Recent blackouts in power systems have shown the necessity of vulnerability assessment. Among all factors, TRANSMISSION system components have a more important role. Power system vulnerability assessment could capture cascading outages which result in large blackouts and is an effective tool for power system engineers for defining power system bottlenecks and weak points. In this paper a new method based on fault chains concept is developed which uses new measures. GENETIC algorithm with an effective structure is used for finding vulnerable branches in a practical power TRANSMISSION system. Analytic hierarchy process is a technique used to determine the weighting factors in fitness function of GENETIC algorithm. Finally, the numerical results for Isfahan Regional Electric Company are presented which verifies the effectiveness and precision of the proposed method according to the practical expriments.

In this paper a new framework is presented for multi–objective TRANSMISSION Expansion Planning (TEP). This framework is based on a multiple criteria decision making whose fundamental elements are REliability and MARKet (REMARK). Investment cost, congestion cost, Users' Benefit (UB) and Expected Customer Interruption Cost (ECOST) are considered in the optimization as four objectives. The proposed model is a complicated non-linear mixed-integer optimization problem. A hybrid GENETIC Algorithm (GA) and Quadratic Programming (QP) is used, followed by a Fuzzy Sets Theory (FST) to obtain the final optimal solution. The planning methodology has been demonstrated on the 6-machine 8-bus test system to show the feasibility and capabilities of the proposed algorithm. Also, in order to compare the historical expansion plan and the expansion plan developed by the proposed methodology, it was applied to the real life system of the northeastern part of Iranian national 400-kV TRANSMISSION grid.

In this article, an attempt has been made to estimate the amount of sound TRANSMISSION loss in a flat oval channel by applying the approach of statistical energy analysis. Correct estimation of sound TRANSMISSION loss in an air conditioning channel is of great importance due to the harmful effects of noise pollution in the environment on human health. Simulation with the statistical energy analysis method is a powerful approach to estimate sound and vibration in problems in which we deal with complex and multi-part systems; is considered. In this method, first, a system is divided into several subsystems, and then by writing a matrix equation that includes the energy exchanges between subsystems and energy loss coefficients; It is investigated from the perspective of vibration and sound estimation.On average, the model presented in this research is able to estimate the sound TRANSMISSION loss in different dimensions of the air conditioning channels according to the experimental results in the accuracy range of ± 2.5 dB. Considering that it seems that the results obtained from modeling with this method are in good agreement with the experimental data; The results of this research can be used as an efficient approach to estimate noise in oval shaped channels stretched in different lengths.

In this article, a new combined approach of a decision tree and clustering is presented to predict the TRANSMISSION of GENETIC diseases. In this article, the performance of these algorithms is compared for more accurate prediction of disease TRANSMISSION under the same condition and based on a series of measures like the positive predictive value, negative predictive value, accuracy, sensitivity and specificity. The results show that support vector machine algorithm outperformed the other two simple algorithms and the neural network and GENETIC algorithms offered better prediction at the end, while the proposed combined approach is developed using different parameters and outperformed the simple methods.

In the present paper, power TRANSMISSION interaction of multilayered sound isolation panels consists of porous, solid materials and air gaps using Transfer Matrix Method (TMM) has been considered. Considering the theories related to acoustical behavior of multilayered panel lined with poroelastic materials, detail explanation of TRANSMISSION Loss (TL) of a panel via TMM has been presented. Calculation of TL for a specified panel via TMM has been compared to existed experimental data in the literature and excellent agreement is observed. Next, based on this verified model, a multi-objective optimization of multilayered panel has been conducted using NSGA-II to maximize TL of panel while the panel weight is kept to a minimum. Results of two-objective optimization reveals, if the designer target is to achieve a specific average TL in the frequency band of 10 to 500 Hz, for a panel with constant width, selecting a panel with lower layers (three layers) can bring lower weight. But, if a higher average TL in the same frequency band is desired, a panel with more layers (six layers) has much better conditions in terms of weight.

TRANSMISSION towers are the most important part of the systems for transferring electrical power and for its distribution. These structures are designed to support the conductors and ground wires of electrical power lines and withstand mechanical forces in the worst weather and under the worst atmospheric conditions. A linear analysis is generally sufficient for the analysis of a TRANSMISSION tower. Since these structures contain a high number of elements, and due to their high cost, their optimization is of great importance from both a material and a construction point of view. TRANSMISSION towers are often subjected to many combinations of loading and, in the process of optimization; one is faced with many analyses. In this paper, a GENETIC algorithm is employed for optimization. One of the main difficulties in using the GENETIC algorithm for optimization is that, for each chromosome in each generation, one analysis should be performed. This requires the inversion of matrices and, with an increase in the number of elements and in geometric optimization, the convergence becomes very slow. In order to overcome this difficulty, neural networks are trained as analyzers to take on part of the computational load. Using neural networks increases the rate of convergence of the optimization. A multi-population GENETIC algorithm is used in this article.