Determination of generators coherent groups based on synchrophasors using bioinformatics toolbox

Hashiesh, Fahd; Mostafa, Hossam E.; Helal, Ibrahim; Mansour, M.M.

doi:10.1109/isgt-mideast.2011.6220819

Cited by 4 publications

(5 citation statements)

References 14 publications

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“…11(b). 15,16,19,20,21,22,23,24,33,34, 35,36 1,2,3,4,5,6,7,8,9,10,11,12,13,14,17,18,25,26,27,28,29,30,31,32,3 7,38,39…”

Section: B Case 2: Comparison With Community Detection Methodsmentioning

confidence: 99%

“…To form self-sustained islands, generator coherency and generation/load imbalance need to be considered. In this paper, we treat generator coherency as the constraint and power flow disruption as the minimization objective as shown in (9).…”

Section: Iiian Adaptive Controlled Islanding Frameworkmentioning

confidence: 99%

“…However, it needs a threshold to identify the correct number of coherent groups, which requires expert knowledge and may vary for different operating conditions and fault locations. Bioinformatics clustering technique is suggested in [9] to determine the coherent groups of generators; however, the number of clusters should be specified which may result in unrealistic grouping if recommended number of clusters is improper. Ariff et al presented an approach based on independent component analysis and considered 20 sec time window data of generator speeds and bus voltage angles to have reasonable and practical grouping [10].…”

Section: Iintroductionmentioning

confidence: 99%

See 2 more Smart Citations

Online coherence identification using dynamic time warping for controlled islanding

Banna

Shi

et al. 2018

J. Mod. Power Syst. Clean Energy

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Controlled islanding is considered to be the last countermeasure to prevent system-wide blackouts in case of cascading failures. It splits the system into self-sustained islands to maintain transient stability at the expense of possible loss of load. Generator coherence identification is critical to controlled islanding scheme as it helps identify the optimal cutset to maintain system transient stability. This paper presents a novel approach for online generator coherency identification using phasor measurement unit (PMU) data and dynamic time warping (DTW). Results from the coherence identification are used to further cluster non-generator buses using spectral clustering with the objective of minimizing power flow disruption. The proposed approach is validated and compared to existing methods on the IEEE 39-bus system, through which its advantages are demonstrated.

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“…11(b). 15,16,19,20,21,22,23,24,33,34, 35,36 1,2,3,4,5,6,7,8,9,10,11,12,13,14,17,18,25,26,27,28,29,30,31,32,3 7,38,39…”

Section: B Case 2: Comparison With Community Detection Methodsmentioning

confidence: 99%

Section: Iiian Adaptive Controlled Islanding Frameworkmentioning

confidence: 99%

Section: Iintroductionmentioning

confidence: 99%

See 1 more Smart Citation

Online coherence identification using dynamic time warping for controlled islanding

Banna

Shi

et al. 2018

J. Mod. Power Syst. Clean Energy

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show abstract

“…The methods in [11] and [12] used bioinformatics clustering technique and K-means clustering technique respectively, for the identification of coherent groups. However, both methods require a pre-specification of the number of clusters, which does not make them adaptive.…”

Section: Introductionmentioning

confidence: 99%

A Robust Scheme for Coherency Detection in Power Systems

Adom-Bamfi¹,

Frimpong²

2021

JNTE

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This paper presents an approach for online generator coherency identification based on windowed dynamic time warping (DTW). Generator rotor speed deviations measured by phasor measurement units (PMUs) are used as input data to compute a DTW dissimilarity matrix. Using the dissimilarity matrix together with Agglomerative Hierarchical Clustering (AHC) and Hubert-Levin index (C-index), generators are optimally grouped into coherent clusters. In addition to the clustering of generators, an index for characterizing the transmission delay of a Wide Area Measurement System (WAMS) is presented. A data delay factor that can indicate whether there is an inconsistent PMU data transmission delay is also proposed. The coherency identification technique and indices were tested using simulations carried out on the IEEE 39-bus system. The test results indicate that the proposed scheme accurately clusters generators into coherent groups. The suggested indices were also found to be valid.

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“…The ratio of accelerated kinetic energy is proposed in [8] to the identification of coherent generators. In [9], a Bioinformatics clustering technique is introduced for identifying the generator coherent groups based on information supplied by PMUs, within a certain window of time, after system contingencies. The Bioinformatics toolbox should be fed with the suggested number of clusters, hence results could be inexact if the suggested number of cluster is improper.…”

Section: Introductionmentioning

confidence: 99%

Real time identification of coherent groups for controlled islanding based on graph theory

Gómez¹,

Ríos²

2015

IET Generation, Transmission & Distribution

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Determination of generators coherent groups based on synchrophasors using bioinformatics toolbox

Cited by 4 publications

References 14 publications

Online coherence identification using dynamic time warping for controlled islanding

Online coherence identification using dynamic time warping for controlled islanding

A Robust Scheme for Coherency Detection in Power Systems

Real time identification of coherent groups for controlled islanding based on graph theory

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