The Experts below are selected from a list of 237 Experts worldwide ranked by ideXlab platform

G.t. Heydt - One of the best experts on this subject based on the ideXlab platform.

  • Synchronous machine parameter estimation using the Hartley series
    IEEE Transactions on Energy Conversion, 2001
    Co-Authors: J.j.r. Melgoza, Ali Keyhani, G.t. Heydt, B.l. Agrawal, D.a. Selin
    Abstract:

    This paper presents a novel alternative to estimate Armature Circuit parameters of large utility generators using real time operating data. The proposed approach uses the Hartley series for fitting operating data (voltage and currents measurements). The essence of the method is the use of linear state estimation to identify the coefficients of the Hartley series. The approach is tested for noise corruption likely to be found in measurements. The method is found to be suitable for the processing of digital fault recorder data to identify synchronous machine parameters.

  • Identification of Armature, field, and saturated parameters of a large steam turbine-generator from operating data
    IEEE Transactions on Energy Conversion, 2000
    Co-Authors: H.b. Karayaka, Ali Keyhani, B.l. Agrawal, D.a. Selin, G.t. Heydt
    Abstract:

    This paper presents a step by step identification procedure of Armature, field and saturated parameters of a large steam turbine-generator from real time operating data. First, data from a small excitation disturbance is utilized to estimate Armature Circuit parameters of the machine. Subsequently, for each set of steady state operating data, saturable mutual inductances L/sub ads/ and L/sub aqs/ are estimated. The recursive maximum likelihood estimation technique is employed for identification in these first two stages. An artificial neural network (ANN) based estimator is used to model these saturated inductances based on the generator operating conditions. Finally, using the estimates of the Armature Circuit parameters, the field winding and some damper winding parameters are estimated using an output error method (OEM) of estimation. The developed models are validated with measurements not used in the training of ANN and with large disturbance responses.

  • Parameter Estimation Using An Orthogonal Series Expansion
    Electric Machines & Power Systems, 2000
    Co-Authors: J. Rico, G.t. Heydt
    Abstract:

    This paper presents an innovative alternative to estimate parameters of a system for which a dynamic model is known. The focus of this paper is the estimation of the Armature Circuit parameters of large utility generators using real time operating data. Other applications are possible. The alternatives considered are the use of orthogonal series expansions, in general, and the Hartley series, in particular. The main idea considers the use of orthogonal series expansions for fitting operating data (e.g., voltage and currents measurements). This allows writing a set of linear algebraic equations that can be “ solved” in the least squares sense for the unknown parameters. The method shown utilizes the pseudoinverse in the solution. The essence of the approach is linear state estimation. Several alternative types of orthogonal expansions are briefly discussed. Although solutions are the same in all domains, one wishes to employ the expansion that gives the most efficient computation. The approach may be used ...

  • Identification of Armature Circuit and field winding parameters of large utility generators
    IEEE Power Engineering Society. 1999 Winter Meeting (Cat. No.99CH36233), 1999
    Co-Authors: H.b. Karayaka, Ali Keyhani, B. Agrawal, D. Selin, G.t. Heydt
    Abstract:

    This paper presents a methodology to estimate Armature Circuit and field winding parameters of large utility generators using both the synthetic data obtained by the machine natural abc frame of reference simulation and real time operating data of a utility generator. A one-machine infinite bus system including the machine and its excitation system is simulated in the abc frame of reference by using parameters provided by the machine manufacturer. A proper data set required for estimation is collected by perturbing the field side of the machine in small amounts. The recursive maximum likelihood (RML) estimation technique is employed for the identification of Armature Circuit parameters. Subsequently, based on the estimates of Armature Circuit parameters, the field winding and some damper parameters are estimated using an output error estimation (OEM) technique. For each estimation case, the estimation performance is also validated with noise corrupted measurements. Finally, the developed methodology is used to identify an actual utility generator parameters from on-line operating data.

  • Methodology development for estimation of Armature Circuit and field winding parameters of large utility generators
    IEEE Transactions on Energy Conversion, 1999
    Co-Authors: H.b. Karayaka, Ali Keyhani, B.l. Agrawal, D.a. Selin, G.t. Heydt
    Abstract:

    This paper presents a methodology to estimate Armature Circuit and field winding parameters of large utility generators using the synthetic data obtained by the machine natural abc frame of reference simulation. First, a one-machine infinite bus system including the machine and its excitation system is simulated in abc frame of reference by using parameters provided by the machine manufacturer. A proper data set required for estimation is collected by perturbing the field side of the machine in small amounts, The recursive maximum likelihood (RML) estimation technique is employed for the identification of Armature Circuit parameters. Subsequently, based on the estimates of Armature Circuit parameters, the field winding and some damper parameters are estimated using an output error estimation (OEM) technique. For each estimation case, the estimation performance is also validated with noise corrupted measurements. Even in case of remarkable noise corruption, the agreement between estimated and actual parameters is quite satisfactory.

