Applications of Robust Failure Detection Algorithms to Power Systems

Drake, K.; Campbell, Stephen L.; Andjelkovic, I.; Hannas, B.L.; Sweetingham, K.

doi:10.1109/isap.2005.1599275

Cited by 3 publications

(6 citation statements)

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“…Also, this approach can determine faults that are masked by the control system. The drawback to the model identification approach is that the models for the normal and faulty system must be sufficiently accurate or the algorithm might produce a false alarm [2].…”

Section: Passive Vs Active Fault Detectionmentioning

confidence: 99%

“…Without a bound on the uncertainty, or noise, any output would be possible from either model and perfect model identification would be impossible [2]. Thus, we assume that the noise in model i is bounded in the L 2 norm by the noise measure…”

Section: Minimum Energy Detection Signal Algorithmmentioning

confidence: 99%

“…Thus, the output sets, A i (v), are translates of open sets byȳ i and they are affinely dependent upon the detection signal, v [2]. The goal of the algorithm is to find the minimum proper signal, where a proper signal is one which separates the two output sets.…”

Section: Minimum Energy Detection Signal Algorithmmentioning

confidence: 99%

“…Alternatively if (x 0 , µ 0 , x 1 , µ 1 ) satisfy the models and v is a proper detection signal, but the outputs are still not distinct, then (x 0 , µ 0 , x 1 , µ 1 ) must not satisfy the noise bound (1.13) for i = 0, 1 [2]. Thus, if (x 0 , µ 0 , x 1 , µ 1 ) satisfies the models and v is a proper detection signal, but an output, y, is still in both output sets, it must be that…”

Section: Minimum Energy Detection Signal Algorithmmentioning

confidence: 99%

“…Now, we need to find c new , which is just the x 1 coordinate when x 2 = 0, with c 1 = 1. So, dropping the error term in V , we have V (x 1 , 0) = 1 = 9x2 1 and thus, x 1 = ± 1 3 . Finally we have that x 1 = c new = new =1 3 .…”

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confidence: 99%

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Auxiliary Signal Design for Fault Detection in Nonlinear Systems

Sweetingham¹,

∗²

2009

Control and Intelligent Systems

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SWEETINGHAM, KELLY A. Auxiliary Signal Design for Fault Detection in Nonlinear Systems. (Under the direction of Professor S. L. Campbell). Recently, research has developed in the area of active fault detection and model identification algorithms for linear systems. These algorithms compute an auxiliary input signal which guarantees fault detection, assuming a bounded noise. This dissertation addresses the question of when the previous linear theory can be applied to nonlinear systems. Several case studies are presented to verify that linearizations can in fact produce results in the nonlinear case. Two results are proven about the use of linearizations. The first result gives a parameter β to scale the entire problem. Using this parameter, the scaled auxiliary signal, along with a scaled noise bound, will guarantee fault detection in the nonlinear problem. The second result shows how to compute the acceptable noise bound for the nonlinear problem using the exact auxiliary signal from the linearized problem. Also presented is a computational study to verify these two results. Two secondary projects are also presented in this dissertation. The first is a comparison of two different linear algorithms used to compute the optimal auxiliary signal. The second is a port of one of the existing pieces of fault detection software into Matlab. This new software is included, as well as a discuss of the complications of the port.

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Section: Passive Vs Active Fault Detectionmentioning

confidence: 99%

Section: Minimum Energy Detection Signal Algorithmmentioning

confidence: 99%

Section: Minimum Energy Detection Signal Algorithmmentioning

confidence: 99%

Section: Minimum Energy Detection Signal Algorithmmentioning

confidence: 99%

mentioning

confidence: 99%

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Auxiliary Signal Design for Fault Detection in Nonlinear Systems

Sweetingham¹,

∗²

2009

Control and Intelligent Systems

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DC Protection on the Electric Ship

Hamilton

Schulz

2007

2007 IEEE Electric Ship Technologies Symposium

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Model‐Based Failure Detection on Nonlinear Systems: Theory and Transition

Drake

Campbell

Andjelkovic

et al. 2007

Naval Engineers Journal

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Failure detection is an active area of Navy research due to its many important applications. Recently, an approach for multi-model identification and fault detection in the presence of bounded energy noise over finite time intervals has been introduced. This family of algorithms was originally designed to work on linear systems that can be modeled analytically. In this paper, efforts made toward extending this algorithm for fault detection to nonlinear systems along with efforts in testing this family of algorithms on real systems are discussed.

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Applications of Robust Failure Detection Algorithms to Power Systems

Cited by 3 publications

References 2 publications

Auxiliary Signal Design for Fault Detection in Nonlinear Systems

Auxiliary Signal Design for Fault Detection in Nonlinear Systems

DC Protection on the Electric Ship

Model‐Based Failure Detection on Nonlinear Systems: Theory and Transition

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