A Recursive Prediction Error Method with Effective Use of Gradient-Functions to Adapt PMSM-Parameters Online

This paper proposes a method for online estimation of electrical parameters of interior permanent magnet synchronous machines (IPMSM) based on the recursive prediction error method (RPEM). The parameter-sensitivity functions (herein known as the gradient functions, Ψ T ) both in dynamic and steady -...

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Vydáno v:Conference record of the Industry Applications Conference s. 1 - 5
Hlavní autoři: Perera, Aravinda, Nilsen, Roy
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 10.10.2020
Témata:
Convergence
Gain-sequence
gradient function
Hessian
Heuristic algorithms
magnet flux linkage
online identification
Permanent magnet machines
permanent magnet synchronous machine
Permanent magnets
Prediction algorithms
recursive prediction error algorithm
sensitivity analysis
Steady-state
stochastic gradient
Torque
ISSN:2576-702X
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Abstract This paper proposes a method for online estimation of electrical parameters of interior permanent magnet synchronous machines (IPMSM) based on the recursive prediction error method (RPEM). The parameter-sensitivity functions (herein known as the gradient functions, Ψ T ) both in dynamic and steady -states are exploited for this purpose. The RPEM has been computed using the stochastic gradient algorithm (SGA). The scalar Hessian matrix, r[k] appearing in the algorithm has been analyzed for both its steady and dynamic states. Different combinations of Ψ T and r[k] -states have been simulated and compared with respect to performance when used for parameter adaptation.
AbstractList This paper proposes a method for online estimation of electrical parameters of interior permanent magnet synchronous machines (IPMSM) based on the recursive prediction error method (RPEM). The parameter-sensitivity functions (herein known as the gradient functions, Ψ T ) both in dynamic and steady -states are exploited for this purpose. The RPEM has been computed using the stochastic gradient algorithm (SGA). The scalar Hessian matrix, r[k] appearing in the algorithm has been analyzed for both its steady and dynamic states. Different combinations of Ψ T and r[k] -states have been simulated and compared with respect to performance when used for parameter adaptation.
Author Perera, Aravinda
Nilsen, Roy
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  givenname: Aravinda
  surname: Perera
  fullname: Perera, Aravinda
  email: aravinda.perera@ntnu.no
  organization: Norwegian University of Science and Technology,Department of Electric Power Engineering,Trondheim,Norway
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  givenname: Roy
  surname: Nilsen
  fullname: Nilsen, Roy
  email: roy.nilsen@ntnu.no
  organization: Norwegian University of Science and Technology,Department of Electric Power Engineering,Trondheim,Norway
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Snippet This paper proposes a method for online estimation of electrical parameters of interior permanent magnet synchronous machines (IPMSM) based on the recursive...
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SubjectTerms Convergence
Gain-sequence
gradient function
Hessian
Heuristic algorithms
magnet flux linkage
online identification
Permanent magnet machines
permanent magnet synchronous machine
Permanent magnets
Prediction algorithms
recursive prediction error algorithm
sensitivity analysis
Steady-state
stochastic gradient
Torque
Title A Recursive Prediction Error Method with Effective Use of Gradient-Functions to Adapt PMSM-Parameters Online
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