Application of sequential testing problem to online detection of transient stability status for power systems

Jhonny Gonzalez; Yerkin Kitapbayev; Tingyan Guo; Jovica V. Milanovic; Goran Peskir; John Moriarty

doi:10.1109/CDC.2016.7798484

Application of sequential testing problem to online detection of transient stability status for power systems

Jhonny Gonzalez, Yerkin Kitapbayev, Tingyan Guo, Jovica V. Milanovic, Goran Peskir, John Moriarty

Research output: Chapter in Book/Conference proceeding › Conference contribution

Abstract

We address the problem of predicting the transient stability status of a power system as quickly as possible in real time subject to probabilistic risk constraints. The goal is to minimise the average time taken after a fault to make the prediction, and the method is based on ideas from statistical sequential analysis. The proposed approach combines probabilistic neural networks with dynamic programming. Simulation results show an approximately three-fold increase in prediction speed when compared to the use of pre-committed (fixed) prediction times.

Original language	English
Title of host publication	2016 IEEE 55th Conference on Decision and Control (CDC)
DOIs	https://doi.org/10.1109/CDC.2016.7798484
Publication status	Published - 29 Dec 2016
Event	Decision and Control (CDC), 2016 IEEE 55th Conference on - Las Vegas, United States Duration: 12 Dec 2016 → 14 Dec 2016

Conference

Conference	Decision and Control (CDC), 2016 IEEE 55th Conference on
Country/Territory	United States
City	Las Vegas
Period	12/12/16 → 14/12/16

Access to Document

10.1109/CDC.2016.7798484

Cite this

@inproceedings{775377a7291e4582bf405879c31b6eb6,

title = "Application of sequential testing problem to online detection of transient stability status for power systems",

abstract = "We address the problem of predicting the transient stability status of a power system as quickly as possible in real time subject to probabilistic risk constraints. The goal is to minimise the average time taken after a fault to make the prediction, and the method is based on ideas from statistical sequential analysis. The proposed approach combines probabilistic neural networks with dynamic programming. Simulation results show an approximately three-fold increase in prediction speed when compared to the use of pre-committed (fixed) prediction times.",

author = "Jhonny Gonzalez and Yerkin Kitapbayev and Tingyan Guo and Milanovic, {Jovica V.} and Goran Peskir and John Moriarty",

year = "2016",

month = dec,

day = "29",

doi = "10.1109/CDC.2016.7798484",

language = "English",

booktitle = "2016 IEEE 55th Conference on Decision and Control (CDC)",

note = "Decision and Control (CDC), 2016 IEEE 55th Conference on ; Conference date: 12-12-2016 Through 14-12-2016",

}

Gonzalez, J, Kitapbayev, Y, Guo, T, Milanovic, JV , Peskir, G & Moriarty, J 2016, Application of sequential testing problem to online detection of transient stability status for power systems. in 2016 IEEE 55th Conference on Decision and Control (CDC). Decision and Control (CDC), 2016 IEEE 55th Conference on, Las Vegas, Nevada, United States, 12/12/16. https://doi.org/10.1109/CDC.2016.7798484

TY - GEN

T1 - Application of sequential testing problem to online detection of transient stability status for power systems

AU - Gonzalez, Jhonny

AU - Kitapbayev, Yerkin

AU - Guo, Tingyan

AU - Milanovic, Jovica V.

AU - Peskir, Goran

AU - Moriarty, John

PY - 2016/12/29

Y1 - 2016/12/29

N2 - We address the problem of predicting the transient stability status of a power system as quickly as possible in real time subject to probabilistic risk constraints. The goal is to minimise the average time taken after a fault to make the prediction, and the method is based on ideas from statistical sequential analysis. The proposed approach combines probabilistic neural networks with dynamic programming. Simulation results show an approximately three-fold increase in prediction speed when compared to the use of pre-committed (fixed) prediction times.

AB - We address the problem of predicting the transient stability status of a power system as quickly as possible in real time subject to probabilistic risk constraints. The goal is to minimise the average time taken after a fault to make the prediction, and the method is based on ideas from statistical sequential analysis. The proposed approach combines probabilistic neural networks with dynamic programming. Simulation results show an approximately three-fold increase in prediction speed when compared to the use of pre-committed (fixed) prediction times.

U2 - 10.1109/CDC.2016.7798484

DO - 10.1109/CDC.2016.7798484

M3 - Conference contribution

BT - 2016 IEEE 55th Conference on Decision and Control (CDC)

T2 - Decision and Control (CDC), 2016 IEEE 55th Conference on

Y2 - 12 December 2016 through 14 December 2016

ER -

Application of sequential testing problem to online detection of transient stability status for power systems

Abstract

Conference

Access to Document

Fingerprint

Cite this