Parameter estimation for induction machines based on sensitivity analysis

Ansuj, S.; Shokooh, F.; Schinzinger, Roland

doi:10.1109/28.44239

Cited by 65 publications

(14 citation statements)

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“…As a consequence, different alternatives have been proposed in the literature. Some examples include that proposed by Waters and Willoughby [3], where the parameter are estimated from the knowledge of certain variables, such as stator resistance and the leakage reactance, that proposed by Ansuj [4], where the identification is…”

Section: Introductionmentioning

confidence: 99%

Induction Motor Parameter Identification Using a Gravitational Search Algorithm

2016

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Abstract:The efficient use of electrical energy is a topic that has attracted attention for its environmental consequences. On the other hand, induction motors represent the main component in most industries. They consume the highest energy percentages in industrial facilities. This energy consumption depends on the operation conditions of the induction motor imposed by its internal parameters. Since the internal parameters of an induction motor are not directly measurable, an identification process must be conducted to obtain them. In the identification process, the parameter estimation is transformed into a multidimensional optimization problem where the internal parameters of the induction motor are considered as decision variables. Under this approach, the complexity of the optimization problem tends to produce multimodal error surfaces for which their cost functions are significantly difficult to minimize. Several algorithms based on evolutionary computation principles have been successfully applied to identify the optimal parameters of induction motors. However, most of them maintain an important limitation: They frequently obtain sub-optimal solutions as a result of an improper equilibrium between exploitation and exploration in their search strategies. This paper presents an algorithm for the optimal parameter identification of induction motors. To determine the parameters, the proposed method uses a recent evolutionary method called the gravitational search algorithm (GSA). Different from most of the existent evolutionary algorithms, the GSA presents a better performance in multimodal problems, avoiding critical flaws such as the premature convergence to sub-optimal solutions. Numerical simulations have been conducted on several models to show the effectiveness of the proposed scheme.

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Section: Introductionmentioning

confidence: 99%

Induction Motor Parameter Identification Using a Gravitational Search Algorithm

2016

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“…The ratio of the stator reactance to rotor reactance, q ¼ X s =X r , is a parameter that depends on the type and class of the machine [10]. Typically, q ¼ 1 for class A and wound-rotor machines, q ¼ 2=3 for class B machines, and q ¼ 3=7 for class C machines.…”

Section: ðB:15þmentioning

confidence: 99%

A simple passive voltage-balancing scheme for three-phase induction generators interfaced with single-phase grids in micro hydroelectric systems

Delghavi

Yazdani

2016

International Journal of Electrical Power & Energy Systems

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“…The method had the advantage of not requiring torque measurements. The mathematical technique for estimating the equivalent circuit parameters of induction machines from the most available performance characteristics was presented in [7,8]. These methods utilize machine equations to estimate the parameters and then performs sensitivity analysis with respect to the circuit parameters to match the given performance characteristics.…”

Section: Introductionmentioning

confidence: 99%

Bacterial Foraging Technique Based Parameter Estimation of Induction Motor from Manufacturer Data

Sakthivel¹,

Ramachandran²,

Subramanian³

2010

Electric Power Components and Systems

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Induction machine parameters supplied by manufacturers are usually sufficient for short-circuit analysis only. System studies that involve transient simulation of machines require additional parameters that are not readily available but, nonetheless, are essential for an accurate modeling of the machines. This article presents a new stochastic optimization technique to estimate the equivalent circuit parameters of induction machines from the manufacturer data, such as nameplate data and motor performance characteristics, using the bacterial foraging technique. The equivalent circuit parameters are obtained as the solution for the error minimization function between the estimated and manufacturer data. The method has the advantage of not requiring any invasive measurements. The feasibility of the proposed bacterial foraging technique has been tested and examined on two different sample motors, and it has been benchmarked with particle swarm optimization, immune algorithm, and classical parameter estimation methods. The simulation results reveal that the proposed technique efficiently solved the parameter estimation problems and outperforms the particle swarm optimization and immune algorithm methods in terms of solution quality and convergence properties.

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Parameter estimation for induction machines based on sensitivity analysis

Cited by 65 publications

References 1 publication

Induction Motor Parameter Identification Using a Gravitational Search Algorithm

Induction Motor Parameter Identification Using a Gravitational Search Algorithm

A simple passive voltage-balancing scheme for three-phase induction generators interfaced with single-phase grids in micro hydroelectric systems

Bacterial Foraging Technique Based Parameter Estimation of Induction Motor from Manufacturer Data

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