Power System Critical Clearing Time Prediction using Extreme Learning Machine
碩士 === 義守大學 === 電機工程學系 === 103 === This thesis uses extreme learning machine (ELM) to predict critical clearing time (CCT). CCT is a measurement for measuring power system transient stability. A larger CCT suggests this power system stability is stronger. However, it wastes a lot of time to obtain C...
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| Format: | Others |
| Language: | zh-TW |
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2015
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| Online Access: | http://ndltd.ncl.edu.tw/handle/91148506216809252583 |
| Summary: | 碩士 === 義守大學 === 電機工程學系 === 103 === This thesis uses extreme learning machine (ELM) to predict critical clearing time (CCT). CCT is a measurement for measuring power system transient stability. A larger CCT suggests this power system stability is stronger. However, it wastes a lot of time to obtain CCT by using the conventional time-domain method. In order to accelerate the CCT computation, many researchers have considered the usage of neural networks in the past three decades. Recently, ELM is a refined product of neural networks in less than ten years. It is the offspring of single layer feedforward network. It is very fast because of using least square method but not iterative gradient method. Therefore the calculating speed can be very fast. This thesis studies the issue of using ELM to find CCT. An example of a six-bus three-machine power system is studied in this thesis. The results show that CCT computation by ELM is fast and fairly accurate.
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