Absrraer-The crucial factor affecting the modern power systems today is load flow control. The Unified Power Flow Controller (UPFC) forms an affective means for controlling the power flow. The UPFC consists of shunt and series inverters which are conventionally controlled using linear controllers. This paper presents the design of neuroidentifiers that identify the system parameters that determine the UPFC controller outputs one-time step ahead thus, making the pathway for the design of adaptive neurocontrollers. Two neuroidentifiers are used for identifying the nonlinear dynamics of power system and UPFC, one neuroidentifier for the shunt inverter and the other for the series inverter. Simulation results carried out in the PSCADlEMTDC environment are presented to show the successful neuroidentification of system parameters are possible.
Abstract-Increasing demand coupled with limitations on new construction indicate that existing power transmission must be better controlled in order to continue reliable operation. Recent advances in FACTS devices provide a mechanism to better control power flow on the transmission network. One particular device, the Unified Power Flow Controller (UPFC), holds the most promise for maintaining operation even when the system has suffered partial failure (either naturally occurring, due to human error, or a malicious attack). In addition to the capital cost, the primary obstacles to widespread UPFC use are the combined problems of selecting the most cost effective locations for installation and maintaining proper control of them once installed.In this paper we list evidence that Gradient Descent search based on load-flow computation is more realistic and accurate than many of the optimization techniques currently in use. We then demonstrate that Gradient Descent search can be used to select control points that improve system fault tolerance more than those found by the Max-Flow technique. In addition, we demonstrate that the size of the system being computed and the number of computations is bounded and is practical for real time control.
The crucial factor affecting the modern power systems today is loadflow control. The Unified Power Flow Controller is an effective means for controlling the power flow. The UPFC is controlled conventionally using PI Controllers. This paper presents the designs of neuroidentiJiers that models the system dynamics one-time step ahead making the pathway for the design of adaptive neurocontrollers. Two neuroidentiJiers are used for identifjling the nonlinear dynamics of a multimachine power system and UPFC, one neuroidentifer for the shunt inverter and another for the series inverter. Simulation results carried out in the PSCAD/EMTDC environments on multimachine power system are presented to show the successful neuroidentifcation of system dynamics.
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