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.

An examination of the dynamic validities of cognitive ability and personality measures with performance

Prior research shows that both cognitive ability (Schmidt & Hunter, 1998) and personality measures (Poropat, 2009; Hough & Furnham, 2003) are valid predictors of job performance. The dynamic nature of the relationships between cognitive ability and personality measures with performance over time spent on the job is less understood and thus this paper explores their relationships. Although there is much research to suggest that the predictive relationship between cognitive ability and performance decreases over years of tenure (e.g., Hulin, Henry, & Noon, 1990), other research suggests that the relationship between cognitive ability and performance will increase over time (Kolz, McFarland, & Silverman, 1988). In regard to personality, this study provides a critical test of two competing theories. The first position holds that the validity of personality degrades over time. Support for this position comes from the “ubiquitous” nature of the simplex pattern in individual differences (Humphreys, 1985). It follows that personality validities should perform like cognitive ability in this respect, and thus decline over time. In contrast to this viewpoint, the alternative position contends that the predictive relationship between personality variables and performance increases over time, with the correlation becoming larger in magnitude and more positive in direction over years of tenure. The results of this study support the latter position; personality validities predicted long term performance outcomes.