ABSTRACT
Approaches to multidimensional data visualization are of ongoing interest in the analysis and characterization of observational data. Data visualization can be used to explore or interpret data or clusters, detect dynamic patterns and facilitate support decision making. When integrated with situational awareness systems visualization techniques can aid in the comprehension of complex system behavior. In this chapter, techniques for visualization of multidimensional data are examined in the context of their application to power system time series. The use of adaptive, data processing techniques to provide automatic early detection of system deterioration in large power systems is investigated. Areas of improvement in near real-time system monitoring are identified and algorithms to detect both oscillatory and abrupt changes in system behavior are proposed and tested. The practical application of these techniques is tested on time-synchronized phasor measurements collected by phasor measurement units (PMUs). Numerical simulations computed using time-energy nonstationary methods are critically compared with conventional approaches.
