The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform

N.a. Demerdash - One of the best experts on this subject based on the ideXlab platform.

  • simulation of inverter fed induction motor drives with pulse width modulation by a time stepping coupled finite element flux linkage based State Space Model
    IEEE Transactions on Energy Conversion, 1999
    Co-Authors: J.f. Bangura, N.a. Demerdash
    Abstract:

    In this paper, a detailed description of the simulation of inverter-fed induction motors in adjustable speed drives by a time-stepping flux linkage-based coupled finite element/State-Space Model for the computation of the drive performance is presented and verified experimentally. The flux linkage-based State-Space Model is iteratively coupled to a 2D time-stepping finite element Model. The iterative nature of the coupling between these two Models facilitates rigorous Modeling of the comprehensive impact of inherent Space harmonics due to geometries and motor magnetics and time harmonics resulting from the electronic switching of inverters, as well as effects of the synergistic interaction between these time and Space harmonics. The State-Space Model includes no frame of reference transformation in so far as the flux linkages, currents or voltages. This implies that the State-Space Model directly couples the motor to its power electronic controller in one formulation and the natural variables (voltages and currents) are directly involved in the formulation and computation. Finally, results of motor drive performance simulations and corresponding test results for a six-switch inverter with 180/spl deg/ e duty cycle for each switch and a six-switch inverter with pulse-width modulation are given with excellent test result correlations, respectively.

  • A time-stepping coupled finite element-State Space Model for induction motor drives. I. Model formulation and machine parameter computation
    1997 IEEE International Electric Machines and Drives Conference Record, 1997
    Co-Authors: N.a. Demerdash, J.f. Bangura, A.a. Arkadan
    Abstract:

    A time-stepping coupled finite element-State-Space Model for induction motor drives is developed. The Model utilizes an iterative approach to include the effects of magnetic nonlinearities, and Space harmonics due to the machine magnetic circuits' topology and discrete winding layouts. Model formulation and development which include an improvement in the layout of the cage circuit representation, are given in this paper. This improvement leads to an enhancement of the "well-posedness" that is, reduction of ill-conditioning in the overall numerical convergence of the Model. Meanwhile, in a companion paper results of induction motor performance simulation are compared with no-load and load tests for sinusoidal and inverter operating conditions. Particular attention is given to comparison between sinusoidal and inverter operating losses obtained from this generalized Model.

J.f. Bangura - One of the best experts on this subject based on the ideXlab platform.

  • simulation of inverter fed induction motor drives with pulse width modulation by a time stepping coupled finite element flux linkage based State Space Model
    IEEE Transactions on Energy Conversion, 1999
    Co-Authors: J.f. Bangura, N.a. Demerdash
    Abstract:

    In this paper, a detailed description of the simulation of inverter-fed induction motors in adjustable speed drives by a time-stepping flux linkage-based coupled finite element/State-Space Model for the computation of the drive performance is presented and verified experimentally. The flux linkage-based State-Space Model is iteratively coupled to a 2D time-stepping finite element Model. The iterative nature of the coupling between these two Models facilitates rigorous Modeling of the comprehensive impact of inherent Space harmonics due to geometries and motor magnetics and time harmonics resulting from the electronic switching of inverters, as well as effects of the synergistic interaction between these time and Space harmonics. The State-Space Model includes no frame of reference transformation in so far as the flux linkages, currents or voltages. This implies that the State-Space Model directly couples the motor to its power electronic controller in one formulation and the natural variables (voltages and currents) are directly involved in the formulation and computation. Finally, results of motor drive performance simulations and corresponding test results for a six-switch inverter with 180/spl deg/ e duty cycle for each switch and a six-switch inverter with pulse-width modulation are given with excellent test result correlations, respectively.

  • A time-stepping coupled finite element-State Space Model for induction motor drives. I. Model formulation and machine parameter computation
    1997 IEEE International Electric Machines and Drives Conference Record, 1997
    Co-Authors: N.a. Demerdash, J.f. Bangura, A.a. Arkadan
    Abstract:

    A time-stepping coupled finite element-State-Space Model for induction motor drives is developed. The Model utilizes an iterative approach to include the effects of magnetic nonlinearities, and Space harmonics due to the machine magnetic circuits' topology and discrete winding layouts. Model formulation and development which include an improvement in the layout of the cage circuit representation, are given in this paper. This improvement leads to an enhancement of the "well-posedness" that is, reduction of ill-conditioning in the overall numerical convergence of the Model. Meanwhile, in a companion paper results of induction motor performance simulation are compared with no-load and load tests for sinusoidal and inverter operating conditions. Particular attention is given to comparison between sinusoidal and inverter operating losses obtained from this generalized Model.

