Identification of synchronous machine parameters Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/db78th251

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  • The synchronous machine is an essential component of a power system and determination of its parameters accurately is an important task in securing adequate modes of operation through certain control strategies. An estimation technique based on the Powell algorithm was evaluated for the identification of these parameters on the basis of small- signal input-output data. A fifth order Park doman flux linkage model of a salient pole machine was used for the identification of the parameters. Stator terminal voltages as transformed into the Park domain, field voltage and rotor frequency were used as input signals to the model. The input signals to the actual machine are the stator terminal voltages and the field voltage. The Park domain stator terminal currents and field current were used as output signals. Due to the lack of access to real data, digital simulation of an actual machine was used in an effort to establish the machine responses in the time domain to small changes in the input signals. These responses were compared with those obtained from the model with the unknown parameters and utilized in the identification process. The sensitivity of a least-square loss-function with respect to each parameter was tested. The proposed parameter identification method was evaluated with data of two different machines. Careful observation of the results indicates that convergence can only be secured if nonsimultaneous perturbation of the direct - and quadrature - axis components of the terminal voltages is applied. Implementation of such a procedure is highly complicated in practice. Therefore, a suitable method for resolving this problem is presented, which is based on transforming the input and output signals of the actual machine and the model to the frequency domain. In this form, the model can be reduced to two sets of equations, each of which depends on one of the forcing function components. The same identification algorithm that was tested in the time domain, can be used for the frequency domain identification process and has the same convergence properties.
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  • description.provenance : Approved for entry into archive by Patricia Black(patricia.black@oregonstate.edu) on 2013-07-15T17:12:07Z (GMT) No. of bitstreams: 1 ShabanAliOmar1986.pdf: 1171606 bytes, checksum: e85619c3ec450e88265c0dface752184 (MD5)
  • description.provenance : Approved for entry into archive by Patricia Black(patricia.black@oregonstate.edu) on 2013-06-26T17:27:36Z (GMT) No. of bitstreams: 1 ShabanAliOmar1986.pdf: 1171606 bytes, checksum: e85619c3ec450e88265c0dface752184 (MD5)
  • description.provenance : Made available in DSpace on 2013-07-15T17:12:07Z (GMT). No. of bitstreams: 1 ShabanAliOmar1986.pdf: 1171606 bytes, checksum: e85619c3ec450e88265c0dface752184 (MD5) Previous issue date: 1985-09-18
  • description.provenance : Submitted by Kirsten Clark (kcscannerosu@gmail.com) on 2013-06-25T18:19:32Z No. of bitstreams: 1 ShabanAliOmar1986.pdf: 1171606 bytes, checksum: e85619c3ec450e88265c0dface752184 (MD5)
  • 1986

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