Adaptive underfrequency load shedding using particle swarm optimization algorithm
Abstract Underfrequency load shedding plays an important role in prevention of the power system blackout. The common load shedding methods are based on measuring the frequency first derivative; therefore, an error in measurement process can highly affect their performance. Also, for proper performance of these load shedding schemes, the exact value of some parameters of power system is needed. Any error in estimation of these parameters reduces the reliability of these load shedding schemes. In this paper, an underfrequency load shedding method based on the forecast minimum frequency of system is proposed. In this method, the samples of the power system frequency are taken after disturbance; then, particle swarm optimization algorithm is used to forecast the minimum frequency based on these samples. To verify the effectiveness of the proposed load shedding scheme, its performance has been compared with a newly suggested method.
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Universidad Nacional Autónoma de México, Instituto de Ciencias Aplicadas y Tecnología
2017
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oai:scielo:S1665-642320170001000542018-11-28Adaptive underfrequency load shedding using particle swarm optimization algorithmKetabi,AbbasHajiakbari Fini,Masoud Blackout Minimum frequency Power deficit Underfrequency load shedding Abstract Underfrequency load shedding plays an important role in prevention of the power system blackout. The common load shedding methods are based on measuring the frequency first derivative; therefore, an error in measurement process can highly affect their performance. Also, for proper performance of these load shedding schemes, the exact value of some parameters of power system is needed. Any error in estimation of these parameters reduces the reliability of these load shedding schemes. In this paper, an underfrequency load shedding method based on the forecast minimum frequency of system is proposed. In this method, the samples of the power system frequency are taken after disturbance; then, particle swarm optimization algorithm is used to forecast the minimum frequency based on these samples. To verify the effectiveness of the proposed load shedding scheme, its performance has been compared with a newly suggested method.info:eu-repo/semantics/openAccessUniversidad Nacional Autónoma de México, Instituto de Ciencias Aplicadas y TecnologíaJournal of applied research and technology v.15 n.1 20172017-01-01info:eu-repo/semantics/articletext/htmlhttp://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S1665-64232017000100054en10.1016/j.jart.2016.12.003 |
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Ketabi,Abbas Hajiakbari Fini,Masoud |
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Ketabi,Abbas Hajiakbari Fini,Masoud Adaptive underfrequency load shedding using particle swarm optimization algorithm |
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Ketabi,Abbas Hajiakbari Fini,Masoud |
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Ketabi,Abbas |
| title |
Adaptive underfrequency load shedding using particle swarm optimization algorithm |
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Adaptive underfrequency load shedding using particle swarm optimization algorithm |
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Adaptive underfrequency load shedding using particle swarm optimization algorithm |
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Adaptive underfrequency load shedding using particle swarm optimization algorithm |
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Adaptive underfrequency load shedding using particle swarm optimization algorithm |
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adaptive underfrequency load shedding using particle swarm optimization algorithm |
| description |
Abstract Underfrequency load shedding plays an important role in prevention of the power system blackout. The common load shedding methods are based on measuring the frequency first derivative; therefore, an error in measurement process can highly affect their performance. Also, for proper performance of these load shedding schemes, the exact value of some parameters of power system is needed. Any error in estimation of these parameters reduces the reliability of these load shedding schemes. In this paper, an underfrequency load shedding method based on the forecast minimum frequency of system is proposed. In this method, the samples of the power system frequency are taken after disturbance; then, particle swarm optimization algorithm is used to forecast the minimum frequency based on these samples. To verify the effectiveness of the proposed load shedding scheme, its performance has been compared with a newly suggested method. |
| publisher |
Universidad Nacional Autónoma de México, Instituto de Ciencias Aplicadas y Tecnología |
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2017 |
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http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S1665-64232017000100054 |
| work_keys_str_mv |
AT ketabiabbas adaptiveunderfrequencyloadsheddingusingparticleswarmoptimizationalgorithm AT hajiakbarifinimasoud adaptiveunderfrequencyloadsheddingusingparticleswarmoptimizationalgorithm |
| _version_ |
1756227690687365120 |