Computational intelligence models for PIV based particle (cuttings) direction and velocity estimation in multi-phase flows

In multi-phase flow, the gas phase, the liquid phase and the particles (cuttings) within the liquid have different flow behaviors. Particle velocity and particle direction are two of the important aspects for determining the drilling particle behavior in multi-phase flows. There exists a lack of information about particle behavior inside a drilling annular wellbore. This paper presents an approach for particle velocity and direction estimation based on data obtained through Particle Image Velocimetry (PIV) techniques fed into computational intelligence models, in particular Artificial Neural Networks (ANNs) and Support Vector Machines (SVM). In this work, feed forward neural networks, support vector machines, support vector regression, linear regression and nonlinear regression models are used for estimating both particle velocity and particle direction. The proposed system was trained and tested using the experimental data obtained from an eccentric pipe configuration. Experiments have been conducted at the Cuttings Transport and Multi-phase Flow Laboratory of the Department of Petroleum and Natural Gas Engineering at Middle East Technical University. A high speed digital camera was used for recording the flow at the laboratory. Collected experimental data set consisted of 1080 and 1235 data points for 15° inclined wellbores, 1087 and 1552 data points for 30° inclined wellbores and 885 and 1119 data points for horizontal (0°), wellbores respectively to use in estimation and classification problems. Results obtained from computational intelligence models are compared with each other through some performance metrics. The results showed that the SVM model was the best estimator for direction estimation, meanwhile the SVR model was the best estimator for velocity estimation. The direction and speed of the particles were estimated with a reasonable accuracy; hence the proposed model can be used in eccentric pipes in the field. © 2018 Elsevier B.V

Load and PV Generation Forecast Based Cost Optimization for Nanogrids with PV and Battery

53rd International Universities Power Engineering Conference (2018 : United Kingdom)Power system resiliency and robustness became major concerns of the system operators and researchers after the introduction of the smart grid concept. The improvements in the battery storage systems (BSS) and the photovoltaic (PV) systems encourage power systems operators to enable the use of those systems in resiliency and robustness studies. Utilization of those systems not only contributes to the robustness of the power systems but also decrease the operational costs. There are several methods in literature to operate the grid systems with partitions of PV and BSS in the most economical way. Although these methods are straightforward and work fine, they can not guarantee the most economical result on a daily basis. In this paper, deep learning based PV generation and load forecasts are used to improve the results of optimization in terms of economic aspects in nano-grid applications. In the considered system, there are loads, PV generation units, BSS and grid connection. Bi-directional power flow is permitted between the main grid and the nano-grid system. The forecasting methodologies and used optimization algorithms will be explained in this paper. © 2018 IEEE.EDF,IEEE,RTDS Technologies,Scottish and Southern Electricity Networks,TJ - H2