Abstract:
Inter-area oscillations are one of the major threats to the stability of an interconnected
power system due to the fact that these oscillations involve generators from different
areas of a power system and hence may result in higher oscillation in the tie-line by
adding up effects from each of the participating generators. The success of oscillation
damping within a given duration, such as less than 15 seconds, is substantially corre-
lated with the safe operation of a contemporary power system. Since power systems are
highly non-linear in nature, with time varying parameters, and a specific control design
based on the linearized model may not ensure satisfactory performance under varied op-
erating scenarios. Therefore, a non-linear self-tuning controller is required that damps
inter-area oscillations in interconnected power systems under varied post-disturbance
operating conditions without requiring manual adjustment or re-tuning of controller pa-
rameters. An effective controller for power oscillation damping has been designed in this
research. Moreover, a powerful online batch training method for neural networks known
as the Levenberg-Marquardt (LM) algorithm is modified for computationally efficient
online estimation of power system’s dynamic behaviour and is referred as Computa-
tionally Efficient Levenberg-Marquardt (CELM) algorithm. A unique form of neural
network, referred as Computationally Efficient Neural Network (CENN), is proposed
that is compatible with the Feedback Linearizable Controller (FBLC), is used to ensure
non-linear self-tuning control. It has been demonstrated that using the modified version
of LM algorithm i.e. CELM algorithm for successive disturbance leads to better accu-
racy, faster convergence and offer less computational time than using the classical LM
algorithm.
