Characteristic Induction Motor Slip Values for Variable Voltage Part Load Performances Optimization

Jian, Ting-Yu; Schmitz, N. L.; Novotny, D.W.

doi:10.1109/tpas.1983.317995

Cited by 45 publications

(2 citation statements)

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“…Major drawbacks of SC are: slow convergence rate, flux/ torque ripples, difficulties in tuning the algorithm for a given application, and the need for precise measurement of the input power which is difficult and costly. On the other hand, the LMC computes the optimum flux level that minimises losses through a machine model [9][10][11][12][13][14][15][16]. LMC approach provides smooth and fast adaptation of the machine flux with low implementation cost.…”

Section: Introductionmentioning

confidence: 99%

“…Application of LMC to the vector, [9][10][11][12], and scalar, [13][14][15], controlled IM drives has been proposed using different steady state models and implementation techniques. When the model disregards the core loss and magnetic saturation, the optimal stator flux (i.e., the flux where the efficiency is maximum) is uniquely determined by the speed [16]. The idea of searching for maximum efficiency by use of a LMC including the core loss and magnetic saturation has also been addressed.…”

Section: Introductionmentioning

confidence: 99%

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Neural network based robust optimal energy control of pulse width modulation‐inverter fed motor driving pump

Ebrahim,

Badr,

Elgendy

et al. 2023

IET Electric Power Appl

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This paper revisits loss model control (LMC) of the 3‐phase induction motor (IM) and presents a robust LMC algorithm for medium‐sized pump drives. Compared with other power loss reduction algorithms for IM, the presented one has the advantages of fast and smooth flux adaptation, high accuracy, and versatile implementation. An improved loss‐model for IM drive has been developed. The model considers the surplus power loss caused by inverter voltage harmonics and magnetic saturation using closed‐form equations. Further, the resistance‐temperature change is considered by a first‐order thermal model. To determine the optimal flux level that achieves maximum drive efficiency, an artificial neural network (ANN) controller is synthesised and trained offline. The voltage and speed control loops are connecting via the stator frequency to avoid the possibility of excessive magnetization. Beside, the obtained thermal information enhances motor protection and control. These together have the potential of making the proposed algorithm robust and reliable. The system reliability is investigated and assessed in terms of energy saving using ramp start/stop. Theoretical analysis, computer simulations, and experimental studies are performed on 5.5 kW variable speed water pump using the proposed control. The test results are provided and discussed to validate the effectiveness.

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