Measurement based method for online characterization of generator dynamic behaviour in systems with renewable generation

Panagiotis N. Papadopoulos, Theofilos A. Papadopoulos, Andreas I. Chrysochos, Jovica V. Milanović

Research output: Contribution to journalArticle

1 Citation (Scopus)
18 Downloads (Pure)

Abstract

This paper introduces a hybrid-methodology for online identification and clustering of generator oscillatory behavior, based on measured responses. The dominant modes in generator measured responses are initially identified using a mode identification technique and then introduced, in the next step, as input into a clustering algorithm. Critical groups of generators that exhibit poorly or negatively damped oscillations are identified, in order to enable corrective control actions and stabilize the system. The uncertainties associated with operation of modern power systems, including Renewable Energy Sources (RES) are investigated, with emphasis on the impact of the dynamic behavior of power electronic interfaced RES.
Original languageEnglish
Number of pages10
JournalIEEE Transactions on Power Systems
Early online date27 Apr 2018
DOIs
Publication statusE-pub ahead of print - 27 Apr 2018

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Power electronics
Clustering algorithms
Uncertainty

Keywords

  • clustering
  • online dynamic security assessment
  • renewable generation
  • unsupervised maching learning

Cite this

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title = "Measurement based method for online characterization of generator dynamic behaviour in systems with renewable generation",
abstract = "This paper introduces a hybrid-methodology for online identification and clustering of generator oscillatory behavior, based on measured responses. The dominant modes in generator measured responses are initially identified using a mode identification technique and then introduced, in the next step, as input into a clustering algorithm. Critical groups of generators that exhibit poorly or negatively damped oscillations are identified, in order to enable corrective control actions and stabilize the system. The uncertainties associated with operation of modern power systems, including Renewable Energy Sources (RES) are investigated, with emphasis on the impact of the dynamic behavior of power electronic interfaced RES.",
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Measurement based method for online characterization of generator dynamic behaviour in systems with renewable generation. / Papadopoulos, Panagiotis N.; Papadopoulos, Theofilos A.; Chrysochos, Andreas I.; Milanović, Jovica V.

In: IEEE Transactions on Power Systems, 27.04.2018.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Measurement based method for online characterization of generator dynamic behaviour in systems with renewable generation

AU - Papadopoulos, Panagiotis N.

AU - Papadopoulos, Theofilos A.

AU - Chrysochos, Andreas I.

AU - Milanović, Jovica V.

N1 - © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

PY - 2018/4/27

Y1 - 2018/4/27

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AB - This paper introduces a hybrid-methodology for online identification and clustering of generator oscillatory behavior, based on measured responses. The dominant modes in generator measured responses are initially identified using a mode identification technique and then introduced, in the next step, as input into a clustering algorithm. Critical groups of generators that exhibit poorly or negatively damped oscillations are identified, in order to enable corrective control actions and stabilize the system. The uncertainties associated with operation of modern power systems, including Renewable Energy Sources (RES) are investigated, with emphasis on the impact of the dynamic behavior of power electronic interfaced RES.

KW - clustering

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KW - renewable generation

KW - unsupervised maching learning

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JO - IEEE Transactions on Power Systems

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