This chapter provides an overview of key principles in wide-area monitoring architectures. Models, applications, and areas of improvement in real-time system monitoring and key research directions in the area of data management are described and highlighted.

In this chapter, promising new approaches for estimating and predicting a multi-variate spatio-temporal process from observational data are introduced. A general mathematical framework within which spatio-temporal analysis of large data sets can be performed is provided. In this approach, spatio-temporal models incorporating the dynamics of only a few temporal modes are developed. The technique can be used to monitor, model, and forecast wide-area physical processes such as inter-area oscillations and can reveal key features of a system.

Power system data are multiscale and multivariate in nature. The increasing availability of wide-area measurement systems capable of producing large amounts of multidimensional data has made the use of multivariate data analysis methods more common place. This chapter examines the feasibility of using multisensor data fusion techniques for monitoring and analyzing power system oscillatory behavior. A common conceptual mathematical framework for integrating multiscale data to improve situational awareness is provided. The framework includes techniques to classify and extract dynamic patterns from multisensor multiscale data. Outlier detection and methods to evaluate the statistical significance of the results obtained from the different methods are also discussed.

Timely and accurate monitoring of system dynamic behavior is essential to improve wide-area situational awareness and system reliability. In the preceding chapter attention was directed to the formulation of analytic methods to assess power system health. This chapter examines the use of near realtime analysis techniques to detect, locate, and characterize power system disturbances and monitor power system oscillatory dynamics. Techniques to detrend and denoise measured power system data are outlined and tested on phasor measurement unit (PMU) data. The critical issues for future research in the area of damage identification are also discussed and tools for realtime visualization and monitoring are also reviewed. Emphasis is placed on the development of multiscale, multivariate data analysis techniques.

An attempt to provide physical insight into the proper orthogonal modes is presented in this appendix. Following Goebel and Epstein, consider a linear system of the form Mq + Kq = 0 where q is the n-dimensional vector of states, and M, K are, respectively, the n-by-n dimensional mass and synchronizing matrices.

This appendix discusses extensions to conventional empirical mode decomposition (EMD) analysis to study oscillatory dynamics.