Matthew Armstrong - One of the best experts on this subject based on the ideXlab platform.

  • a wound field three phase flux switching synchronous motor with all excitation sources on the stator
    IEEE Transactions on Industry Applications, 2010
    Co-Authors: Ackim Zulu, B C Mecrow, Matthew Armstrong
    Abstract:

    A three-phase, segmental-rotor, flux-switching synchronous motor is presented for the first time, with both field and Armature windings placed on the stator. Mutual coupling of the Circuits is through segments whose motion causes switching of flux in the Armature Circuit. The magnetic geometry is substantially different to that of other flux-switching machines (FSMs), and may offer advantages in terms of torque density and controllability. This paper describes the principles and the evaluation of a three-phase design, based on a 12-tooth stator with an eight-segment rotor, before introducing a prototype machine and test results. The prototype has been found to have torque density and profile comparable to that of a switched reluctance motor (SRM) of the same size.

  • a wound field three phase flux switching synchronous motor with all excitation sources on the stator
    Energy Conversion Congress and Exposition, 2009
    Co-Authors: Ackim Zulu, B C Mecrow, Matthew Armstrong
    Abstract:

    A three-phase, segmental-rotor, flux-switching synchronous motor is presented for the first time, with both field and Armature windings placed on the stator. Mutual coupling of the Circuits is through segments whose motion causes switching of flux in the Armature Circuit. The magnetic geometry is substantially different to that of other flux-switching machines, and may offer advantages in terms of torque density and controllability. This paper describes the principles and the evaluation of a three-phase design, based on a 12-tooth stator with an 8-segment rotor before introducing a prototype machine and test results.

D.a. Selin - One of the best experts on this subject based on the ideXlab platform.

  • Synchronous machine parameter estimation using the Hartley series
    IEEE Transactions on Energy Conversion, 2001
    Co-Authors: J.j.r. Melgoza, Ali Keyhani, G.t. Heydt, B.l. Agrawal, D.a. Selin
    Abstract:

    This paper presents a novel alternative to estimate Armature Circuit parameters of large utility generators using real time operating data. The proposed approach uses the Hartley series for fitting operating data (voltage and currents measurements). The essence of the method is the use of linear state estimation to identify the coefficients of the Hartley series. The approach is tested for noise corruption likely to be found in measurements. The method is found to be suitable for the processing of digital fault recorder data to identify synchronous machine parameters.

  • Identification of Armature, field, and saturated parameters of a large steam turbine-generator from operating data
    IEEE Transactions on Energy Conversion, 2000
    Co-Authors: H.b. Karayaka, Ali Keyhani, B.l. Agrawal, D.a. Selin, G.t. Heydt
    Abstract:

    This paper presents a step by step identification procedure of Armature, field and saturated parameters of a large steam turbine-generator from real time operating data. First, data from a small excitation disturbance is utilized to estimate Armature Circuit parameters of the machine. Subsequently, for each set of steady state operating data, saturable mutual inductances L/sub ads/ and L/sub aqs/ are estimated. The recursive maximum likelihood estimation technique is employed for identification in these first two stages. An artificial neural network (ANN) based estimator is used to model these saturated inductances based on the generator operating conditions. Finally, using the estimates of the Armature Circuit parameters, the field winding and some damper winding parameters are estimated using an output error method (OEM) of estimation. The developed models are validated with measurements not used in the training of ANN and with large disturbance responses.

  • Methodology development for estimation of Armature Circuit and field winding parameters of large utility generators
    IEEE Transactions on Energy Conversion, 1999
    Co-Authors: H.b. Karayaka, Ali Keyhani, B.l. Agrawal, D.a. Selin, G.t. Heydt
    Abstract:

    This paper presents a methodology to estimate Armature Circuit and field winding parameters of large utility generators using the synthetic data obtained by the machine natural abc frame of reference simulation. First, a one-machine infinite bus system including the machine and its excitation system is simulated in abc frame of reference by using parameters provided by the machine manufacturer. A proper data set required for estimation is collected by perturbing the field side of the machine in small amounts, The recursive maximum likelihood (RML) estimation technique is employed for the identification of Armature Circuit parameters. Subsequently, based on the estimates of Armature Circuit parameters, the field winding and some damper parameters are estimated using an output error estimation (OEM) technique. For each estimation case, the estimation performance is also validated with noise corrupted measurements. Even in case of remarkable noise corruption, the agreement between estimated and actual parameters is quite satisfactory.

