Static VAR compensator using recurrent neural network
In this paper, an internal model control recurrent neural network method is used to control the switching of thyristor-controlled reactor in a static VAR compensator (SVC) system for regulating the voltage. The novel controller scheme contains several feedback loops instead of only a feed-forward lo...
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Springer
2014
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| பகà¯à®¤à®¿à®•à®³à¯: | |
| நிகழà¯à®¨à®¿à®²à¯ˆ அணà¯à®•à®²à¯: | http://eprints.utm.my/62660/ http://eprints.utm.my/62660/ http://eprints.utm.my/62660/ |
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| தொகà¯à®ªà¯à®ªà¯: | In this paper, an internal model control recurrent neural network method is used to control the switching of thyristor-controlled reactor in a static VAR compensator (SVC) system for regulating the voltage. The novel controller scheme contains several feedback loops instead of only a feed-forward loop as in the conventional recurrent neural network (RNN). In the proposed controller model, the RNN identifier creates a sample of the connected system and its output generates a part of inputs for the RNN controller which then sends the control signal to the SVC system. Three types of non-linear conditions are chosen to test the operational capability of the new control system to perform the voltage regulation satisfying the IEEE Std 519-1992. The test cases contain a three-phase fault power system, opening of one of the transmission lines in a double line transmission system and sudden changes in the load demand. Results show that the proposed control model is capable of regulating the voltage of the system in a desired range. |
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