Copyright (c) 2009 All rights reserved. http://works.bepress.com/hamidreza_radmanesh Recent documents in Hamidreza Radmanesh en-us Sun, 02 Aug 2009 12:05:15 PDT 3600 http://works.bepress.com/hamidreza_radmanesh/9 http://works.bepress.com/hamidreza_radmanesh/9 Fri, 31 Jul 2009 22:31:06 PDT Based on the concept of the external power system dynamic equivalent obtained for the study system, in this paper a reduced-order artificial neural network is proposed, to construct a model for the external part. The mastermind behind the proposed method is to identify the external part as a dynamic-algebraic ANN, and this separation between dynamic equations in the state space and algebraic equations is useful to solve the prediction problem. Moreover, using similarity transformations, the state space model can be simplified, such that all the nonlinearities are embedded in the algebraic part. Since usually the study system equations are available in the continuous time domain, the external part is converted to the continuous time domain by a novel method. To obtain this model, the system should be excited first by a sort of random disturbances, and then data measured on the boundary nodes is used to identify the model. Identification process is accomplished by training the proposed network which can be used to predict behavior of the external system with a high degree of accuracy. Such an equivalent has wide applications for dynamic stability studies. Hamed Shakouri G. Power System Dynamics http://works.bepress.com/hamidreza_radmanesh/8 http://works.bepress.com/hamidreza_radmanesh/8 Sun, 19 Oct 2008 05:53:58 PDT Based on the concept of the external power system dynamic equivalent for the study system, in this paper a reduced-order artificial neural network is proposed, which is constructed to model the external part. The mastermind behind the proposed method is to identify the external part as a dynamic-algebraic ANN, and this separation between dynamic equations in the state space form and algebraic equations is useful to solve the prediction problem. To obtain this model, the system should be excited by some disturbances, and according to the measured data on the boundary nodes, identification procedure is accomplished. Therefore, the trained network can be used to predict behavior of the external system in a high degree of accuracy. Hamidreza Radmanesh Power System Dynamics http://works.bepress.com/hamidreza_radmanesh/7 http://works.bepress.com/hamidreza_radmanesh/7 Sun, 25 Nov 2007 07:06:43 PST Hamidreza Radmanesh Telecommunication http://works.bepress.com/hamidreza_radmanesh/6 http://works.bepress.com/hamidreza_radmanesh/6 Sun, 25 Nov 2007 04:08:43 PST Hamed Shakouri G. Systems and Differential Equations http://works.bepress.com/hamidreza_radmanesh/5 http://works.bepress.com/hamidreza_radmanesh/5 Sun, 25 Nov 2007 03:39:28 PST Hamidreza Radmanesh Power System Dynamics http://works.bepress.com/hamidreza_radmanesh/2 http://works.bepress.com/hamidreza_radmanesh/2 Sat, 24 Nov 2007 22:31:52 PST This paper presents an efficient alternative method to estimate synchronous generators parameters using real time operating data. The main idea considers the use of Hybrid Legendre Block-Pulse functions for fitting measured input-output synchronous generator signals. This allows writing a set of linear algebraic equations that can be solved for the unknown parameters using the pseudo-inverse. The results obtained by this method show accuracy and robustness against the noise corruption in process and/or measurements. Therefore, this alternative utilizes two advantages: first, Integration for calculating basis functions coefficients, and averaging the signals, so that the noise corruption is highly reduced. Second, the minimum squared error concept is used in pseudo-inverse method, so that the remained error from previous stages is minimized. Hamidreza Radmanesh Power System Dynamics http://works.bepress.com/hamidreza_radmanesh/1 http://works.bepress.com/hamidreza_radmanesh/1 Sat, 24 Nov 2007 21:35:48 PST The necessity of dynamic equivalents for power system analysis has been well known since the expansion of large interconnected power networks, and has been discussed during the last decades. The present paper proposes a new method for constructing dynamic equivalents of power systems. In this method, at the first step the ``study system" is modeled completely via a single machine-infinite bus modeling procedure. Then the ``external system" is identified as a MIMO feedback block of this model in such a manner that can include dynamic effects of the latter on the behavior of the former. The method is successfully experimented on a part of the Iranian southern network. Hamed Shakouri G. Power System Dynamics