Wide Area Monitoring of Interconnected Power Systems by Arturo Roman Messina
By Arturo Roman Messina
Wide zone tracking of Interconnected strength Systems bargains with the advance and alertness of latest analytical strategies, in line with complex sign processing equipment and multi-scale, multi-temporal research instruments, and the research, tracking and keep an eye on of wide-area phenomena in huge interconnected strength structures. it's the first complete, systematic account of complicated well-being tracking and keep watch over structures, and close to real-time energy process research and defense tracking. moreover, it comprises equipment for superior visualization of process stipulations, optimum facts fusion, stronger forecasting and modeling of process behavior.
This e-book is meant for complex undergraduate and graduate classes in electric engineering and scholars on clever grid courses, in addition to for researchers, software engineers, and complicated educating within the fields of utilized keep watch over and tool engineering. the topic has huge purposes in organic and clinical (health tracking) sciences, and engineering (structural healthiness overview, forecasting and prediction, sensors) and should be of curiosity to these within the box.
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Additional resources for Wide Area Monitoring of Interconnected Power Systems
6 . 4 jm 3 7 7 7 7 7 7 5 lm with FTF ¼ I, where m denotes the number of measurement locations or sensors. 10) where use has made of the property FTF ¼ I. 12) and 32 Wide-area monitoring of interconnected power systems where A is an m-by-N matrix of time-dependent coefficients of the form 2 3 2 3 a1 ðtÞ a11 ðtÞ a12 ðtÞ Á Á Á a1N ðtÞ 6 a ðtÞ 7 6 a ðtÞ a ðtÞ Á Á Á a ðtÞ 7 22 2N 6 2 7 6 21 7 7 6 AðtÞ ¼ 6 .. .. 7 6 .. 7 ¼ 6 .. 7 4 . 5 4 . . 13) 6 .. 7 4 . 5 am ðtÞ The following points should be noted: 1.
Dynamic information can be extracted on raw data or processed data using data fusion techniques as discussed in the foregoing chapters. 3 Spatio-temporal modeling of multivariate processes Most practical process data contain contributions at multiple scales in time and frequency . A key objective is to decompose such data into uncorrelated modes of variation and, specifically, in isolating these modes for further analysis, prognosis, and prediction [10–14]. 2) j¼1 with p < N, where a1, a2, .
15). The group centroid of the time-varying amplitudes can then be computed as aðtÞ ¼ n 1X aj ðtÞ n j¼1 where n can represent a subset of dynamic trajectories or be equal to the model dimensionality. A similar interpretation is also possible for the diffusion coordinates. In practice, the dimensionality of the embeddings can be determined from the spectral gap of eigenvalues or using energy criteria as discussed in the numerical implementation of the procedure. Spatio-temporal modeling of power system dynamic processes 47 Conceptually, the diffusion process transforms the time series from multiple measurement points into a single time series, preserving as much of the relevant information as possible during the dimensionality reduction.