Oil Retention and Pressure Drop in Horizontal and Vertical Suction Lines with R410A / POE ISO 32

In refrigeration systems a small amount of compressor lubricant is entrained in the refrigerant and circulated through the system, where some is retained in each component. The suction line to the compressor has the largest potential for oil retention. This paper presents results from an experimental apparatus that has been constructed to circulate POE (polyolester) oil and R410A at a controlled mass flux, OCR (oil in circulation ratio), and apparent superheat, and to directly measure the pressure drop and mass of oil retained in horizontal and vertical suction lines. The bulk vapor velocity and overall void fraction are determined from direct mass and temperature measurements. The oil retention, pressure drop, and flow regimes near the minimum ASHRAE recommended mass flux condition are explored. It was found that oil retention begins to increase sharply even above the minimum recommended flux, so conditions near the minimum should be avoided. Two relationships were developed to predict the oil retention in the vertical and horizontal suction lines. The average error from the predictions method was 10.9% for the vertical tube, and 7.9% for the horizontal tube.

A Framework for Assessing the Reliability of Wind Energy Conversion Systems

During the last decade, wind power generation has seen rapid development. According to the U.S. Department of Energy, achieving 20\% wind power penetration in the U.S. by 2030 will require: (i) enhancement of the transmission infrastructure, (ii) improvement of reliability and operability of wind systems and (iii) increased U.S. manufacturing capacity of wind generation equipment. This research will concentrate on improvement of reliability and operability of wind energy conversion systems (WECSs). The increased penetration of wind energy into the grid imposes new operating conditions on power systems. This change requires development of an adequate reliability framework. This thesis proposes a framework for assessing WECS reliability in the face of external disturbances, e.g., grid faults and internal component faults. The framework is illustrated using a detailed model of type C WECS - doubly fed induction generator with corresponding deterministic and random variables in a simplified grid model. Fault parameters and performance requirements essential to reliability measurements are included in the simulation. The proposed framework allows a quantitative analysis of WECS designs; analysis of WECS control schemes, e.g., fault ride-through mechanisms; discovery of key parameters that influence overall WECS reliability; and computation of WECS reliability with respect to different grid codes/performance requirements.

Dynamic-Data Driven Real-Time Identification for Electric Power Systems

Power system engineers face a double challenge: to operate electric power systems within narrow stability and security margins, and to maintain high reliability. There is an acute need to better understand the dynamic nature of power systems in order to be prepared for critical situations as they arise. Innovative measurement tools, such as phasor measurement units, can capture not only the slow variation of the voltages and currents but also the underlying oscillations in a power system. Such dynamic data accessibility provides us a strong motivation and a useful tool to explore dynamic-data driven applications in power systems. To fulfill this goal, this dissertation focuses on the following three areas: Developing accurate dynamic load models and updating variable parameters based on the measurement data, applying advanced nonlinear filtering concepts and technologies to real-time identification of power system models, and addressing computational issues by implementing the balanced truncation method. By obtaining more realistic system models, together with timely updated parameters and stochastic influence consideration, we can have an accurate portrait of the ongoing phenomena in an electrical power system. Hence we can further improve state estimation, stability analysis and real-time operation.