Application of Time-Synchronized Measurements in Power by Mladen Kezunovic, Sakis Meliopoulos, Vaithianathan PDF

By Mladen Kezunovic, Sakis Meliopoulos, Vaithianathan Venkatasubramanian, Vijay Vittal (auth.)

ISBN-10: 3319062174

ISBN-13: 9783319062174

ISBN-10: 3319062182

ISBN-13: 9783319062181

This e-book illuminates how synchrophasors in attaining the tracking, safety and keep an eye on optimizations essential to extend present strength platforms to help expanding quantities of renewable and disbursed strength assets. The authors describe synchrophasor innovations that may offer operators with larger solution in taking pictures dynamic habit of the facility grid. The ensuing insights help greater real-time choice making within the face of extra new release and cargo uncertainty, in addition to interruptions attributable to random acts of nature and malicious assaults. Armed with the data during this state-of-the-art source, grid planners and operators could make optimized, versatile, resilient strength platforms a reality.

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Extra resources for Application of Time-Synchronized Measurements in Power System Transmission Networks

Example text

Compute the state estimate xˆ in the weighted least squares sense. m m i =1 i =1 Step 2. Compute the value ζ = ∑ si2 ( xˆ ) = ∑ ( hi ( xˆ ) − bi σi 2 ). Step 3. “Read” the probability Pr  χ 2 ≤ ζ  = Pr (ζ , v ) from the chi-square probability distribution function. Step 4. 0 − Pr (ζ , v ). Above probability is the probability that measurements fit the model of the system. This can be also expressed as a confidence level. , data that do not fit the model. , low value indicates presence of bad data.

The results are presented in terms of the magnitude and phase residuals. The displayed residuals are amplified by a factor of 50; the actual maximum values of the residuals are also given for reference. 16 the residuals for phase A are relatively very small. 18. It is clear that without actual phase B and phase C measurements, the residuals for these phases are relatively large. The magnitude depends on the system imbalance. This system has relatively small imbalance, but yet the results clearly show a deterioration of the state estimator performance for the unmeasured phases.

The state estimation is solved using the weighted least squares approach. 12) where η = z − h ( x, y ) and W is a diagonal matrix, whose nonzero entries are equal to the inverse of the variance of the measurement errors:   1 W = diag  2  . 14) where x refers to the best estimate of the state vector, and H is the Jacobian matrix of the measurement equations. , it becomes a LSE. 7 Performance Metrics The accuracy of the state estimate is quantified via the standard chi-square test and the statistical properties of the state estimator.

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Application of Time-Synchronized Measurements in Power System Transmission Networks by Mladen Kezunovic, Sakis Meliopoulos, Vaithianathan Venkatasubramanian, Vijay Vittal (auth.)


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