Harmonic estimation for a signal distorted with additive noise has been an area of interest for researchers in many disciplines of science and engineering. This work presents a new algorithm based on the foraging behavior of E. coli bacteria in our intestine to estimate the harmonic components present in power system voltage/current waveforms. The basic foraging strategy is made adaptive, through a Takagi-Sugeno fuzzy scheme, depending on the operating condition to make the convergence faster. Besides, the harmonic estimation is linear in amplitude and nonlinear in phase. As the proposed algorithm does not rely on Newton-like gradient descent methods, this is used for phase estimation whereas the linear least square scheme estimates the amplitude, thereby presenting the hybrid method. The improvement in %error, as well as the processing time compared with the conventional discrete Fourier transform and genetic algorithm method is demonstrated in this paper. Besides, the performance is quite acceptable even in the presence of decaying dc component as well as to change in amplitude and phase angle of harmonic components.

A hybrid least square-fuzzy bacterial foraging strategy for harmonic estimation

The paper presents a new approach for the estimation of harmonic components of a power system using a linear adaptive neuron called Adaline. The learning parameters in the proposed neural estimation algorithm are adjusted to force the error between the actual and desired outputs to satisfy a stable difference error equation. The estimator tracks the Fourier coefficients of the signal data corrupted with noise and decaying DC components very accurately. Adaptive tracking of harmonic components of a power system can easily be done using this algorithm. Several numerical tests have been conducted for the adaptive estimation of harmonic components of power system signals mixed with noise and decaying DC components.

An adaptive linear combiner for on-line tracking of power system harmonics

Harmonics estimation in emerging power system: Key issues and challenges

A feedforward neural network (FFNN) has been trained to discriminate between power transformer magnetizing inrush and fault currents The training algorithm used was backpropagation, assuming initially a sigmoid transfer function for the network's processing units ("neurons") Once the network was trained the units' transfer function was changed to hard limiters with thresholds equal to the biases obtained for the sigmoids during training The off-line experimental results presented in this paper show that a FFNN may be considered as an alternative method to make the discrimination between inrush and fault currents in a digital relay implementation >

Training an artificial neural network to discriminate between magnetizing inrush and internal faults

In this work, an artificial neural network based algorithm for a three phase power transformer protection scheme is developed and implemented in real time using the DS-1102 digital signal processor. Distinguishing between the magnetizing inrush and internal fault currents is always a consideration for power transformer protection. Existing methods, mostly based on harmonic restraint, are not very reliable for modern transformer protection. The reason for loss of reliability is that the use of low-loss amorphous material in modern transformer cores causes reduced second harmonic content of the magnetizing inrush current. Other methods based on the transformer equivalent circuit model are susceptible to parameter variations and hence are not suitable under all operating conditions. The work presented here shows the usefulness of the artificial neural network which is able to distinguish between the magnetizing inrush and the internal fault currents without harmonic decomposition or using the transformer equivalent circuit model. The inherent advantages of the generalization and the pattern recognition characteristics make the artificial neural network based method quite suitable for distinguishing between magnetizing inrush and the internal fault currents. In this work, a two-layer artificial neural network with sixteen inputs and one output is designed. The data to train and test the artificial neural network are experimentally obtained. The artificial neural network is trained with an input data set and subsequently tested with a different data set. The off-line test results show that the artificial neural network is quite capable of distinguishing between the magnetizing inrush and internal fault currents. Finally, the on-line implementation successfully establishes the efficacy of the ANN based algorithm for power transformer protection.

Artificial neural network based protection of power transformers

A novel method for predicting power system voltage harmonics with an artificial neural network is presented. The method is based on the backpropagation learning technique for feedforward neural networks. The promise of the proposed method in harmonics prediction is shown. In order to demonstrate its effectiveness, the proposed method is applied to voltage harmonics observed through a personal computer based measurement system and the performance is compared with that of conventional methods. >

H. Uematsu

Papers

An artificial neural-net based method for predicting power system voltage harmonics