Stator Resistance Estimation Research Articles

Online Estimation of Stator Resistance in Induction Machines Using the Zero-Sequence Component of the Stator Current: Investigating Two Implementations

Online Estimation of Stator Resistance in Induction Machines Using the Zero-Sequence Component of the Stator Current: Investigating Two Implementations

A non-intrusive technique for estimating the efficiency of low voltage three-phase induction motors

This paper introduces a non-intrusive technique based on the air–gap torque (AGT) method to estimate the efficiency of induction motors (IMs). The technique uses a 3rd-degree equation to calculate the sum of windage and friction losses (FWL) and rotor stray load losses (SLLr), considering the number of IM poles. IM speed is estimated using motor current signature analysis, and a new procedure to estimate stator resistance is presented. Efficiency is then determined by applying the estimated rotor speed, stator resistance, and the sum of FWL and SLLr in the AGT equations. This method requires only nameplate information, IM line voltages, and currents. Simulations and experimental tests for balanced, unbalanced, and harmonic feeding modes show the proposed technique’s estimated efficiency values are very close to measured values, unlike the AGT method. This demonstrates the proposed technique’s high accuracy, making it suitable for non-intrusive efficiency estimation of in-service IMs in industrial programs.

An improved voltage model based on stator resistance estimation for FOC technique in the induction motor drive

An improved voltage model based on stator resistance estimation for FOC technique in the induction motor drive

Novel Advanced Artificial Neural Network-Based Online Stator and Rotor Resistance Estimator for Vector-Controlled Speed Sensorless Induction Motor Drives

This paper presents a new approach for the online estimation of stator and rotor resistance of induction motors for speed sensorless vector-controlled drives, using feed-forward artificial neural networks with advanced adaptive learning rates. For the rotor resistance estimation, a neural network model based on rotor speed and stator currents is developed. The rotor flux linkages acquired from the voltage model are compared with the neural network model. The feed-forward neural network employs an adaptive learning rate as the function of the obtained error during training for quick convergence with minimal estimation error. A two-layered neural network model based on the stator voltage and current equations is developed for the stator resistance estimation. The d-q axes stator currents obtained from the developed model are compared with the acquired d-q axes stator currents. For the fast convergence with minimal estimation error, an adaptive learning rate as the function of error is adopted during training. Furthermore, the neural network estimates the induction motor’s speed. The simulation and experimental results justify that the developed algorithms track variation in the resistances quickly and precisely along with the speed as compared with the conventional constant learning rate algorithm, leading to reliable operation of the drive.

Optimized Reference Points Based Vector Control of Induction Motor Drive for Electric Vehicle

In this paper, to reduce the power consumption and to improve the efficiency in both steady state and transient condition, optimized operating point selection based control of induction motor is presented. Firstly, the steady state non-linear loss function, which incorporates iron loss and saturation is solved for steady state optimized operating point (OOP) references. To enhance the maximum torque along with reduction in energy consumption during low speed transients, over fluxing based OOP reference selection is proposed. To overcome the adverse impact of over fluxing, in the decoupled control and to make the selection process robust, model reference adaptive system (MRAS) is used for parallel estimation of rotor time constant and stator resistance. Super twisting sliding mode observer (ST-SMO) is incorporated for speed estimation, which is also utilized as reference model (rotor time constant estimation) and adjustable model (stator resistance estimation) in MRAS based observer. This OOP reference selection method is implemented along with the rotor field-oriented control (RFOC) for the induction machine. From the simulation and experimental results, it is justified that the presented algorithm reduces the power consumption and improves the efficiency at all the modes of operation of electric vehicles.

Design and Stability Analysis of Modified Real Power-Based Stator Resistance Estimation for DTFC-SVM of Multi-Level Inverter Fed Speed Sensorless PMSM Drive

This article presents the design and stability analysis of real power-based stator resistance estimation method for sensorless control of permanent magnet synchronous motors (PMSM) using of model reference adaptive system. The proposed method uses a direct torque and flux control scheme with space vector modulation for PMSM control. The speed, torque, and flux PI controllers are designed in the synchronous reference frame and their stability is analyzed besides stator resistance estimation stability. The proposed method is implemented on a PMSM drive fed by two-level, three-level, and five-level inverters and the performance is validated through experimental and simulation results under different operating conditions. The results proved that the proposed method provides an accurate and stable estimation of stator resistance and improved control performance of the PMSM drive.

