Robust stator flux and load torque estimations for induction motor drives with EKF-based observer

Demir, Rıdvan

doi:10.1007/s00202-022-01717-y

Cited by 5 publications

(3 citation statements)

References 30 publications

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“…These statistics clearly show the strong domination of the IMs for electrical machines in contemporary times. Additionally, according to statistical forecasters, it has been projected that IMs will continue to be the predominant machine in use for a minimum of twenty years [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Extended Kalman Filter design for sensorless sliding mode predictive control of induction motors without weighting factor: An experimental investigation

Chebaani,

Mahmoud,

Tazay

et al. 2023

PLoS ONE

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Due to their simplicity, cheapness, and ease of maintenance, induction motors (IMs) are the most widely used motors in the industry. However, if they are not properly controlled, the load torque and motor speed will fluctuate in an unsatisfactory fashion. To effectively control the load torque and speed of these IMs, it is necessary to use specialized drives. The entire system (IMs + Drives) will experience uncertainty, nonlinearities, and disruptions, which calls for an outstanding performance control structure. The sensorless sliding mode predictive torque control (SSM-PTC) for both AC-DC converter and DC-AC inverter, which are utilized for feeding the IM, is investigated in this work. The AC-DC converter is controlled using the SSM-PTC method in order to follow the DC-link reference voltage throughout any changes in the operating point of the IM. While the DC-AC inverter is controlled using a sensorless predictive power control (SPPC). Within a unity power factor, this SPPC regulates the reactive power flow between the motor and the supply to account for the undesirable harmonic components of the grid current. In addition, an experimental performance improvement of SSM-PTC of IM supplied by a 5-leg AC-DC-AC power converter using extended Kalman filter (EKF) without weighting factor (WF) is also studied in this work. Design and implantation of the suggested control systems are performed using a dSPACE 1104 card. The experimental results of the proposed converter control demonstrate that the suggested approach effectively regulated the DC link, reducing load torque and speed fluctuations. In the context of inverter control, a prompt active power response yields a motor current waveform that resembles a sinusoidal pattern, exhibiting minimal levels of harmonic distortion.

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Section: Introductionmentioning

confidence: 99%

Extended Kalman Filter design for sensorless sliding mode predictive control of induction motors without weighting factor: An experimental investigation

Chebaani,

Mahmoud,

Tazay

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…These estimators/observers can be classified as reduced/ full-order observers (Orlowska-Kowalska et al, 2019), sliding-mode observer (Mousavi et al, 2023), model reference adaptive systems (Das et al, 2019), extended Luenberger observers (You et al, 2018, and unscented/extended Kalman filters (EKFs) (Yildiz et al, 2020). Contrary to the other methods, the EKFs offer a stochastic approach taking into account the measurement and system noises for the states and parameters estimations of IM (Demir, 2023). Therefore, many researchers use the EKF algorithm to realise state and parameter estimations of nonlinear systems in the literature.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Controlled IM Drive based on IT2FNN Controller

Demir,

Yildiz,

Gani

2023

Power Electronics and Drives

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In this paper, the predictive torque control (PTC) based induction motor (IM) drive using an interval type-2 fuzzy neural network (IT2FNN) controller in the speed control loop is designed and tested in simulations. The states required for the proposed motor drive are estimated by extended complex Kalman filter (ECKF). The ECKF performs online estimations of stator currents, rotor fluxes, rotor mechanical speed, and rotor resistance. Compared to conventional extended Kalman filter (EKF), which estimates the same states/parameters, the designed ECKF has less computational burden because it does not contain matrix inverse and the matrix dimensions have been reduced. In addition, the rotor resistance estimated by ECKF is updated online to the PTC system. Thus, the performance of the PTC-based IM drive is improved against variations in the rotor resistance, whose value changes with operating conditions such as frequency and temperature. In order to force both the ECKF observer and the proposed IM drive, a challenging scenario containing the wide speed range operation of the IM is designed. Simulation results confirm the performance of the proposed speed-sensorless PTC-based drive that uses an IT2FNN controller in the speed control loop and the estimation performance of the ECKF observer.

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“…Some reasons for using IMs in both industrial and EV applications are reasonable cost, simplicity in construction, working in harsh environments, and fewer maintenance requirements. There are also some control strategies used in the IM drives to perform the high-performance control application of IMs, such as direct/indirect vector control (VC) (Reddy et al, 2022;Vas, 1998), direct torque control (Alsofyani and Idris, 2016;Demir, 2023;Vas, 1998), and model predictive control (Rodriguez et al, 2012;Wróbel et al, 2020). However, the highly nonlinear model of the IM whose parameters change with operating conditions makes high-performance control challenging.…”

Section: Introductionmentioning

confidence: 99%

Rotor Speed and Load Torque Estimations of Induction Motors via LSTM Network

Kosten,

Emlek,

Yildiz

et al. 2023

Power Electronics and Drives

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In this study, a long short-term memory (LSTM) based estimator using rotating axis components of the stator voltages and currents as inputs is designed to perform estimations of rotor mechanical speed and load torque values of the induction motor (IM) for electrical vehicle (EV) applications. For this aim, first of all, an indirect vector controlled IM drive is implemented in simulation to collect both training and test datasets. After the initial training, a fine-tuning process is applied to increase the robustness of the proposed LSTM network. Furthermore, the LSTM parameters, layer size, and hidden size are also optimised to increase the estimation performance. The proposed LSTM network is tested under two different challenging scenarios including the operation of the IM with linear and step-like load torque changes in a single direction and in both directions. To force the proposed LSTM network, it is also tested under the variation of stator and rotor resistances for the both-direction scenario. The obtained results confirm the highly satisfactory estimation performance of the proposed LSTM network and its applicability for the EV applications of the IMs.

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Robust stator flux and load torque estimations for induction motor drives with EKF-based observer

Cited by 5 publications

References 30 publications

Extended Kalman Filter design for sensorless sliding mode predictive control of induction motors without weighting factor: An experimental investigation

Extended Kalman Filter design for sensorless sliding mode predictive control of induction motors without weighting factor: An experimental investigation

Model Predictive Controlled IM Drive based on IT2FNN Controller

Rotor Speed and Load Torque Estimations of Induction Motors via LSTM Network

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