Transient Load Model of an Induction Motor

Cathey, J.J.; Cavin, Ralph K.; Ayoub, A. K.

doi:10.1109/tpas.1973.293548

Cited by 69 publications

(19 citation statements)

References 5 publications

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“…The total model of a circuit breaker is shown in Figure 8 in bond graphs. Note that each switched 0-junction is also given a control signal, U , as in (10), and that additional switched 0-junctions are needed to connect several gensets to a common power grid.…”

Section: Circuit Breakers and Power Grid Modelmentioning

confidence: 99%

“…Such model reductions are also studied in [8,9]. In [7], stability and control of such systems are treated as well, and in [10] the sensitivity of eigenvalues is studied and in [11] the Nyquist stability criterion is used to assure stability in DC power systems. When it comes to overall power plant control, functionalities for synchronizing gensets and for load sharing are important.…”

Section: Introductionmentioning

confidence: 99%

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A real-time simulator framework for marine power plants with weak power grids

Skjong

Pedersen

2017

Mechatronics

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This work takes aim at presenting a generic real-time simulation framework for marine power plants with weak power grids, containing transient functionalities such as starting and stopping of arbitrary generators, and phase synchronization. The generator models used in the power plant are hybrid causality models, meaning that they have the ability to switch between causality orientations, between voltage and current. These models facilitate real-time simulations as long as they are solved properly, as will be discussed in this article. Much is devoted to numerical stability, model robustness and power plant control, e.g. rms voltage control, engine speed control, active-and reactive power sharing control and phase synchronization control. Some focus is also given to overall power plant control structure. A case study of a marine power plant including two generators and a fluctuating-and noisy power consumption is presented and analysed, and illustrates the advantages of the proposed framework as well as giving a good foundation for future works.

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Section: Circuit Breakers and Power Grid Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A real-time simulator framework for marine power plants with weak power grids

Skjong

Pedersen

2017

Mechatronics

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“…The d-q model for synmtetrical two phases induction machines is described in the following equation [2,8,10]. (1 …”

Section: Equation Of Induction Motor and Proposed Schemementioning

confidence: 99%

“…An error value is then computed by comparing the output value of this network with a desired value which resulted from the model reference (MRAIMS). The error is starting speed error respect to the reference minimized by computing the gradient of the squared error when the net is mapping data of input vectors to output vectors, we get (8) where od (it) and ok (n) are desired output and computed output at nth sampling interval respectively. The error signal is propagated back through the network and used to update the weight of network.…”

Section: Equation Of Induction Motor and Proposed Schemementioning

confidence: 99%

Adaptive Supervisory of Induction Motor Starting by Artificial Neural Networks

1996

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This paper presents implementation of Artificial Neural Network Controller (ANNC) for induction motor starting , to track the reference condition scheme. To achieve mapping of the desired input/output we combine generally known method for control, the model reference adaptive system and the learning algoritlmr. The reference data as tracking trajectory computed from the model referennnnnnnnnnnnnnnnnnnnnce adaptive of induction motor starting (MRAIMS). A supervised learning neural network algorithm is used to return the induction motor starting speed (rotation starting) disturbance condition caused by the varies of load, back to the starting speed reference condition. The ANNC continuously monitors the status of the system parameter and the performance of motor starting, and the ANNC output is used as the current control signal of induction motor starting, because control of starting current, will improve the behavior of starting speed.keywords: artificial neural network controller(ANNC), model reference adaptive of induction motor starting(MRAIMS), starting speed

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“…The most commonly used generator is the induction generator, of which the types include cage, wound rotor and doubly fed induction generator (DFIG). The dynamic modeling [16][17][18][19][20] and control [21][22][23][24][25][26][27][28] of induction machines have been extensively researched. Thringer and Luomi [16] examined the validity of various dynamic models of induction machines to include the fifth-order Park model and other reduced order models by predicting the low frequency dynamic response of a 15 kW induction machine and comparing results to actual measurements.…”

Section: Introductionmentioning

confidence: 99%

Robust nonlinear control strategy to maximize energy capture in a variable speed wind turbine with an internal induction generator

Iyasere

Salah

Dawson

et al. 2012

J. Control Theory Appl.

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This paper proposes a control strategy to maximize the wind energy captured in a variable speed wind turbine, with an internal induction generator, at low to medium wind speeds. The proposed strategy controls the tip-speed ratio, via the rotor angular speed, to an optimum point at which the efficiency constant (or power coefficient) is maximum for a particular blade pitch angle and wind speed. This control method allows for aerodynamic rotor power maximization without exact wind turbine model knowledge. Representative numerical results demonstrate that the wind turbine can be controlled to achieve near maximum energy capture.

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Transient Load Model of an Induction Motor

Cited by 69 publications

References 5 publications

A real-time simulator framework for marine power plants with weak power grids

A real-time simulator framework for marine power plants with weak power grids

Adaptive Supervisory of Induction Motor Starting by Artificial Neural Networks

Robust nonlinear control strategy to maximize energy capture in a variable speed wind turbine with an internal induction generator

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