This paper intends to present an approach to classify different types of faults and to identify the location of the faults in a non-radial power system network using ElectroMagnetic Transients Program(ATP/EMTP)software and Artificial Neural Network(ANN).Firstly, a balanced threephase system is designed with a RLC load and then different types of faults(single line to ground fault, line to line fault, double line to ground fault and three phase fault) are created at various points of the transmission line. The transmission line model is simulated using the EMTP software. The resulting current waveforms under different fault conditions are observed from the sending end. Fault occurs at the point where peaks appear in the current signal when viewed from the sending end. These current waveforms are analyzed using the wavelet toolbox in MATLAB and the entropy values of the current signals, so obtained, are given as input to the artificial neural network for automatic fault classification and identification of the faulty line. This scheme is also tested under different types of faults with different fault resistances and varying fault locations and the results show that it is able to discriminate the faults and identify the fault locations rapidly and correctly.

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Use of 2‐rate controllers for robust control via zero‐placement is a topic of interest in current literature. This paper proposes a 2‐degree‐of‐freedom, 2‐rate, dynamic, output feedback controller to compensate single‐input single‐output (SISO), unstable, non‐minimum phase (NMP), discrete, linear time‐invariant (LTI) plants of relative degree unity; and achieves exactly the same loop and input‐output properties as can be achieved by a linear quadratic regulator (LQR) for the equivalent lifted LTI plant using state feedback. Such LQR‐like loop and input‐output properties, it may be noted, cannot be attained by LTI or even the existing 2‐rate output feedback controllers for plants with NMP zeros. Examples presented illustrate the efficacy of the proposed controller with respect to the existing ones.

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