Neural Network pada Koordinasi PSS dan TCSC untuk Meningkatkan Kestabilan Sistem Tenaga Terinterkoneksi

Rudy Gianto, Kho Hie Khwee


This paper develops a design procedure for adaptive coordination among power system damping controllers (i.e. power system stabilizers and supplementary damping controller of thyristor-controlled series capacitor) for improving the stability of an interconnected electric power system. The design is based on the use of neural network which identifies the optimal controller parameters online. The inputs to the neural network include the active- and reactive- power of the synchronous generators which represent the power loading on the system, and elements of the reduced nodal impedance matrix for representing the power system configuration. The neural network-based adaptive controller is trained offline with a wide range of credible power system operating conditions and configurations. The controller parameters obtained from the trained neural network are verified by both eigenvalue calculations and time-domain simulations, which confirms that good dampings of the eletromechanical modes and stability are achieved.

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