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Abstract— In OFDM systems, it is necessary to estimate the channel to overcome the distortion caused by channel fading’s which can be induced by many phenomena such as: delay spread, mobility and Doppler shift. Most of the channel estimation techniques are proposed in frequency domain using the pilot symbols. One of them that is less complicated is least-squares (LS) method which is widely used in channel estimation but it is more sensitive to noise respected to the other reported techniques. In this paper, a new threshold based method using wavelet decomposition will be proposed which is based on an initial LS estimation technique. The reported simulation results show that the proposed method has better performance compared to the other methods such as Lee Method that has been published recently.

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Blind source separation is the technique that anyone can separate the original signals from their mixtures without any knowledge about the mixing process, but using some statistical properties of original source signals. Independent component analysis is a statistical method expressed as a set of multidimensional observations that are combinations of unknown variables which are assumed to be statistically independent with respect to each other. In this paper we will use the nonlinear autcorrelation function as an object function to separate the source signals from the noisy mixing signals. Also we apply the wavelet transform in our proposed algorithm. Maximization of the object function in wavelet domain using the LMS algorithm will be obtained the coefficients of a linear filter which separate the source signals with high SNR. To calculate the performance of the proposed algorithm, two parameters of Performance Index and Signal to Noise and Interference Ratio will be used. To test the proposed algorithm, we will use Inovation Gaussian signals, Speech signals and ECG signals. Finally level of wavelet decomposition effects will be consider on the obtained results. It will be shown that the proposed algorithm gives better results than the other methods such as NoisyNA method that has been proposed by Shi.

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One of the serious concern of grid connected distributed resources is islanding problem. This condition should be prevented due to several reasons concerning such as personnel and equipment safety. This paper investigated a passive islanding detection method using wavelet transforms analysis. The proposed method is based on Rate of change of Frequency (RoCoF) measurements and processing of this signal with analysis of a Symlets wavelets transform. The presented studies are based on time-domain simulations using MATLAB, and its feasibility is evaluated with an experimental analysis in comparison with the recently reported method based on Daubechies wavelet transform. The experimental system parameters are the same as those of the simulated system. The results evaluation confirm that the proposed islanding detection method succeeds in detecting islanding mode both in the experimental and simulated systems and is superior to the Daubechies wavelet transform based detection method.

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This paper investigates the performance of differential protection of power transformers in the presence of internal faults, external faults, and cross-country faults in the presence of current transformers saturation, which is one of the main innovations of this study. Today, detection and discrimination of cross-country faults from other disturbances are one of the most important challenges facing protection engineers. Therefore, in this study, maximum overlap discrete wavelet transform has been used in order to accurately detect and classify these disturbances based on the extraction of energy coefficient indices of superior features. First, the cross-country faults, internal faults and external electrical faults, and inrush current phenomenon on the system under study in the EMTP software are simulated and differential current is sampled in different disturbances. Then, the mean indices of the sum of energy coefficient each level are calculated by MODWT by MATLAB software, and based on the values of indices, discrimination and classification of events are done. The results obtained from the simulations confirm that the proposed protection algorithm can detect and classify cross-country faults from other disturbances. Also, this method will improve the differential protection performance in different operating conditions and increase the reliability of power systems.