Peter W Glynn - One of the best experts on this subject based on the ideXlab platform.

  • on incorporating forecasts into linear State Space Model markov decision processes
    Philosophical Transactions of the Royal Society A, 2021
    Co-Authors: Jacques A De Chalendar, Peter W Glynn
    Abstract:

    Weather forecast information will very likely find increasing application in the control of future energy systems. In this paper, we introduce an augmented State Space Model formulation with linear dynamics, within which one can incorporate forecast information that is dynamically revealed alongside the evolution of the underlying State variable. We use the martingale Model for forecast evolution (MMFE) to enforce the necessary consistency properties that must govern the joint evolution of forecasts with the underlying State. The formulation also generates jointly Markovian dynamics that give rise to Markov decision processes (MDPs) that remain computationally tractable. This paper is the first to enforce MMFE consistency requirements within an MDP formulation that preserves tractability. This article is part of the theme issue 'The mathematics of energy systems'.

  • on incorporating forecasts into linear State Space Model markov decision processes
    arXiv: Optimization and Control, 2021
    Co-Authors: Jacques A De Chalendar, Peter W Glynn
    Abstract:

    Weather forecast information will very likely find increasing application in the control of future energy systems. In this paper, we introduce an augmented State Space Model formulation with linear dynamics, within which one can incorporate forecast information that is dynamically revealed alongside the evolution of the underlying State variable. We use the martingale Model for forecast evolution (MMFE) to enforce the necessary consistency properties that must govern the joint evolution of forecasts with the underlying State. The formulation also generates jointly Markovian dynamics that give rise to Markov decision processes (MDPs) that remain computationally tractable. This paper is the first to enforce MMFE consistency requirements within an MDP formulation that preserves tractability.

Jacques A De Chalendar - One of the best experts on this subject based on the ideXlab platform.

  • on incorporating forecasts into linear State Space Model markov decision processes
    Philosophical Transactions of the Royal Society A, 2021
    Co-Authors: Jacques A De Chalendar, Peter W Glynn
    Abstract:

    Weather forecast information will very likely find increasing application in the control of future energy systems. In this paper, we introduce an augmented State Space Model formulation with linear dynamics, within which one can incorporate forecast information that is dynamically revealed alongside the evolution of the underlying State variable. We use the martingale Model for forecast evolution (MMFE) to enforce the necessary consistency properties that must govern the joint evolution of forecasts with the underlying State. The formulation also generates jointly Markovian dynamics that give rise to Markov decision processes (MDPs) that remain computationally tractable. This paper is the first to enforce MMFE consistency requirements within an MDP formulation that preserves tractability. This article is part of the theme issue 'The mathematics of energy systems'.

  • on incorporating forecasts into linear State Space Model markov decision processes
    arXiv: Optimization and Control, 2021
    Co-Authors: Jacques A De Chalendar, Peter W Glynn
    Abstract:

    Weather forecast information will very likely find increasing application in the control of future energy systems. In this paper, we introduce an augmented State Space Model formulation with linear dynamics, within which one can incorporate forecast information that is dynamically revealed alongside the evolution of the underlying State variable. We use the martingale Model for forecast evolution (MMFE) to enforce the necessary consistency properties that must govern the joint evolution of forecasts with the underlying State. The formulation also generates jointly Markovian dynamics that give rise to Markov decision processes (MDPs) that remain computationally tractable. This paper is the first to enforce MMFE consistency requirements within an MDP formulation that preserves tractability.

Aphrodite Galata - One of the best experts on this subject based on the ideXlab platform.

  • visual speech synthesis by Modelling coarticulation dynamics using a non parametric switching State Space Model
    International Conference on Multimodal Interfaces, 2010
    Co-Authors: Salil Deena, Shaobo Hou, Aphrodite Galata
    Abstract:

    We present a novel approach to speech-driven facial animation using a non-parametric switching State Space Model based on Gaussian processes. The Model is an extension of the shared Gaussian process dynamical Model, augmented with switching States. Audio and visual data from a talking head corpus are jointly Modelled using the proposed method. The switching States are found using variable length Markov Models trained on labelled phonetic data. We also propose a synthesis technique that takes into account both previous and future phonetic context, thus accounting for coarticulatory effects in speech.