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Abstract-The objective of this paper is to design a measurement based self-tuning controller which does not rely on accurate models and deals with nonlinearities in system response. A special form of neural network (NN) model called as feedback linearizable neural network (FLNN) compatible with feedback linearization technique is proposed for representation of nonlinear power systems behaviour. Levenberg−Marquardt (LM) is applied in batch mode to improve the model estimation. A time varying feedback linearization controller (FBLC) is employed in conjunction with the FLNN−LM estimator to generate the control signal. Validation of the performance of proposed algorithm is done through the modeling and simulating both normal and heavy loading of transmission lines, when the nonlinearities are pronounced. Case studies on a large scale 16−machine, 5−area power system are reported for different power flow scenarios, to prove the superiority of proposed scheme against a conventional model based controller. A coefficient vector Λ for FBLC is derived and utilized online at each time instant, to enhance the damping performance of controller.

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Abstract-Levenberg-Marquardt (LM) algorithm, a powerful off-line batch training method for neural networks, is adapted here for online estimation of power system dynamic behavior. A special form of non-linear neural network compatible with the feedback linearization framework is used to enable nonlinear self-tuning control. Use of LM is shown to yield better closed-loop performance compared to conventional recursive least square (RLS) approach. For successive disturbance use of LM in conjunction with non-linear neural network structure yields consistent convergence compared to RLS obviating the need for parameter reset in steady state. A case study on a test system demonstrates the effectiveness of the online LM method for both linear and nonlinear estimation over RLS estimation (linear).

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