• Transactions of the Korean Society of Machine Tool Engineers
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• v.15 no.3
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• pp.8-16
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• 2006

This paper discusses design sensitivity analysis and its application to a structural dynamics modification. Eigenvalue derivatives are determined with respect to the element parameters, which include intrinsic property parameters such as Young's modulus, density of the material, diameter of a beam element, thickness of a plate element, and shape parameters. Derivatives of stiffness and mass matrices are directly calculated by derivatives of element matrices. The first and the second order derivatives of the eigenvalues are then mathematically derived from a dynamic equation of motion of FEM model. The calculation of the second order eigenvalue derivative requires the sensitivity of its corresponding eigenvector, which are developed by Nelson's direct approach. The modified eigenvalue of the structure is then evaluated by the Taylor series expansion with the first and the second derivatives of eigenvalue. Numerical examples for simple beam and plate are presented. First, eigenvalues of the structural system are numerically calculated. Second, the sensitivities of eigenvalues are then evaluated with respect to the element intrinsic parameters. The most effective parameter is determined by comparing sensitivities. Finally, we predict the modified eigenvalue by Taylor series expansion with the derivatives of eigenvalue for single parameter or multi parameters. The examples illustrate the effectiveness of the eigenvalue sensitivity analysis for the optimization of the structures.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.12
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• pp.193-200
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• 2010

In this paper, the eigenvalue sensitivity analysis is calculated in the power system which is including both generator controllers such as Exciter, PSS and thyristor controlled FACTS devices in transmission lines such as TCSC. Exciter and PSS are continuously operating controllers but TCSC has a switching device which operates non-continuously. To analyze both continuous and non-continuous operating equipments, the RCF method one of the numerical analysis method in discrete time domain is applied using discrete models of the power system. Also the eigenvalue sensitivity calculation algorithm using state transition equations in discrete time domain is devised and applied to a sampled system. As a result of simulation, the eigenvalue sensitivity coefficients calculated using discrete system models in discrete time domain are changed periodically and showed different values compared to those of continuous system model in time domain by the effect of periodic switching operations of TCSC.

• Proceedings of the KIEE Conference
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• summer
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• pp.345-347
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• 2004

In small signal stability analysis of power systems, eigenvalue analysis is the most useful method and the detailed modeling of generator has an important effect to the eigenvalues. Generator full model is used for precise dynamic analysis of generators and controllers while two-axis model is used for multi-machine systems because of the reduced order of the state matrix. Also, the eigenvalue sensitivity coefficients are used for optimizing controller parameters to improve system stability. This paper compare the first and second order eigenvalue sensitivity coefficients of controllers using generator full model with those of two-axis model. As a result of an example, the estimated eigenvalues using the first and the second eigenvalue sensitivity coefficients using generator full model is very close to those of state matrix. Also the error ratios throughout a wide range of controller parameters is less than $1\%$.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.3
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• pp.363-368
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• 2008

In this paper, the Resistive Companion Form(RCF) analysis method is applied to analyze small signal stability of power systems including thyristor controlled FACTS equipments such as SVC. The eigenvalue sensitivity analysis algorithm in discrete systems based on the RCF analysis method is presented and applied to the power system including SVC. As a result of simulation, the RCF analysis method is proved very effective to precisely calculate the variations of eigenvalues or newly generated unstable oscillation modes after periodic switching operations of SVC. Also the eigenvalue sensitivity analysis method based on the RCF analysis method enabled to precisely calculate eigenvalue sensitivity coefficients of controller parameters about the dominant oscillation mode after periodic switching operations in discrete systems. These simulation results are different from those of the conventional continuous system analysis method such as the state space equation and proved that the RCF analysis method is very effective to analyze the discrete power systems including periodically operated switching equipments such as SVC.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.05a
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• pp.609-615
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• 2001

In this paper, the eigenvalue sensitivity of vehicle powertrain was investigated by analytic method. The powertrain system was considered as a rigid body with multiple engine mounts, and the engine mounts were supposed as three linear springs in three orthogonal directions. The design parameters for the sensitivity analysis were engine mount properties (positions, stiffness, and orientations) and powertrain properties (mass, second moment of inertia, and center of gravity). Firstly, an effective form of eigenvalue problem for the powertrain system was introduced. Then, the analytic sensitivity of eigenvalue was derived using the equation. Lastly, the derived sensitivity equation was applied to a real powertrain system to provide its correctness and applicability.

• Proceedings of the KIEE Conference
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• 2003.07a
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• pp.119-121
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• 2003

In small signal stability analysis of power systems, eigenvalue analysis is the most useful method and the detailed modeling of generator gives an important effect to the eigenvalues. Generator full model is used for precise dynamic analysis of generators and controllers while two-axis model is used for multimachine systems because of the reduced order of the state matrix. Also, the eigenvalue sensitivity coefficients are used for optimization of controller parameters to improve system stability. This paper compare the first order eigenvalue sensitivity coefficients of controllers in case of generator full model with those of two-axis model. As a result of an example the estimated eigenvalues using sensitivity coefficients in case of generator full model is very close to those of state matrix within 1% error ratios.

• Proceedings of the KIEE Conference
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• 2001.07a
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• pp.217-219
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• 2001

This paper presents a new eigenvalue sensitivity analysis method based on AESOPS algorithm. The additional calculation steps are derived from the original AESOPS algorithm. The additional calculation steps are performed directly from the AESOPS algorithm after iteratively calculating electro-mechanical oscillation modes in small signal stability problems. Owing to the structural characteristics of partitioned sub-matrix of state space equations, the partial differentiation terms of system state matrix for obtaining eigenvalue sensitivity indices can be calculated very simply. By the method presented in this paper, the AESOPS algorithm can be used in controller design problem as well as analysis of small signal stability problem.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.4
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• pp.593-600
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• 2003

Structural optimization often requires the evaluation of design sensitivities. The Semi Analytic Method(SAM) fur computing sensitivity is popular in shape optimization because this method has several advantages. But when relatively large rigid body motions are identified for individual elements. the SAM shows severe inaccuracy. In this study, the improvement of design sensitivities corresponding to the rigid body mode is evaluated by exact differentiation of the rigid body modes. Moreover. the error of the SAM caused by numerical difference scheme is alleviated by using a series approximation for the sensitivity derivatives and considering the higher order terms. Finally the present study shows that the refined SAM including the iterative method improves the results of sensitivity analysis in dynamic problems.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.11
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• pp.577-584
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• 2002

This paper presents a novel approach based on eigenvalue perturbation of augmented matrix(AMEP) to estimate the eigenvalue for variation of controller parameter. AMEP is a useful tool in the analysis and design of large scale power systems containing many different types of exciters, governors and stabilizers. Also, it can be used to find possible sources of instability and to determine the most sensitivity parameters for low frequency oscillation modes. This paper describes the application results of AMEP algorithm with respect to all controller parameter of KEPCO systems. Simulation results for interarea and local mode show that the proposed AMEP algorithm can be used for turning controller parameter, and verifying system data and linear model.

• Journal of the Korean Data and Information Science Society
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• v.14 no.3
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• pp.623-630
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• 2003

Recently, Tanaka(1988) derived two influence functions related to an eigenvalue problem $(A-\lambda_sI)\upsilon_s=0$ of real symmetric matrix A and used them for sensitivity analysis in principal component analysis. In this paper, we deal with the perturbation expansions up to quadratic terms of the same functions and discuss the application to sensitivity analysis in principal component regression analysis(PCRA). Numerical example is given to show how the approximation improves with the quadratic term.