Parameter and VSI Nonlinearity Hybrid Estimation for PMSM Drives Based on Recursive Least Square

Precise electrical parameters play important roles in the high-performance control of permanent magnet synchronous machines (PMSMs). This paper proposes a novel parameter and voltage source inverter (VSI) nonlinearity hybrid estimation method to accurately estimate stator resistance, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i> -axis inductances, permanent magnet flux linkage, and VSI nonlinearity, in which the effects of magnetic saturation, cross-saturation, and temperature are all considered. The proposed hybrid estimation method consists of two parts: offline and online estimation. In the offline estimation, the four electrical parameters are successively identified by setting different operating conditions, and the identification results are stored in nonvolatile memory in tabular form. In the online estimation, the VSI nonlinearity and the compensation terms of stator resistance and permanent magnet flux linkage related to factors such as temperature and frequency are simultaneously identified by using the recursive least square (RLS) algorithm. Experimental results on a 300 kW PMSM drive system demonstrate that compared to the results achieved with the existing method, the proposed scheme achieves higher estimation accuracy. Consequently, the control performance of the system, such as the output current quality, is efficiently improved.

An improved Sliding Mode Observer for parameter estimation in induction motor drive with optimised gains

ABSTRACT This paper represents the online parameter identification and continuous updating in the controller of Induction Motor Drives (IMD). The online measure of parameters improves the operation of the speed estimator, particularly nearer to the zero speed, and also computes the adaptive slip angular frequency. The parameter of speed estimation is performed by the improved Optimized Constant Rate Reaching Law (OCRRL)-based sliding mode observer (SMO), and electrical parameters of stator resistance, mutual inductance, and rotor time constant estimation is performed by Improved Adaptive SMO (IASMO). To enhance the monitoring reliability driven by parameter mismatches, an Online Parameter Estimation Control (OPEC) is presented. Lyapunov’s stability function is used to develop OPEC’s motor parameter adaptive rule. The conventional SMO suffers from the problem of higher chattering phenomena at the sliding surface, especially at very low and zero speeds. An improved OCRRL-based SMO dissolves this issue. Further, to enhance the performance by eliminating the time required for manual tuning of gains of observers in IASMO and PI controllers in Indirect Field Oriented Control (IFOC) are obtained by the Grey Wolf Optimization (GWO) technique. The effectiveness and performance of the proposed observers are tested and verified for possible operating conditions.

Active Current Sensor Fault-Tolerant Control of Induction Motor Drive with Online Neural Network-Based Rotor and Stator Resistance Estimation

Abstract This article presents an active current sensor (CS) fault-tolerant control (FTC) strategy for induction motor (IM) drive with adaptation of rotor and stator resistances. The stator current estimator with online adaptation of resistance parameters was applied for the reconstruction of missing current signals. A model reference adaptive system (MRAS), based on a neural network (NN), was used to estimate the rotor resistance. Additionally, stator resistance estimation was applied based on ratio index. The use of such a solution allowed for a significant increase in the quality of stator current reconstruction, which is particularly important for the design of CS fault detection (FD) and compensation algorithms. A wide range of simulation studies have been carried out for different operating conditions of the IM drive. The results showed that applying rotor and stator resistance estimation can improve the quality of stator current estimation by up to approximately 95% under rated operating point. The study was carried out for nominal and low speeds, with two, one, and without healthy CS.

State Estimation MRAS and Identification of Stator Winding Phase Fault Detection of the PMSG in Wind Energy Based on the Sliding Mode Control

Abstract This paper proposes a method for the diagnosis of stator inter-turn short-circuit fault for permanent magnet synchronous generators (PMSG). Inter-turn short-circuit currents are among the most critical in PMSG. For safety considerations, a fast detection is required when a fault occurs. This approach uses the parameter estimation of the per-phase stator resistance in closed-loop control of variable speed of wind energy conversion system (WECS). In the presence of an incipient short-circuit fault, the estimation of the resistance of the stator in the d-q reference frame does not make it possible to give the exact information. To solve this problem, a novel fault diagnosis scheme is proposed using parameter estimation of the per-phase stator resistance. The per-phase stator resistance of PMSG is estimated using the MRAS algorithm technique in real time. Based on a faulty PMSG model expressed in Park’s reference frame, the number of short-circuited turns is estimated using MRAS. Fault diagnosis is on line detected by analysing the estimated stator resistance of each phase according to the fault condition. The proposed fault diagnosis scheme is implemented without any extra devices. Moreover, the information on the estimated parameters can be used to improve the control performance. The simulation results demonstrate that the proposed method can estimate the faulty phase.