H.b. Karayaka - One of the best experts on this subject based on the ideXlab platform.

  • synchronous generator model identification and parameter estimation from operating data
    IEEE Transactions on Energy Conversion, 2003
    Co-Authors: H.b. Karayaka, Ali Keyhani, G T Heyd, L Agrawal, D Seli
    Abstract:

    A novel technique to estimate and model parameters of a 460-MVA large steam turbine generator from operating data is presented. First, data from small excitation disturbances are used to estimate linear model Armature Circuit and field winding parameters of the machine. Subsequently, for each set of steady state operating data, saturable inductances L/sub ds/ and L/sub qs/ are identified and modeled using nonlinear mapping functions-based estimators. Using the estimates of the Armature Circuit parameters, for each set of disturbance data collected at different operating conditions, the rotor body parameters of the generator are estimated using an output error method (OEM). The developed nonlinear models are validated with measurements not used in the estimation procedure.

  • Identification of Armature, field, and saturated parameters of a large steam turbine-generator from operating data
    IEEE Transactions on Energy Conversion, 2000
    Co-Authors: H.b. Karayaka, Ali Keyhani, B.l. Agrawal, D.a. Selin, G.t. Heydt
    Abstract:

    This paper presents a step by step identification procedure of Armature, field and saturated parameters of a large steam turbine-generator from real time operating data. First, data from a small excitation disturbance is utilized to estimate Armature Circuit parameters of the machine. Subsequently, for each set of steady state operating data, saturable mutual inductances L/sub ads/ and L/sub aqs/ are estimated. The recursive maximum likelihood estimation technique is employed for identification in these first two stages. An artificial neural network (ANN) based estimator is used to model these saturated inductances based on the generator operating conditions. Finally, using the estimates of the Armature Circuit parameters, the field winding and some damper winding parameters are estimated using an output error method (OEM) of estimation. The developed models are validated with measurements not used in the training of ANN and with large disturbance responses.

  • Identification of Armature Circuit and field winding parameters of large utility generators
    IEEE Power Engineering Society. 1999 Winter Meeting (Cat. No.99CH36233), 1999
    Co-Authors: H.b. Karayaka, Ali Keyhani, B. Agrawal, D. Selin, G.t. Heydt
    Abstract:

    This paper presents a methodology to estimate Armature Circuit and field winding parameters of large utility generators using both the synthetic data obtained by the machine natural abc frame of reference simulation and real time operating data of a utility generator. A one-machine infinite bus system including the machine and its excitation system is simulated in the abc frame of reference by using parameters provided by the machine manufacturer. A proper data set required for estimation is collected by perturbing the field side of the machine in small amounts. The recursive maximum likelihood (RML) estimation technique is employed for the identification of Armature Circuit parameters. Subsequently, based on the estimates of Armature Circuit parameters, the field winding and some damper parameters are estimated using an output error estimation (OEM) technique. For each estimation case, the estimation performance is also validated with noise corrupted measurements. Finally, the developed methodology is used to identify an actual utility generator parameters from on-line operating data.

  • Methodology development for estimation of Armature Circuit and field winding parameters of large utility generators
    IEEE Transactions on Energy Conversion, 1999
    Co-Authors: H.b. Karayaka, Ali Keyhani, B.l. Agrawal, D.a. Selin, G.t. Heydt
    Abstract:

    This paper presents a methodology to estimate Armature Circuit and field winding parameters of large utility generators using the synthetic data obtained by the machine natural abc frame of reference simulation. First, a one-machine infinite bus system including the machine and its excitation system is simulated in abc frame of reference by using parameters provided by the machine manufacturer. A proper data set required for estimation is collected by perturbing the field side of the machine in small amounts, The recursive maximum likelihood (RML) estimation technique is employed for the identification of Armature Circuit parameters. Subsequently, based on the estimates of Armature Circuit parameters, the field winding and some damper parameters are estimated using an output error estimation (OEM) technique. For each estimation case, the estimation performance is also validated with noise corrupted measurements. Even in case of remarkable noise corruption, the agreement between estimated and actual parameters is quite satisfactory.

Ali Keyhani - One of the best experts on this subject based on the ideXlab platform.

