Blackout prediction in interconnected electric energy systems considering generation re-dispatch and energy curtailment

Sadegh Kamali and Turaj Amraee

Applied Energy, 2017, vol. 187, issue C, 50-61

Abstract: Blackouts or cascading outages are costly events that threaten the integrity of electric energy systems around the world. Controlled splitting is executed as the last countermeasure to reduce the undesired economic and social consequences of a blackout. In this paper, a new two-stage scheme is proposed to predict the risk of a blackout in electric energy systems. In the first stage, the boundaries of electric islands are determined using a Mixed Integer Non-Linear Programming model that minimizes the cost of generation re-dispatch and load curtailment. In the second step, a data-mining technique is perfected to predict the risk of electrical separation of an electric island from the rest of the network. Each predictor is trained based on the phasor-measurement data taken at the synchronous generator terminals. Using a wide-area measurement system, the required phasor measurements are gathered and processed in the Energy Management System. Various scenarios, including the island and non-island conditions, are generated and then utilized by the decision-tree classification technique to predict the risk of a blackout. The proposed algorithm is simulated over the IEEE 39-bus test system to demonstrate its performance in online applications.

Keywords: Blackout; Islanding; Prediction; Data mining; Load curtailment; Generation re-dispatch (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261916316257
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:187:y:2017:i:c:p:50-61

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2016.11.040

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().