Estimation of Rotor Velocity and Stator Resistance for PMSM Drive using Back-EMF SMO

Background: Sensorless control of permanent magnet synchronous motor (PMSM) at low speed remains a challenging task. Introduction: In this paper, a sensorless vector control of PMSM using a new structure of a back EMF sliding mode observer (SMO) is proposed. Methods: To remove the mechanical sensors, a back EMF-SMO is built to estimate the rotor position and speed of PMSM drives. The SMO, which replaces a sign function with a sigmoid function, can reduce the chattering phenomenon. This sensorless speed control shows great sensitivity to stator resistance and system noise. Results &amp; Discussion: To improve the robustness of sensorless vector control, the back EMFSMO technique has been used for stator resistance estimation. A novel stator resistance estimator is incorporated into the sensorless drive to compensate for the effects of stator resistance variation. Conclusion: The validity of the proposed SMO with a 1.1 kw low-speed PMSM sensorless vector control has been demonstrated by real experiments.

Single Current Sensor-Based Speed Sensorless Vector Controlled PMSM Drive

Single current sensor-based speed sensorless vector controlled PMSM (“Permanent Magnet Synchronous Motor”) drive is presented in this paper. Speed, position, and currents are estimated using single current sensor information of 3-Φ PMSM drive. 2-Φ currents in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i> – <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">axes</i> are calculated and closed in the loop using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>qs</i></sub> and a phase current information obtained from the single current sensor. The proposed method applies to all types of 3-Φ PMSM; current estimation is independent of machine parameters and inverter switching states. Drive is made speed and position sensorless by estimating using Y-MRAS (Model Reference Adaptive System). Y-MRAS is developed using reference voltages and estimated currents. The speed estimator depends on stator resistance; any variation in it will affect the drive performance. So, stator resistance needs to be estimated online and compensated in the speed estimation technique. Modified P-MRAS technique is used for stator resistance estimation. Here, the drive performance is also validated under stator resistance variation and its compensation. The proposed drive is independent of switching states, integrator terms, and differentiator terms. The single sensor drive reduces the overall cost of the drive and can be implemented into the existing system for sensor condition monitoring and to make the drive fault tolerant against the sensor failure without any extra hardware. The proposed single sensor-based drive is theoretically-modeled and simulated in the MATLAB/SIMULINK platform. The stability of the proposed drive is verified through a stability analysis. It is experimentally validated using a laboratory-developed PMSM drive prototype with a dSPACE-1104 controller board.

Development of Integrated Softcomputing Approach for Stator Resistance Estimation of Three Phase Induction Motor

In this paper, an integrated quantum inspired GA (QGA) based generalized neural network (QGA–GNN) has been developed. The QGA–GNN is used for estimation of stator resistance of a 5 hp three phase induction motor (3Φ I.M.) under different healthy and unhealthy working conditions. The experimentation is performed in the laboratory for estimating stator winding resistance under healthy and faulty (i.e., 10, 20, 30, or 40% short circuited) conditions. The motor current and motor speed are considered as input and stator resistance as output of the proposed technique. The results obtained from QGA–GNN are compared with the ANN and GNN. QGA–GNN is giving good results under different working conditions.

An Algorithm for Estimation of Stator Resistance and Inductance of Low-Cost SMPMSM Drives

The information on stator resistance and inductance is extremely important for advanced electrical drives, which are used for tuning of position estimators, current controllers, temperature monitoring, and so on. The properly tuned control systems can provide fast response, have higher stability, and increase the efficiency of motor drives; as a result, it is of great importance at the development and tuning stages. This article proposes a simple estimation technique for surface-mounted permanent magnet synchronous motors (SMPMSMs), capable of measuring stator resistance and inductance. In order to measure the stator resistance, the developed algorithm injects two-leveled dc currents that provide information for resistance calculation. At the next stage, the estimation algorithm initiates exponential current transient, measures electric time constant of the motor, and calculates inductance. In contradiction to the conventional approach, this work composes a more detailed model of the motor and takes inverter voltage drop into account. Furthermore, this article studies several exponential transients, compares them to each other, and provides recommendations for use of each approach. Then, the proposed ideas are verified using several motors of different powers and parameters. Finally, the recommendations on application of each approach and parameter selection are provided.

