Heping Liu

An Integrated Wind Power Forecasting Methodology: Interval Estimation Of Wind Speed, Operation Probability Of Wind Turbine, And Conditional Expected Wind Power Output Of A Wind Farm

The article presents a novel quantitative methodology for wind farm management. The methodology starts by forecasting the time series mean and volatility of wind speed. The forecasting of wind speed mean and its volatility is built on an autoregressive moving average model with a generalized autoregressive conditional heteroscedasticity process, namely an ARMA-GARCH model. With the prediction of wind speed mean and its volatility, the article establishes the interval estimation of wind speed which makes the prediction of wind speed more accurate and reliable. To facilitate the quantitative management of wind farm, the operation probability (OP) of wind turbine is formulated according to the interval estimation of wind speed. Based on the characteristics power curve of wind turbine, the article develops the conditional expected wind power output equation (CEWPOE). The interval estimation of wind speed, the OP of wind turbine, and the CEWPOE thus comprise an integrated methodology for the quantitative management of wind farm operations.

Prediction of wireless network connectivity using a Taylor Kriging approach

The research aim of this paper is to investigate the effectiveness of a new Kriging model which uses Taylor expansion to predict wireless network connectivity. Wireless network connectivity is measured by the strength of emitted signal power from the tower to the point in question. The prediction results are compared with those from the literature where an Ordinary Kriging model and a neural network are used to conduct the same prediction. Root mean squared error (RMSE) and maximum absolute relative error (MARE) show that the prediction results of the new Kriging model are much better than those obtained before with average differences from 51.56% to 85%. This study shows the promise of the new Kriging model to accurately estimate wireless signal strength.

Inferring the Size Distribution of 3D Particle Clusters in Metal Matrix Nanocomposites

Metal matrix nanocomposites (MMNCs) are produced by dispersing reinforcing nanoparticles into metal matrix. It is a type of emerging materials with high strength and light weight and draws significant attentions in recent years. If the particles are not well dispersed, they will form particle clusters in the metal matrix. These clusters will detrimentally impact on the final quality of MMNCs. This paper proposes a statistical approach to estimating the parameters of the size distribution of clusters in MMNCs. One critical challenge is that the clusters are distributed in a three-dimensional (3D) space, while the observations we have are two-dimensional (2D) cross-section microscopic images of these clusters. In the proposed approach, we first derived the probability distribution of the observed sizes of the 2D cross sections of the clusters and then a maximum likelihood estimation (MLE) method is developed to estimate the 3D cluster size distribution. Computational efficient algorithms are also established to make computational load manageable. The case studies based on simulation and real observed data are conducted, which demonstrates the effectiveness of the proposed approach.

Hybrid approach for Pareto front expansion in heuristics

Heuristic search can be an effective multi-objective optimization tool; however, the required frequent function evaluations can exhaust computational sources. This paper explores using a hybrid approach with statistical interpolation methods to expand optimal solutions obtained by multiple criteria heuristic search. The goal is to significantly increase the number of Pareto optimal solutions while limiting computational effort. The interpolation approaches studied are kriging and general regression neural networks. This paper develops a hybrid methodology combining an interpolator with a heuristic, and examines performance on several non-linear bi-objective example problems. Computational experience shows this approach successfully expands and enriches the Pareto fronts of multi-objective optimization problems.

System development and application of Taylor Kriging metamodeling

Purpose – The purpose of this paper is to develop an application software interpolation system based on Taylor Kriging (TK) metamodeling, and apply the developed software system to addressing some engineering interpolation problems.Design/methodology/approach – TK is a novel Kriging model where Taylor expansion is used to identify the base functions of drift function in Kriging. The paper explains the methodology of TK, illustrates the development of software, and reports the results of two case studies by comparing TK with several regression methods.Findings – TK has the advantage of interpolation accuracy, and the developed Kriging software system is useful and can be conveniently manipulated by users.Practical implications – The developed software system can benefit practical engineering applications that need accurate interpolations under limited observations.Originality/value – This paper develops an application software interpolation system based on a novel TK metamodel, and the practical engineerin...

Statistical Orbit Determination by Global Evolutionary Optimization and Batch Filter

A combined algorithm which has global and local optimization capabilities is applied to a statistical orbit determination problem. The objective is to estimate initial states of a nearearth satellite nonlinear dynamical system, as well as unknown parameters, by using discrete observations. A particle swarm optimizer is the selected global search tool, and it is used in the first phase to obtain the preliminary results over a large searching space. A batch filter which has faster convergence and higher accuracy in local optimization is applied in the second phase to refine the preliminary results. The initial experimental results show that the combined algorithm has the potential to solve a statistical orbit determination problem.