Improvement of static VAR compensator using PID and recurrent neural network
Paper ID : 1151-ICTCK (R2)
1حمید نشاط قالیباف *, 2علی اصغر شجاعی
1دانشگاه ازاد نیشابور
2دانشگاه ازاد
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.
Internal model control . neural network Static VAR compensator Voltage regulation . Power system . PID controller
Status : Paper Accepted