Tieling Zhang

Bio: Tieling Zhang is an academic researcher from University of Wollongong. The author has contributed to research in topics: Turbine & Asset management. The author has an hindex of 15, co-authored 60 publications receiving 874 citations. Previous affiliations of Tieling Zhang include University of Tokyo & University UCINF.

Failure Data Analysis with Extended Weibull Distribution

Abstract: A three-parameter distribution called extended Weibull distribution is investigated in this article. This model is generated by a method of introducing an additional parameter into a family of distributions by Marshall and Olkin (1997). It has two-parameter Weibull distribution as a special case. One of the merits of this distribution is that the hazard-rate can be increasing, decreasing, or initially increasing, then decreasing and eventually increasing. The model characterization based on the Weibull Probability Plot (WPP) is studied in this article. The WPP for actual data set can be concave, convex, or likely S-shaped. A procedure is provided for parameter estimation based on WPP. In addition, the maximum likelihood estimation is also presented. An example is shown to illustrate the procedure and application.

A method and a system for controlling operation of a wind turbine

Abstract: A method and a system for controlling operation of a wind turbine are provided. The method includes determining at least one failure mode relating to one or more components of the wind turbine, estimating a remaining lifetime of the component under current operating conditions, determining one or more control schemes to control the operation of the wind turbine in order to adjust the remaining lifetime of the component to a desired remaining lifetime of the component, determining a power production yield for the determined one or more control schemes and selecting a determined control scheme for controlling the operation of the wind turbine that maximizes the power production yield.

Reliability Engineering and System Safety

Abstract: This paper presents a polynomial dimensional decomposition (PDD) method for global sensitivity analysis of stochastic systems subject to independent random input following arbitrary probability distributions. The method involves Fourier-polynomial expansions of lower-variate component functions of a stochastic response by measure-consistent orthonormal polynomial bases, analytical formulae for calculating the global sensitivity indices in terms of the expansion coefficients, and dimension-reduction integration for estimating the expansion coefficients. Due to identical dimensional structures of PDD and analysis-of-variance decomposition, the proposed method facilitates simple and direct calculation of the global sensitivity indices. Numerical results of the global sensitivity indices computed for smooth systems reveal significantly higher convergence rates of the PDD approximation than those from existing methods, including polynomial chaos expansion, random balance design, state-dependent parameter, improved Sobol’s method, and sampling-based methods. However, for non-smooth functions, the convergence properties of the PDD solution deteriorate to a great extent, warranting further improvements. The computational complexity of the PDD method is polynomial, as opposed to exponential, thereby alleviating the curse of dimensionality to some extent. Mathematical modeling of complex systems often requires sensitivity analysis to determine how an output variable of interest is influenced by individual or subsets of input variables. A traditional local sensitivity analysis entails gradients or derivatives, often invoked in design optimization, describing changes in the model response due to the local variation of input. Depending on the model output, obtaining gradients or derivatives, if they exist, can be simple or difficult. In contrast, a global sensitivity analysis (GSA), increasingly becoming mainstream, characterizes how the global variation of input, due to its uncertainty, impacts the overall uncertain behavior of the model. In other words, GSA constitutes the study of how the output uncertainty from a mathematical model is divvied up, qualitatively or quantitatively, to distinct sources of input variation in the model [1].

Teaching for quality learning at university

Abstract: by J. Biggs and C. Tang, Maidenhead, England, Open University Press, 2007, 360 pp., £29.99, ISBN-13: 978-0-335-22126-4

Overview of Control Systems for the Operation of DFIGs in Wind Energy Applications

Abstract: Doubly fed induction generators (DFIGs), often organized in wind parks, are the most important generators used for variable-speed wind energy generation. This paper reviews the control systems for the operation of DFIGs and brushless DFIGs in wind energy applications. Control systems for stand-alone operation, connection to balanced or unbalanced grids, sensorless control, and frequency support from DFIGs and low-voltage ride-through issues are discussed.

The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review

Abstract: Wind turbines have been developed fast in the recent years and at the same time have brought some problems. It is important to maintain the healthy condition of the running turbine because the consequences after faults are miserable for both the company and owner. There is a constant need to reduce the costs of operating and maintaining the turbines. Therefore, it is very important to detect the faults/failures early so as to minimize downtime and maximize productivity. This paper reviewed the structure of wind turbines and analyzed the different components of wind turbines in order to detect the faults that may happen. Meanwhile, this paper mainly reviewed fault diagnosis methods of wind turbines in the last three years. The main purpose of this paper is to supply some information on structure healthy condition monitoring (SHCM) and fault diagnosis in wind turbines for related researchers.