فصلنامه روشهای هوشمند در صنعت برق
پیاپی 36 (زمستان 1397)

In this thesis in addition to optimal placement of phasor measurement units (PMUs) a new method is proposed for optimal distribution of measurements redundancy. Since the buses having first order redundancy degrade the system observability by missing their measurements, this method is used to reduce the number of these. On the other hand, the proposed method, in spite of the previous methods, is used to enhance the system observability by improving the redundancy order of the other low order redundancy buses without increasing the number of the lower orders. .In addition, by using this method, the optimal PMU placement can be obtained considering the contingencies such as PMU outage or line outage. It should be noted that the proposed methods are implemented on the IEEE 14-, 30, 57- and 118- bus power networks. The simulation results are compared with previous methods. In addition, simulations are performed with and without considering the effect of zero injection buses.

The purpose of this article is to classify electroencephalogram signals into two types of epilepsy and healthy. To achieve the highest accuracy, various techniques have been used. The desired characteristics of these signals can be extracted by Wavelet Transform and Empirical Mode Decomposition methods. These two methods are compared in terms of impact in the classification process. To reduce the dimensions of the feature space, Independent and principal Component Analysis methods can be used. Then, in order to reduce the effect of noise on electroencephalogram signal analysis, a smoothing method can be applied. Finally, by using Support Vector machine classifier, the existing data classified. These steps were tested for an existing data set, including 5 groups of single channel electroencephalogram signals. Results show that the empirical decomposition method has high efficiency and accuracy to extract the characteristics and classification of signals. Accordingly, the accuracy and sensitivity of both combinations of "empirical mode decomposition - independent component analysis" and "empirical mode decomposition - principal component analysis", after data smoothing, as a new approach to extraction and classification of features are 100%. The output of this system is used to control and treat the disease.

The presence of speckle as multiplicative noise in ultrasound and radar images defects the image perception. Therefore, it is necessary to reduce the speckle before processing like segmentation, edge detecting, and target navigation. In general, denoising is performed either in spatial or transform domain where in this paper, we focused on transform domain as well. Bayesian method and BiShrink approach which is the two-variable Bayesian, are addressed in the domains of separable, stationary, and Dual-tree wavelets for speckle noise reduction by thresholding. Based on simulation results, the Dual-tree wavelet is appropriate because of being separate the real and imaginary parts. In addition, the BiShrink method is more efficient than the Bayesian. To compare the performance of different methods, the standard Lena and Barabra test images and a real SAR image are used, MSE, PSNR, SSIM, ENL, and NV are computed as quantitative criteria. Also, in order to evaluate the coefficients sparsity, the histograms are shown and the average standarad devition values for all subbands are obtained.

The production of additional cells often forms a mass of tissue that is referred to as a tumor. Tumors can disrupt the proper functioning of the brain and even lead to the patients' death. One of the non-invasive diagnostic methods for this disease is Magnetic Resonance Imaging (MRI). The development of an automated or semi-automatic diagnostic system is required by the computer in medical treatments. Several algorithms have been used to detect a tumor, each with its own advantages and disadvantages. In the present study, an automatic method has been developed by the combination of new methods in order to find the exact area of the tumor in the MRI image. This algorithm is based on super pixel and RVM classification. The algorithm used in the super pixel method is the SLIC algorithm, which calculates for each super pixel 13 statistical characteristics and severity. Finally, an educational method introduced from the RVM classification algorithm that can detect the tumor portion from non-tumor in each brain MRI image. BRATS2012 dataset and FLAIR weights have been utilized in this study The results are compared with the results of the BRATS2012 data and The overlap coefficients of Dice, BF score, and Jaccard were 0.898, 0.697 and 0.754, respectively.

Usually, in the studies of optimal control for the micro-grid with combined heat and power (CHP), economic and environmental goals have been raised. In these studies, the optimization of control devices and compensators that are aimed at improving the technical aspects of micro-grid has not been considered. Therefore, in this paper, these goals are considered simultaneously. In this paper the shunt capacitor, shunt reactor, static var compensator, load ratio control transformer, step voltage regulator and power generation of DGs are investigated as control variables with the aim of considering costs, losses and voltage profile improvement. The case under study is a distribution test system including 22 buses, which consists of photovoltaic and fuel cell. All network data is extracted from a real system. As a result, the system can completely represent a real system. In order to complete the study of the system, the emissions of fuel cells are considered in the objective function. The efficiency and output electrical and thermal power of the fuel cell are considered related to part load ratio as a real standard model introduced in previous studies.

Capacitor placement at the substation is one of the effective methods for loss reduction and efficiency increasing of the power systems. In this paper, a novel method for capacitor placement on the secondary side of the distribution transformers is carried out. The mathematical method of Mixed–Integer Linear Programming (MILP) algorithm is used for paper goal to increase the Net Present Value (NPV), resulting from the loss reduction. Due to the variability of the loads, a special template is used for loads modelling. Not only the constant loads, but also three types of industrial, residential and commercial loads are assessed with the proposed method. Then, the results are discussed in order to re-grade loads. Also, the proposed method are compared with two methods as Particle Swarm Optimization (PSO) algorithm and Teaching–Learning Based Optimization (TLBO) algorithm. All of the tests are simulated with MATLAB software and total time of problem is considered as ten years.