Journal of Advanced Materials in Engineering (Esteghlal)

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In this paper, radar detection based on Monte Carlo sampling is studied. Two detectors based on Importance Sampling are presented. In these detectors, called Particle Detector, the approximated likelihood ratio is calculated by Monte Carlo sampling. In the first detector, the unknown parameters are first estimated and are substituted in the likelihood ratio (like the GLRT method). In the second detector, the averaged likelihood ratio is calculated by integrating out the unknown parameters (like the AALR method). Thanks to the numerical nature of these methods, they can be applied to many detection problems which do not have analytical solutions. Simulation results show that both the proposed detectors and the GLRT have approximately the same performance in problems to which the GLRT can be applied. On the other hand, the proposed detectors can be used in many cases for which either no ML estimate of unknown parameters exists or their prior distribution is known.

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All algorithms for impedance calculation use an analog-to-digital converter. The high accuracy of the impedance seen by a distance relay is an important factor in the correct isolation of the faulty part of power systems. To achieve this, a novel technique based on third order interpolation is used in this paper. According to this technique, the times and the values of the obtained samples are changed to real ones. To evaluate the new technique, it is applied to six digital distance algorithms, namely, Discrete Fourier Transform (DFT), Half-cycle Discrete Fourier Transform, Least Square, Mann-Morrison, Least Square with Delete Dc, and Prodar70. The technique is found to be capable of accurately computing the impedance in the algorithms mentioned. Comparisons are made among the results to show the efficiency of the new technique for decreasing errors in all algorithms.