construction based on dominant points selected from the bin centroids, and turbulence intensity correction and outlier detection are conducted iteratively for refining identification results. Finally, the test results based on actual operating SCADA data demonstrate better performance in comparison with two counterparts. [ABSTRACT FROM AUTHOR]

As wind energy is increasingly exploited worldwide, optimisation of wind farm layout becomes more crucial. To guarantee the economic efficiency and profit of a wind farm, the deployment of wind turbines has to be optimised before operation. Traditional methods usually assume that identical type of wind turbines are utilised in a layout design. In this study, multiple types of turbines are considered in wind farm layout optimisation in complex terrain, namely mixed‐installation. By utilising different power generation characteristics, hub heights and rotor diameters, and cost models of different types of turbines, the efficiency of a wind farm can be further improved. A single‐objective optimisation problem is firstly established by modelling all aforementioned factors, and the objective is to achieve a minimum cost per unit of energy. Subsequently, after using computational fluid dynamics to simulate the wind flow over complex terrain, a genetic algorithm‐particle swarm optimisation optimisation algorithm is then proposed to determine the position and type of every individual turbine simultaneously. Eventually, extensive simulation studies are present to verify the feasibility of this scheme. [ABSTRACT FROM AUTHOR]

Abstract: A novel optimal approach named invasive weed optimization‐control vector parameterization (IWO‐CVP) for chemical dynamic optimization problems is proposed where CVP is used to transform the problem into a nonlinear programming (NLP) problem and an IWO algorithm is then applied to tackle the NLP problem. To improve efficiency, a new adaptive dispersion IWO‐based approach (ADIWO‐CVP) is further suggested to maintain the exploration ability of the algorithm throughout the entire searching procedure. Several classic chemical dynamic optimization problems are tested and detailed comparisons are carried out among ADIWO‐CVP, IWO‐CVP, and other methods. The research results demonstrate that ADIWO‐CVP not only is efficient, but also outperforms IWO‐CVP in terms of both accuracy and convergence speed. [ABSTRACT FROM AUTHOR]

Fault diagnosis is always a vital technology in the chemical industry and is influenced by a large number of process variables in the practical manufacturing process. Thus, an effective diagnosis method is crucial in practical chemical processes. A novel fault diagnosis method is proposed based on the weighted least squares support vector machine (WLSVM) to deal with the small sample and non-linear partition data. It provides the strong potential in predicting faults, especially by further employing a suitable particle swarm optimization (PSO) algorithm to determine the most important parameters of the punishment factor and the Gaussian RBF kernel, which has the advantages of high accuracy and low false alarm rate. PSO-WLSSVM is further accomplished with the data from the classic benchmark test set Tennessee Eastman (TE) process. Also, the models SVM, PSO-SVM, and PSO-LSSVM are employed on the TE process to compare the performance of PSO-WLSSVM. The results show the validity of the proposed approach on the TE process. [ABSTRACT FROM AUTHOR]

Transient control performance of power systems is extremely important, especially during the recovery stage from a system temporary fault. A large power angle error or generator relative speed could result in unrecoverable failure or system damage. In this study, a class of novel excitation controllers is proposed for power systems to place user‐defined time‐varying constraints on the system errors. First, the direct feedback linearisation technique is employed to transfer the original non‐linear system to a linear one. An error transformation method is introduced to generate a new non‐linear system, whose stability ensures that the time‐varying constraints on the original system are satisfied. Thus, a backstepping‐based excitation control methodology is designed for ideal case by assuming all system parameters are known. Thereafter, the uncertainties of system dynamics are also considered and compensated by designing an adaptive excitation controller. By utilising the standard Lyapunov synthesis, the closed‐loop system is proved to be asymptotically stable, and all the other signals are bounded for the two control schemes. Furthermore, the system transient performance can be shaped arbitrarily with predefined trajectories, in the sense that the system errors never cross certain boundaries during transient phase. Eventually, extensive simulation studies demonstrate the feasibility of the theoretical results. [ABSTRACT FROM AUTHOR]

This article investigates the interindividual variability of underlying glucose dynamics and the relative predictive power of exogenous inputs in different frequency bands (FBs) for online subcutaneous glucose prediction for subjects with type 1 diabetes mellitus. Auto-regressive (AR) models and AR models with exogenous inputs (ARX) are developed based on two groups of ambulatory subjects and two groups of in silico subjects using different combinations of FBs. Some important modeling parameters are studied with respect to their influences on glucose prediction. Four issues are of particular interest and discussed based on the illustration results, suggesting that: (i) In different frequency bands, the underlying glucose dynamics act differently across subjects for online glucose prediction; (ii) A global AR model can be developed for one subject in some FB and then used to make online glucose predictions for other subjects in the same FB, revealing little interindividual variability; (iii) The exogenous inputs have different influences in different FBs for prediction of future subcutaneous glucose concentration; (iv) The exogenous inputs may not excite the glucose signals in some FBs, that is, the inclusion of the exogenous inputs may not result in more accurate models in comparison with standard AR model in some FBs. © 2013 American Institute of Chemical Engineers AIChE J, 59: 4228-4240, 2013 [ABSTRACT FROM AUTHOR]

A new model-based control strategy for the internal thermally coupled distillation column (ITCDIC) is presented. Based on the nonlinear wave theory that describes the nonlinear dynamics in the separation processes, a simplified nonlinear wave model is established that concerns both the wave propagation and the profile shape. An advanced controller (WGGMC) is formulated by combining the nonlinear wave model with a generalized generic model control (GGMC). Compared with a conventional generic model controller based on a data-driven model (TGMC), and another wave-model based generic model controller (WGMC) developed in our previous work, WGGMC exhibits the best performances in both servo control and regulatory control. Furthermore, WGGMC can handle a very-high-purity system of ITCDIC with top product composition of 0.99999, while the other two controllers fail to work. © 2013 American Institute of Chemical Engineers AIChE J, 59: 4133-4141, 2013 [ABSTRACT FROM AUTHOR]

The quality-concerned between-phase transition analysis is performed and an improved calibration modeling strategy is designed for quality prediction and interpretation in multiphase batch processes. From the between-phase viewpoint, the quality-related phase behaviors are decomposed and two subspaces are separated. In common subspace, the underlying quality-relevant variation stays invariable between the neighboring phases, showing the common contribution to quality. The other part changes with the alternation of phases and has the different influences on quality interpretation, termed specific subspace here. Based on subspace separation, between-phase transition regions are distinguished from steady phases. Different models are developed in steady phases and transition regions respectively for online quality prediction. Offline quality analyses are also conducted in two subspaces to explore the time cumulative effects. The proposed method gives an interesting insight into the phase behaviors and between-phase transitions for quality prediction. The feasibility and performance of the proposed method are illustrated with a typical multiphase batch process. © 2012 American Institute of Chemical Engineers AIChE J, 59: 108-119, 2013 [ABSTRACT FROM AUTHOR]

This paper is concerned with the delay-dependent stability and robust stability for uncertain systems with time-varying delay. Through constructing an appropriate type of Lyapunov-Krasovskii functional and proving its positive definiteness, using slack matrices and a convex combination condition, the delay-dependent stability criteria, which are less conservative, are derived in terms of linear matrix inequalities. Numerical examples are also given to illustrate the improvement on the conservatism of the delay bound over some existing results. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [ABSTRACT FROM AUTHOR]