  • synchronous generator model identification and parameter estimation from operating data
    IEEE Transactions on Energy Conversion, 2003
    Co-Authors: H.b. Karayaka, Ali Keyhani, G T Heyd, L Agrawal, D Seli
    Abstract:

    A novel technique to estimate and model parameters of a 460-MVA large steam turbine generator from operating data is presented. First, data from small excitation disturbances are used to estimate linear model Armature Circuit and field winding parameters of the machine. Subsequently, for each set of steady state operating data, saturable inductances L/sub ds/ and L/sub qs/ are identified and modeled using nonlinear mapping functions-based estimators. Using the estimates of the Armature Circuit parameters, for each set of disturbance data collected at different operating conditions, the rotor body parameters of the generator are estimated using an output error method (OEM). The developed nonlinear models are validated with measurements not used in the estimation procedure.

  • Synchronous machine parameter estimation using the Hartley series
    IEEE Transactions on Energy Conversion, 2001
    Co-Authors: J.j.r. Melgoza, Ali Keyhani, G.t. Heydt, B.l. Agrawal, D.a. Selin
    Abstract:

    This paper presents a novel alternative to estimate Armature Circuit parameters of large utility generators using real time operating data. The proposed approach uses the Hartley series for fitting operating data (voltage and currents measurements). The essence of the method is the use of linear state estimation to identify the coefficients of the Hartley series. The approach is tested for noise corruption likely to be found in measurements. The method is found to be suitable for the processing of digital fault recorder data to identify synchronous machine parameters.

  • Identification of Armature, field, and saturated parameters of a large steam turbine-generator from operating data
    IEEE Transactions on Energy Conversion, 2000
    Co-Authors: H.b. Karayaka, Ali Keyhani, B.l. Agrawal, D.a. Selin, G.t. Heydt
    Abstract:

    This paper presents a step by step identification procedure of Armature, field and saturated parameters of a large steam turbine-generator from real time operating data. First, data from a small excitation disturbance is utilized to estimate Armature Circuit parameters of the machine. Subsequently, for each set of steady state operating data, saturable mutual inductances L/sub ads/ and L/sub aqs/ are estimated. The recursive maximum likelihood estimation technique is employed for identification in these first two stages. An artificial neural network (ANN) based estimator is used to model these saturated inductances based on the generator operating conditions. Finally, using the estimates of the Armature Circuit parameters, the field winding and some damper winding parameters are estimated using an output error method (OEM) of estimation. The developed models are validated with measurements not used in the training of ANN and with large disturbance responses.

  • Identification of Armature Circuit and field winding parameters of large utility generators
    IEEE Power Engineering Society. 1999 Winter Meeting (Cat. No.99CH36233), 1999
    Co-Authors: H.b. Karayaka, Ali Keyhani, B. Agrawal, D. Selin, G.t. Heydt
    Abstract:

    This paper presents a methodology to estimate Armature Circuit and field winding parameters of large utility generators using both the synthetic data obtained by the machine natural abc frame of reference simulation and real time operating data of a utility generator. A one-machine infinite bus system including the machine and its excitation system is simulated in the abc frame of reference by using parameters provided by the machine manufacturer. A proper data set required for estimation is collected by perturbing the field side of the machine in small amounts. The recursive maximum likelihood (RML) estimation technique is employed for the identification of Armature Circuit parameters. Subsequently, based on the estimates of Armature Circuit parameters, the field winding and some damper parameters are estimated using an output error estimation (OEM) technique. For each estimation case, the estimation performance is also validated with noise corrupted measurements. Finally, the developed methodology is used to identify an actual utility generator parameters from on-line operating data.

  • Methodology development for estimation of Armature Circuit and field winding parameters of large utility generators
    IEEE Transactions on Energy Conversion, 1999
    Co-Authors: H.b. Karayaka, Ali Keyhani, B.l. Agrawal, D.a. Selin, G.t. Heydt
    Abstract:

    This paper presents a methodology to estimate Armature Circuit and field winding parameters of large utility generators using the synthetic data obtained by the machine natural abc frame of reference simulation. First, a one-machine infinite bus system including the machine and its excitation system is simulated in abc frame of reference by using parameters provided by the machine manufacturer. A proper data set required for estimation is collected by perturbing the field side of the machine in small amounts, The recursive maximum likelihood (RML) estimation technique is employed for the identification of Armature Circuit parameters. Subsequently, based on the estimates of Armature Circuit parameters, the field winding and some damper parameters are estimated using an output error estimation (OEM) technique. For each estimation case, the estimation performance is also validated with noise corrupted measurements. Even in case of remarkable noise corruption, the agreement between estimated and actual parameters is quite satisfactory.