Showing 3 results for Sharifi
H. Heydari, R. Sharifi,
Volume 7, Issue 1 (March 2011)
Abstract
The design process of a superconducting current limiter (SFCL) requires simulation and definition of its electrical, magnetic and thermal properties in form of equivalent circuits and mathematical models. However, any change in SFCL parameters: dimension, resistance, and operating temperature can affect the limiting mode, quench time, and restore time. In this paper, following the simulation of electrical and thermal behavior of resistive and inductive SFCLs and investigation on their performance variation responded to change parameters, the best design cases will be selected by using multiple criteria decision making (MCDM) techniques. As a case study, to evaluate proposed MCDM approaches in design of superconducting fault current limiter, a model in which a SFCL is located at an outgoing feeder in a 20 kV distribution substation will be considered and best designs will be presented for both resistive and inductive type.
R. Ebrahimpour, S. Sarhangi, F. Sharifizadeh,
Volume 7, Issue 4 (December 2011)
Abstract
This paper presents the results of Persian handwritten word recognition based on Mixture of Experts technique. In the basic form of ME the problem space is automatically divided into several subspaces for the experts, and the outputs of experts are combined by a gating network. In our proposed model, we used Mixture of Experts Multi Layered Perceptrons with Momentum term, in the classification Phase. We produce three different Mixture of Experts structure. Experimental result for proposed method show an error rate reduction of 6.42 % compare to the mixture of MLPs experts. Comparison with some of the most related methods indicates that the proposed model yields excellent recognition rate in handwritten word recognition.
A. Ghanuni, R. Sharifi, H. Feshki Farahani,
Volume 19, Issue 3 (September 2023)
Abstract
Operation scheduling of a Virtual Power Plant (VPP) includes several challenges for the system according to the uncertain parameters, and security requirements, which intensify the need for more efficient models for energy scheduling and power trading strategies. Making suitable decisions under uncertainties, related to Renewable Energy Resources (RES), loads, and market prices impose extra considerations for the problem to make a clearer insight for the system operators to participate in local markets. This paper proposes a new risk-based hybrid stochastic model to investigate the effects of wind turbine power fluctuations on profit function, energy scheduling, and market participating strategies. Also, an incentivized Demand Response Program (DRP) is used, to enhance the system’s efficiency. The results of the study indicate that the proposed model based on Information Gap Decision Theory (IGDT) approach makes a clearer environment for the decision-maker to be aware of the effects of risk-taking or a risk-averse strategy on financial profits. The results show that a 30% of robustness and opportunity consideration would change the profit function from -12.5% up to 14.5%, respectively. A modified IEEE 33 bus test system is used to simulate a technical VPP considering the voltage stability and thermal capacity of line requirements.
