Sharif Digital Repository

With the rising penetration of wind power into electric power systems, more accurate and comprehensive studies are required to identify the interaction between wind farm(s) and power system. This requires accurate models of different types of wind turbine-generators. This paper describes modeling and small signal analysis of a grid connected fixed speed wind turbine generator (FSWTG). A complete model of FSWTG is derived and some reduced-order models are deduced. Eigenvalues and participation factors of the system for these models are calculated. Then, using the eigen analysis results, the models are compared and the simplifying assumptions which have been considered in the literatures are... 

Multiple input multiple output (MIMO) radar is known for its superiority over conventional radar due to its antenna and waveform diversity. However, the increased hardware cost (due to multiple transmitters and receivers), power consumption (due to multiple transmitters and pulses), and computational complexity (due to numerous pulses) form the drawbacks of MIMO radar. On one hand, a higher estimation accuracy is required, but on the other hand, a lower number of active antennas/pulses is desirable. Therefore, in this paper, by proposing a convex optimization approach for the general case of transmitter-receiver-pulse selection, we will minimize the total number of active antennas/pulses in... 

This paper develops a mathematical model based on the stochastic programming for simultaneous generation expansion and retirement planning. Retirement decision of the existing generating units is accommodated since these units are aging more and more. The proposed method is formulated as an optimization problem in which the objective function is to minimize the expected total cost consisting of the investment required for commissioning new units, operation and maintenance costs, the retirement salvage cost, and the system risk cost. The problem is subjected to a set of generating unit and system physical and operational constraints. The modeling of energy limited units is also devised in a... 

In conventional arrays, improving angular resolution requires larger aperture which demands more number of elements. On the other hand, array thinning is an efficient method of achieving narrow beamwidth (high angular resolution) with fewer number of elements. Reducing the number of elements results in reducing weight, cost, hardware complexity, and energy consumption. In this paper, a novel dynamic programming algorithm of array thinning with the objective of reducing sidelobe levels (SLLs), desired for large circular arrays, is proposed. The circular array is partitioned into annular rings, and the objective of the optimization problem is to determine the number of active elements in each... 

Array Thinning is the procedure of reducing the number of elements in such a way that leads to low sidelobe level and narrow beamwidth. Main motivations of array thinning are reduction in weight and cost of antennas array. W.P.M.N. Keizer has presented a new approach for the synthesis of thinned uniformly spaced linear arrays featuring a minimum sidelobe level. The method is based on the iterative Fourier technique to derive element excitations from the prescribed array factor using successive forward and backward Fourier transforms. Array thinning is accomplished by setting the amplitudes of the largest element excitations to unity and the others to zero during each iteration cycle. In this... 

Flow assurance is a major challenge in offshore and deepwater operations. Conventional approaches for preventing gas-hydrate formation involve using thermodynamic inhibitors (e.g., methanol, glycol) or kinetic hydrate inhibitors or operating outside the hydratestability zone by insulating the pipeline and/or active heating. These techniques are not always economical and in some cases are not practical for deepwater operations, long tiebacks, or aging reservoirs with high water cuts. The industry needs new and novel flow-assurance techniques to address these challenging conditions. The approach presented in this paper is a wet cold-flow-based method in which gas-hydrate management rather than... 

We propose two minimum-mean-square-error (MMSE) estimation methods for denoising non-Gaussian first-order autoregressive (AR(1)) processes. The first one is based on the message passing framework and gives the exact theoretic MMSE estimator. The second is an iterative algorithm that combines standard wavelet-based thresholding with an optimized non-linearity and cycle-spinning. This method is more computationally efficient than the former and appears to provide the same optimal denoising results in practice. We illustrate the superior performance of both methods through numerical simulations by comparing them with other well-known denoising schemes