Improvement of static VAR compensator using PID and recurrent neural network

In this paper, we comparison between PID and recurrent neural network in three strategy: first)controller svc with RNN , second)controller svc with PID ,third) svc without controller, 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

Improvement of static VAR compensator using PID and recurrent neural network Keywords:

Internal model control , Recurrent neural network , Static VAR compensator , Voltage regulation , Power system , PID controller