An offline parameter identification method of induction motor

Zheng, Jinhai; Wang, Yunkuan; Qin, Xiaofei; Zhang, Xin

doi:10.1109/wcica.2008.4594335

Cited by 16 publications

(10 citation statements)

References 8 publications

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“…(4) shows that the force is attracting and the -1 value shows that the force is repelling, and k t is the parameter controlling the effect of the force type. Usually, the force coming out as attracting gathers the CPs in a certain area within the search area, while the repelling force tries to distribute the CPs.…”

Section: Stopmentioning

confidence: 99%

Induction motor parameter estimation using metaheuristic methods

Çanakoğlu¹,

Yetgin²,

Temurtaş³

et al. 2014

Turk J Elec Eng & Comp Sci

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Abstract:The steady-state equivalent circuit parameters of an induction motor can be estimated using the operation characteristics that are provided by manufacturers. The characteristics of the motor used in estimation methods are the starting, maximum, and nominal torque values; the power factor; and efficiency. The operation characteristics of a motor given in data sheets are generally based on design parameters and are not suitable with real values. For this reason, in this paper, the data used in the parameter estimation for induction motors are taken from the literature.Using an optimization method for parameter estimation is useful for comparing the manufacturer values and values at the end of estimation, as well as minimizing the error in between. There are many methods in the literature for the parameter estimation of induction motors. In this study, the estimation is made using the charged system search (CSS), differential evolution algorithm (DEA), particle swarm optimization, and genetic algorithm optimization techniques. The CSS algorithm is first applied for estimation of the parameters of an induction motor. The results obtained from all of the methods show that the CSS algorithm is suitable with the DEA. From the obtained results, it is understood that an exact approach can be made to equivalent circuit parameters in case the values given by the manufacturer model the motor properly.

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Section: Stopmentioning

confidence: 99%

Induction motor parameter estimation using metaheuristic methods

Çanakoğlu¹,

Yetgin²,

Temurtaş³

et al. 2014

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

show abstract

“…Using HCP algorithm, multiple motor parameters can be obtained in only one run. Hence, it avoids errors due to constant machine parameters assumption [20].…”

Section: Introductionmentioning

confidence: 99%

“…The proposed method is low cost and technically simple. It does not require special test signals or configuration of machine [23], [20]. The required current and voltage can be readily measured from switchboard.…”

Section: Introductionmentioning

confidence: 99%

Induction Motor Parameter Estimation Using Sparse Grid Optimization Algorithm

Duan

Živanović

Al-Sarawi

et al. 2016

IEEE Trans. Ind. Inf.

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Inaccurate motor parameters can lead to an inefficient motor control. Although several motor estimation methods have been utilized to estimate motor parameters, it is still challenging to ensure a good level of confidence in the estimation. In this paper, we propose a novel offline induction motor parameter estimation method based on sparse grid optimization algorithm. The estimation is achieved by matching the response of machines mathematical model with recorded stator current and voltage signals. This approach is non-invasive as it uses external measurements, resulting in reduced system complexity and cost. A globally optimal point was found by sampling on the sparse grid, which was created using the hyperbolic cross points (HCPs) and additional heuristics. This has resulted in reducing the total number of search points and provided the best match between the mathematical model and measurement data. The estimated motor parameters can be further refined by using any local search method. The experimental results indicate a very good agreement between estimated values and reference values.

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“…The proposed technique do not use the voltage signal (2) , but uses the current information with several steps to complete the identification of all the parameters, where the current controller structure is changed every time the identification step proceeds, and the minimum operation point of the current norm is sought by tuning the specific motor parameter until the parameter mismatch is cancelled out. The search for the current norm minimum point is achieved by an ordinary hillclimbing-algorithm with respect to the parameter mismatch.…”

Section: Introductionmentioning

confidence: 99%

Offline Parameter Identification of Interior Permanent Magnet Synchronous Motor Based on Current Norm Characteristics

Noguchi

2015

IEEJ Journal IA

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This paper describes a novel offline parameter identification technique for an interior permanent magnet synchronous motor. In general, the motor controller requires its parameter information to start the motor. Therefore, offline parameter identification is indispensable to avoid degradation in the control performance. The proposed technique changes the configuration of the current regulator and seeks the minimum point of the current norm where the parameter mismatch is eliminated. The unique feature of the method is the use of only the current information and its insensitiveness to the winding resistance variation due to a temperature change. The paper presents an identification process for the q-axis inductance L q , the winding resistance R, the magnetic flux linkage ψ and the d-axis inductance L d . In addition, some simulation and experimental tests are conducted, and their results are presented to demonstrate the validity of the proposed technique. In the final part of the paper, the sensitivity to the winding resistance is also checked by the experimental test.

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An offline parameter identification method of induction motor

Cited by 16 publications

References 8 publications

Induction motor parameter estimation using metaheuristic methods

Induction motor parameter estimation using metaheuristic methods

Induction Motor Parameter Estimation Using Sparse Grid Optimization Algorithm

Offline Parameter Identification of Interior Permanent Magnet Synchronous Motor Based on Current Norm Characteristics

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