Transient voltage distribution in inverter fed motor windings: experimental study and modeling

Bidan, Pierre; Lebey, Thierry; Montseny, G.; Saint‐Michel, Jacques

doi:10.1109/63.903993

Cited by 57 publications

(29 citation statements)

References 11 publications

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“…Then, applying the Jordan lemma and the Cauchy theorem to the inverse Laplace transform L −1 , the right-hand side relation p ± = L −1 P ± is transformed into an integral along −θ ± . This yields new expressions for the kernels p ± , which after replacement in (1)- (2) and identification, provide formulas for the complex diffusive symbols μ ± associated with P and θ ± :…”

Section: −θmentioning

confidence: 99%

“…Once the diffusive symbols are available, another concern is the numerical approximation of the integrals in (2). The diffusive realization z(x) = z + (x) + z − (x) being evaluated at some points (x n ) n=0,..,N ∈ (0, 1), the corresponding values z(x n ) are evaluated thanks to a quadrature rule by (6) z…”

Section: −θmentioning

confidence: 99%

“…Its formulation relates to an interesting technique applicable to causal operator realizations which is well developed in the context of time operators. One of the main advantages of this approach is its low computational cost; see the papers by G. Montseny et al [16], [24], [13], [2], [3] and by D. Matignon et al [10], [11], [22] for representations of various pseudodifferential operators and their approximation. C. Lubich et al [21], [20], [17], [5], [26], [18] apply a similar idea to convolution operators and develop optimized numerical methods.…”

Section: Introductionmentioning

confidence: 99%

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Diffusive realizations for solutions of some operator equations: The one-dimensional case

Lenczner¹,

Montseny²,

Yakoubi³

2012

Math. Comp.

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Abstract. In this paper we deal with the derivation of state-realizations of linear operators that are solutions to certain operator linear differential equations in one-dimensional bounded domains. We develop two approaches in the framework of diffusive representations: one with complex diffusive symbols; the other with real diffusive symbols. Then, we illustrate the theories and develop numerical methods for a Lyapunov equation arising from optimal control theory of the heat equation. A practical purpose of this approach is real-time computation on a semi-decentralized architecture with low granularity.

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Section: −θmentioning

confidence: 99%

Section: −θmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Diffusive realizations for solutions of some operator equations: The one-dimensional case

Lenczner¹,

Montseny²,

Yakoubi³

2012

Math. Comp.

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“…A complete theoretical statement will be found in Montseny [2005] and various applications to non trivial problems will be found in , Carmona and Coutin [1998a,b], Carmona et al [2003], Casenave andMontseny [2009, 2008], Degerli et al [Ap. 1999], Garcia and Bernussou [1998], Lenczner and Montseny [2005], Lenczner et al [2010], Levadoux and Montseny [2003], Montseny [2007], Mouyon and Imbert [2002], Rumeau et al [2006], Bidan et al [2001], , , Devy-Vareta and , Helie and Matignon [2006], Laudebat et al [2004], Lavernhe et al [2001], Lavernhe and Solhusvik [1998], Leger and Pontier [1999], Lubich and Schädler [2002], Matignon [July 2001], Matignon and Prieur [2005], Mbodge et al [1994], Mbodge and Montseny [1995], Montseny [2002bMontseny [ ,a, 1998], Montseny et al [2000], Nihtila and Tervo [2002], Rouzaud [1998].…”

Section: H(t S)u(s)dsmentioning

confidence: 99%

Introduction to Diffusive Representation

Casenave

Montseny

2010

IFAC Proceedings Volumes

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Diffusive representation is an operator theory elaborated during the last years. It is devoted to time-nonlocal problems, allowing significant simplifications for analysis and numerical realization of integral time operators encountered in many physical situations. Namely, most of the shortcomings induced by time-nonlocal formulations are by-passed by use of suitable time-local state realizations deduced from diffusive representation and whose numerical approximations are straightforward, thanks to good properties of diffusion equations. In this paper, we introduce the notion of diffusive representation and its dual one: the diffusive symbol, and we briefly describe the associated mathematical framework and numerical techniques. The interest of this approach is highlighted by numerical examples.

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“…Rouzaud [2003]), in electrical engineering (see e.g. Bidan et al [2001]), in biology (see e.g. Topaz and Bertozzi [2004]), etc.…”

Section: Introductionmentioning

confidence: 99%

Controllability of SISO Volterra Models via Diffusive Representation

Casenave

Prieur²

2011

IFAC Proceedings Volumes

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Abstract:The problem under consideration is the controllability of a wide class of convolution Volterra systems, namely the class of "diffusive" systems, for which there exists an input-output state realization whose state evolves in the so-called diffusive representation space. We first show that this universal state variable is approximately controllable, and then deduce that such Volterra systems always possess suitable controllability properties, stated and proved. Then, we show how to solve the optimal null control problem in an LQ sense. A numerical example finally highlights these results.

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Transient voltage distribution in inverter fed motor windings: experimental study and modeling

Cited by 57 publications

References 11 publications

Diffusive realizations for solutions of some operator equations: The one-dimensional case

Diffusive realizations for solutions of some operator equations: The one-dimensional case

Introduction to Diffusive Representation

Controllability of SISO Volterra Models via Diffusive Representation

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