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Andrew T. Yang - One of the best experts on this subject based on the ideXlab platform.

  • Preservation of passivity during RLC Network Reduction via split congruence transformations
    IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 1998
    Co-Authors: Kevin J. Kerns, Andrew T. Yang
    Abstract:

    Resistance-inductance-capacitance (RLC) Network Reduction refers to the formulation of small Networks whose port behavior is similar to that of large RLC Networks. Several Network Reduction algorithms have been developed in the last few years, but none exist for RLC Networks which preserve passivity. The loss of passivity can be a serious problem because simulations of the reduced Networks may encounter artificial oscillations or "time step too small" errors which render the simulations useless. This paper presents a set of well-conditioned transformations called "split congruence transformations" (SCTs) which can he used to preserve specified moments and resonances for RLC Network Reduction, and these transformations are proven to preserve passivity. Network Reduction examples are provided to demonstrate the utility of SCTs.

  • preservation of passivity during rlc Network Reduction via split congruence transformations
    Design Automation Conference, 1997
    Co-Authors: Kevin J. Kerns, Andrew T. Yang
    Abstract:

    None of the existing Network Reduction tools preserve passivityfor RLC Networks. The loss of passivity can be a serious problembecause simulations of the reduced Networks may encounter"time step too small" errors. This paper presents a set oftransformations called "Split Congruence Transformations"(SCT's) which can be used to accurately reduce a RLC Networkwhile preserving passivity.

  • DAC - Preservation of passivity during RLC Network Reduction via split congruence transformations
    Proceedings of the 34th annual conference on Design automation conference - DAC '97, 1997
    Co-Authors: Kevin J. Kerns, Andrew T. Yang
    Abstract:

    None of the existing Network Reduction tools preserve passivityfor RLC Networks. The loss of passivity can be a serious problembecause simulations of the reduced Networks may encounter"time step too small" errors. This paper presents a set oftransformations called "Split Congruence Transformations"(SCT's) which can be used to accurately reduce a RLC Networkwhile preserving passivity.

Kevin J. Kerns - One of the best experts on this subject based on the ideXlab platform.

  • Preservation of passivity during RLC Network Reduction via split congruence transformations
    IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 1998
    Co-Authors: Kevin J. Kerns, Andrew T. Yang
    Abstract:

    Resistance-inductance-capacitance (RLC) Network Reduction refers to the formulation of small Networks whose port behavior is similar to that of large RLC Networks. Several Network Reduction algorithms have been developed in the last few years, but none exist for RLC Networks which preserve passivity. The loss of passivity can be a serious problem because simulations of the reduced Networks may encounter artificial oscillations or "time step too small" errors which render the simulations useless. This paper presents a set of well-conditioned transformations called "split congruence transformations" (SCTs) which can he used to preserve specified moments and resonances for RLC Network Reduction, and these transformations are proven to preserve passivity. Network Reduction examples are provided to demonstrate the utility of SCTs.

  • preservation of passivity during rlc Network Reduction via split congruence transformations
    Design Automation Conference, 1997
    Co-Authors: Kevin J. Kerns, Andrew T. Yang
    Abstract:

    None of the existing Network Reduction tools preserve passivityfor RLC Networks. The loss of passivity can be a serious problembecause simulations of the reduced Networks may encounter"time step too small" errors. This paper presents a set oftransformations called "Split Congruence Transformations"(SCT's) which can be used to accurately reduce a RLC Networkwhile preserving passivity.

  • DAC - Preservation of passivity during RLC Network Reduction via split congruence transformations
    Proceedings of the 34th annual conference on Design automation conference - DAC '97, 1997
    Co-Authors: Kevin J. Kerns, Andrew T. Yang
    Abstract:

    None of the existing Network Reduction tools preserve passivityfor RLC Networks. The loss of passivity can be a serious problembecause simulations of the reduced Networks may encounter"time step too small" errors. This paper presents a set oftransformations called "Split Congruence Transformations"(SCT's) which can be used to accurately reduce a RLC Networkwhile preserving passivity.

Carlos Canudas De Wit - One of the best experts on this subject based on the ideXlab platform.

  • Large-scale Network Reduction towards scale-free structure
    IEEE Transactions on Network Science and Engineering, 2019
    Co-Authors: Nicolas Martin, Paolo Frasca, Carlos Canudas De Wit
    Abstract:

    This paper deals with a particular problem of graph Reduction. The reduced graph is aimed to have a particular structure, namely to be scale-free. To this end, we define a metric to measure the scale-freeness by measuring the difference between the degree distribution and the scale-free degree distribution. The Reduction is made under constraints to preserve consistency with the initial graph. In particular, the reduced graph preserves the eigenvector centrality of the initial graph. We study the optimization problem and, based on the gained insights, we derive an algorithm allowing to find an approximate solution. We also show that, if the initial Network is a flow Network, it is possible to design the algorithm such that the output remains a flow Network. Experimental results are then presented to optimally choose the parameters of the algorithm suggesting that, by tuning a parameter, it is possible to speed up the algorithm with a comparable efficiency. Finally, the algorithm is applied to an example of large physical Network: the Grenoble urban traffic Network.