Double Dead-Time Signal Injection Strategy for Stator Resistance Estimation of Induction Machines

A sensorless online temperature estimator is presented in this paper, which estimates the temperature using a novel signal injection strategy. This allows to eliminate the temperature sensors in the machine, as well as their faults, increasing the system reliability. A double dead-time DC signal is injected in the machine, adding a controlled offset in the control drive through the inverter. The proposed strategy eliminates the effect of the dead-time in the injected signal, which is an important drawback in DC injection strategies for resistance estimation. Furthermore, additional hardware is not needed. The strategy has been implemented in an inverter-fed railway traction induction machine. The proposed algorithm has been validated in a real test-bench.

Stator resistance and speed estimation for five level inverter fed DTFC-SVM of speed sensorless PMSM drive

Stator resistance and speed estimation for five level inverter fed DTFC-SVM of speed sensorless PMSM drive

A Simple Method for Stator Inductance and Resistance Estimation for PMSM at Standstill

An accurate stator resistance and inductance are necessary for high-performance permanent magnet synchronous motor (PMSM) control. The stator resistance and inductance can be estimated during motor standstill operation. This study proposes a standstill estimation method for the determination of dq-axis inductances and resistance of a PMSM drive system fed by a conventional voltage source inverter (VSI). The proposed method estimates both inductance and the rotor's position using the same algorithm, and knowledge of its initial position is not required. The d- and q-axis inductances were estimated by applying three short-time voltage pulses and measuring phase current peak values. The stator's resistance is estimated by monitoring the exponential decay process of the direct axis current. The method was verified by simulation and experiments conducted on two different PM synchronous motors. A good agreement of simulation and experimental results was obtained. Moreover, the proposed method is relatively simple and can identify stator resistance and inductance at any motor load condition. Compared to the existing parameter estimation strategies, the proposed estimation scheme has a relatively faster estimation time. Additionally, it is shown that the method accounts for the dead-time effect as well.

Sensor-less Monitoring of Induction Motor Temperature with an Online Estimation of Stator and Rotor Resistances Taking the Effect of Machine Parameters Variation into account

Sensor-less Monitoring of Induction Motor Temperature with an Online Estimation of Stator and Rotor Resistances Taking the Effect of Machine Parameters Variation into account

Influence of Selected Non-Ideal Aspects on Active and Reactive Power MRAS for Stator and Rotor Resistance Estimation

Mathematical models of induction motor (IM) used in direct field-oriented control (DFOC) strategies are characterized by parametrization resulting from the IM equivalent circuit and model-type selection. The parameter inaccuracy causes DFOC detuning, which deteriorates the drive performance. Therefore, many methods for parameter adaptation were developed in the literature. One class of algorithms, popular due to their simplicity, includes estimators based on the model reference adaptive system (MRAS). Their main disadvantage is the dependence on other machines’ parameters. However, although typically not considered in the respective literature, there are other aspects that impair the performance of the MRAS estimators. These include, but are not limited to, the nonlinear phenomenon of iron losses, the effect of necessary discretization of the algorithms and selection of the sampling time, and the influence of the supply inverter nonlinear behavior. Therefore, this paper aims to study the effect of the above-mentioned negative aspects on the performance of selected MRAS estimators: active and reactive power MRAS for the stator and rotor resistance estimation. Furthermore, improved reduced-order models and MRAS estimators that consider the iron loss phenomenon are also presented to examine the iron loss influence. Another merit of this paper is that it shows clearly and in one place how DFOC, with the included effect of iron losses and inverter nonlinearities, can be modeled using simulation tools. The modeling of the IM and DFOC takes place in MATLAB/Simulink environment.

Sensorless monitoring of induction motor temperature with an online estimation of stator and rotor resistances taking the effect of machine parameters variation into account

Sensorless monitoring of induction motor temperature with an online estimation of stator and rotor resistances taking the effect of machine parameters variation into account