Estimating Parameters of the Induction Machine by the Polynomial Regression

Wu, Ray; Tseng, Yuan-Wei; Chen, Cheng-Yi

doi:10.3390/app8071073

Cited by 22 publications

(11 citation statements)

References 20 publications

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“…Since no additional parameter tuning was carried out, the experimental results also highlight the robustness of the adaptive observer with respect to substantial parameter deviations in the characterization of the induction machine. Indeed, standard IEEE 112 tests are affected by non-negligible errors, especially when compared to more advanced off-line estimation methods [37]. This approach allows us, therefore, to portray the improved accuracy in rotor position estimation granted by the adaptive observer in comparison to a standard observer.…”

Section: Resultsmentioning

confidence: 99%

Performance Analysis of a Full Order Sensorless Control Adaptive Observer for Doubly-Fed Induction Generator in Grid Connected Operation

2021

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This paper focuses on the performance analysis of a sensorless control for a Doubly Fed Induction Generator (DFIG) in grid-connected operation for turbine-based wind generation systems. With reference to a conventional stator flux based Field Oriented Control (FOC), a full-order adaptive observer is implemented and a criterion to calculate the observer gain matrix is provided. The observer provides the estimated stator flux and an estimation of the rotor position is also obtained through the measurements of stator and rotor phase currents. Due to parameter inaccuracy, the rotor position estimation is affected by an error. As a novelty of the discussed approach, the rotor position estimation error is considered as an additional machine parameter, and an error tracking procedure is envisioned in order to track the DFIG rotor position with better accuracy. In particular, an adaptive law based on the Lyapunov theory is implemented for the tracking of the rotor position estimation error, and a current injection strategy is developed in order to ensure the necessary tracking sensitivity around zero rotor voltages. The roughly evaluated rotor position can be corrected by means of the tracked rotor position estimation error, so that the corrected rotor position is sent to the FOC for the necessary rotating coordinate transformation. An extensive experimental analysis is carried out on an 11 kW, 4 poles, 400 V/50 Hz induction machine testifying the quality of the sensorless control.

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Section: Resultsmentioning

confidence: 99%

Performance Analysis of a Full Order Sensorless Control Adaptive Observer for Doubly-Fed Induction Generator in Grid Connected Operation

2021

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“…(37), and all the IFOC d-q voltage, current, flux, slip frequency, etc., are calculated using Eq. ( 2)- (11).…”

Section: Ecps Evaluation During Runningmentioning

confidence: 99%

Online equivalent parameter estimation using BPANN controller with low-frequency signal injection for a sensorless induction motor drive

Banerjee

Bera

2022

Journal of Electrical Systems and Inf Technol

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The development of a sensorless induction motor drive is elaborated in this paper with a unique feature of online estimation of equivalent circuit parameters (ECPs) during its running condition. The cases like temperature rise of the motor, change in drive speed due to sudden changes in its loading, or even in supply voltage are the sources of error in accurate ECPs evaluation. The indirect field-oriented control (IFOC) scheme is adopted in its controller to deal with these challenges. The drive controller is designed with a model reference adaptive system (MRAS) having two models—one is the plant model to estimate the ECPs during running conditions while the other one is the reference model. The H-G diagram method is utilized in the reference model to estimate the reference ECPs (ECPR) before starting without the need of performing any physical tests on the motor. During the transition period of the drive, the feedback signal is fed to the reference model to generate the ECPR. The technique of the backpropagation algorithm with an artificial neural network (BPANN) is utilized in the plant model while its weight and gain parameters are tuned with the reference ECPs. The Adam rule is utilized for fast convergence of the BPANN weights during the transition period while stator temperature and speed feedback enhance the overall accuracy in ECPs. A discrete-time low-frequency signal injection-based resistance assessment and sensorless speed estimation method to determine inductances are adopted for minimizing the ECPs errors. The results from MATLAB-based simulation and a hardware prototype using a DSPIC microcontroller with different running conditions show the efficacy of the proposed algorithm.

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“…High power induction motors are designed to obtain higher active factors and efficiencies. For this reason, the parameters of the equivalent circuits, as well as the coefficients a , b, and c, depend on the rated power [ 15 , 16 , 17 , 18 ]. Other parameters, such as the number of polar pairs, play a minor rule in the mechanical power–current relationship.…”

Section: The Proposed Techniquementioning

confidence: 99%

A Low-Cost IoT Sensors Network for Monitoring Three-Phase Induction Motor Mechanical Power Adopting an Indirect Measuring Method

Ciancetta

Fiorucci

Ometto

et al. 2021

Sensors

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Three-phase induction motors are widely diffused in the industrial environment. Many times, the rated power of three-phase induction motors is not properly chosen causing incorrect operating conditions from an energetic point of view. Monitoring the mechanical dimension of a new motor is helpful, should an existing motor need to be replaced. This paper presents an IoT sensors network for monitoring the mechanical power produced by three-phase induction motors, adopting an indirect measuring method. The proposed technique can be easily adopted to monitor the mechanical power using only one line of current transducer, reducing the cost of the monitoring system. The proposed indirect measurement technique has been implemented on a low-cost IoT system, based on a Photon Particle SoC. The results show that the proposed IoT system can estimate the mechanical power with a relative error of within 8%.

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Estimating Parameters of the Induction Machine by the Polynomial Regression

Abstract: The theoretical impedance-slip rate characteristic curve of the induction machine can be expressed as a polynomial fraction. Parameters can be obtained via one calculation.

Cited by 22 publications

References 20 publications

Performance Analysis of a Full Order Sensorless Control Adaptive Observer for Doubly-Fed Induction Generator in Grid Connected Operation

Performance Analysis of a Full Order Sensorless Control Adaptive Observer for Doubly-Fed Induction Generator in Grid Connected Operation

Online equivalent parameter estimation using BPANN controller with low-frequency signal injection for a sensorless induction motor drive

A Low-Cost IoT Sensors Network for Monitoring Three-Phase Induction Motor Mechanical Power Adopting an Indirect Measuring Method

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