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Hierarchical Clustering based optimal PMU placement for power system fault observability

Optimal number and location of phasor measurement units (PMUs) in the power system networks faces challenges for achieving the full network observability during fault conditions. Achieving fault observability approach requires more constraints than normal system observability and consequently suffer...

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Detalles Bibliográficos
Autores principales: Eissa, Moustafa, Kassem, Amr
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6088460/
https://www.ncbi.nlm.nih.gov/pubmed/30109277
http://dx.doi.org/10.1016/j.heliyon.2018.e00725
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author Eissa, Moustafa
Kassem, Amr
author_facet Eissa, Moustafa
Kassem, Amr
author_sort Eissa, Moustafa
collection PubMed
description Optimal number and location of phasor measurement units (PMUs) in the power system networks faces challenges for achieving the full network observability during fault conditions. Achieving fault observability approach requires more constraints than normal system observability and consequently suffers from complex analysis and heavy computational burden for the large-scale networks. A new algorithm for determining the optimal PMU placement considering the network fault observability is introduced. The proposed algorithm is achieved through four stages. The first stage is achieved through the network fault simulation to obtain the post fault change in voltage (ΔV) at each bus. Then, the post fault change in voltage (ΔV) is used to build the network connectivity matrix (CM) and forming a new developed Faulted Connectivity Matrix (FCM) that describes the power system topology during the fault conditions. The correlation between the buses is obtained, in the second stage, by applying Pearson correlation coefficient. Hierarchical Clustering technique is given, in the third stage, to cluster the network into coherent zones to find the most correlated buses. Finally, the optimal location of the PMUs is identified within each zone based on simple proposed placement rules. The proposed algorithm is tested under a variety of fault events applied on different standard test systems. The results show the simplicity and the effectiveness of the proposed algorithm.
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spelling pubmed-60884602018-08-14 Hierarchical Clustering based optimal PMU placement for power system fault observability Eissa, Moustafa Kassem, Amr Heliyon Article Optimal number and location of phasor measurement units (PMUs) in the power system networks faces challenges for achieving the full network observability during fault conditions. Achieving fault observability approach requires more constraints than normal system observability and consequently suffers from complex analysis and heavy computational burden for the large-scale networks. A new algorithm for determining the optimal PMU placement considering the network fault observability is introduced. The proposed algorithm is achieved through four stages. The first stage is achieved through the network fault simulation to obtain the post fault change in voltage (ΔV) at each bus. Then, the post fault change in voltage (ΔV) is used to build the network connectivity matrix (CM) and forming a new developed Faulted Connectivity Matrix (FCM) that describes the power system topology during the fault conditions. The correlation between the buses is obtained, in the second stage, by applying Pearson correlation coefficient. Hierarchical Clustering technique is given, in the third stage, to cluster the network into coherent zones to find the most correlated buses. Finally, the optimal location of the PMUs is identified within each zone based on simple proposed placement rules. The proposed algorithm is tested under a variety of fault events applied on different standard test systems. The results show the simplicity and the effectiveness of the proposed algorithm. Elsevier 2018-08-08 /pmc/articles/PMC6088460/ /pubmed/30109277 http://dx.doi.org/10.1016/j.heliyon.2018.e00725 Text en © 2018 The Authors. Published by Elsevier Ltd. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Eissa, Moustafa
Kassem, Amr
Hierarchical Clustering based optimal PMU placement for power system fault observability
title Hierarchical Clustering based optimal PMU placement for power system fault observability
title_full Hierarchical Clustering based optimal PMU placement for power system fault observability
title_fullStr Hierarchical Clustering based optimal PMU placement for power system fault observability
title_full_unstemmed Hierarchical Clustering based optimal PMU placement for power system fault observability
title_short Hierarchical Clustering based optimal PMU placement for power system fault observability
title_sort hierarchical clustering based optimal pmu placement for power system fault observability
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6088460/
https://www.ncbi.nlm.nih.gov/pubmed/30109277
http://dx.doi.org/10.1016/j.heliyon.2018.e00725
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