  • ECC - A Network Reduction Method Inducing Scale-Free Degree Distribution
    2018 European Control Conference (ECC), 2018
    Co-Authors: Nicolas Martin, Paolo Frasca, Carlos Canudas De Wit
    Abstract:

    This paper deals with the problem of graph Reduction towards a scale-free graph while preserving a consistency with the initial graph. This problem is formulated as a minimization problem and to this end we define a metric to measure the scale-freeness of a graph and another metric to measure the similarity between two graphs with different dimensions, based on spectral centrality. We also want to ensure that if the initial Network is a flow Network, the reduced Network preserves this property. We explore the optimization problem and, based on the gained insights, we derive an algorithm allowing to find an approximate solution. Finally, the effectiveness of the algorithm is shown through a simulation on a Manhattan-like Network.

Nicolas Martin - One of the best experts on this subject based on the ideXlab platform.

  • Large-scale Network Reduction towards scale-free structure
    IEEE Transactions on Network Science and Engineering, 2019
    Co-Authors: Nicolas Martin, Paolo Frasca, Carlos Canudas De Wit
    Abstract:

    This paper deals with a particular problem of graph Reduction. The reduced graph is aimed to have a particular structure, namely to be scale-free. To this end, we define a metric to measure the scale-freeness by measuring the difference between the degree distribution and the scale-free degree distribution. The Reduction is made under constraints to preserve consistency with the initial graph. In particular, the reduced graph preserves the eigenvector centrality of the initial graph. We study the optimization problem and, based on the gained insights, we derive an algorithm allowing to find an approximate solution. We also show that, if the initial Network is a flow Network, it is possible to design the algorithm such that the output remains a flow Network. Experimental results are then presented to optimally choose the parameters of the algorithm suggesting that, by tuning a parameter, it is possible to speed up the algorithm with a comparable efficiency. Finally, the algorithm is applied to an example of large physical Network: the Grenoble urban traffic Network.

  • ECC - A Network Reduction Method Inducing Scale-Free Degree Distribution
    2018 European Control Conference (ECC), 2018
    Co-Authors: Nicolas Martin, Paolo Frasca, Carlos Canudas De Wit
    Abstract:

    This paper deals with the problem of graph Reduction towards a scale-free graph while preserving a consistency with the initial graph. This problem is formulated as a minimization problem and to this end we define a metric to measure the scale-freeness of a graph and another metric to measure the similarity between two graphs with different dimensions, based on spectral centrality. We also want to ensure that if the initial Network is a flow Network, the reduced Network preserves this property. We explore the optimization problem and, based on the gained insights, we derive an algorithm allowing to find an approximate solution. Finally, the effectiveness of the algorithm is shown through a simulation on a Manhattan-like Network.

Funso K. Ariyo - One of the best experts on this subject based on the ideXlab platform.

  • Electrical Network Reduction for Load Flow and Short-Circuit Calculations using PowerFactory Software
    2013
    Co-Authors: Funso K. Ariyo
    Abstract:

    The primary purpose in constructing equivalents is to represent a portion of a Network containing many buses but having only a few boundary buses by a reduced Network containing only the boundary buses and, perhaps, a few selected buses from within the original sub-Network. The equivalent constructed gives an exact reproduction of the self and transfer impedances of the external system as seen from its boundary buses. PowerFactory’s Network Reduction algorithm produces an equivalent representation of the reduced part of the Network and calculates its parameters. This equivalent representation is valid for both load flow and short-circuit calculations, including asymmetrical faults (that is, single-phase faults).

  • Electrical Network Reduction for Load Flow and Short-Circuit Calculations Using Power Factory Software
    American Journal of Electrical Power and Energy Systems, 2013
    Co-Authors: Funso K. Ariyo
    Abstract:

    The primary purpose in constructing equivalents is to represent a portion of a Network containing many buses but having only a few "boundary buses" by a reduced Network containing only the boundary buses and, perhaps, a few selected buses from within the original sub-Network. The equivalent constructed gives an exact reproduction of the self and transfer impedances of the external system as seen from its boundary buses. PowerFactory’s Network Reduction algorithm produces an equivalent representation of the reduced part of the Network and calculates its parameters. This equivalent re-presentation is valid for both load flow and short-circuit calculations, including asymmetrical faults (that is, single-phase faults).