Estimation Of Motor Parameters Research Articles

An Overview of Diagnosis Methods of Stator Winding Inter-Turn Short Faults in Permanent-Magnet Synchronous Motors for Electric Vehicles

This article provides a comprehensive overview of state-of-the-art techniques for detecting and diagnosing stator winding inter-turn short faults (ITSFs) in permanent-magnet synchronous motors (PMSMs) for electric vehicles (EVs). The review focuses on the following three main categories of diagnostic approaches: motor model-based, signal processing-based, and artificial intelligence (AI)-based fault detection and diagnosis methods. Motor model-based methods utilize motor state estimation and motor parameter estimation as the primary strategies for ITSF diagnosis. Signal processing-based techniques extract fault signatures from motor measured data across time, frequency, or time-frequency domains. In contrast, AI-based methods automatically extract higher-order fault signatures from large volumes of preprocessed data, thereby enhancing the effectiveness of fault diagnosis. The strengths and limitations of each approach are thoroughly examined, providing valuable insights into the advancements in ITSF detection and diagnosis techniques for PMSMs in EV applications. The emphasis is placed on the application of signal processing methods and deep learning techniques in the diagnosis of ITSF in PMSMs in EV applications.

Parameter Estimation and Predictive Speed Control of Chopper-Fed Brushed DC Motors

This paper presents an effective speed control method for brushed DC motors fed by a DC chopper using the concept of Finite Control Set-Model Predictive Control (FCS-MPC). As this control algorithm requires the parameters of the controlled object, the estimation of motor parameters is first performed by using two types of data. The first data includes the output speed response corresponding to the step input voltage to obtain the transfer function in the no-load regime. The second data consists of the motor speed and armature current when a load torque is applied to the motor shaft. The discrete-time equation of the motor armature circuit is used to obtain the future values of the armature circuit current and the motor speed. A cost function is defined based on the difference between the reference and predicted motor speed. The optimal switching states of the DC chopper are selected corresponding to the maximum value of the cost function. The performance of the proposed speed control algorithm is validated on an experimental system. The simulation and experimental results obtained show that the MPC controller can outperform the conventional proportional-integral (PI) controller.

Online Diagnosis and Discrimination of Stator Faults in Six-Phase Induction Motors Based on Voltage Symmetrical Components

In recent years, multiphase machines have been presented as a good alternative to the classical three-phase machines, mainly due to their ability to operate smoothly under fault conditions. Due to multiple stator phase arrangements, existing fault diagnostic (FD) methods are not sufficiently universal and cannot be applied to the multiple configurations of multiphase machines. In this regard, the current research proposes a universal and effective online diagnostics technique based on the computation and monitoring of a relevant severity factor, which is defined as the ratio of the zero, negative and positive voltage symmetrical components. The Short Time Least Square Prony’s (STLSP) approach is used to implement this method online in LabVIEW environment. In addition to the wide applicability of the method, the obviation of any motor parameter estimation and limited requirements regarding variables measurements make the proposed approach extremely advantageous for motors with high number of phases. This article addresses at stator faults in symmetrical six-phase induction machines (6PIM). The proposed method’s experimental results on six-phase motor drive platforms demonstrate the excellent generalization capability of the proposed method, as well as high accuracy and robustness, along with the ability to accurately distinguish between the occurrence of Inter-Turn Short Circuit (ITSC) faults and the presence of Unbalance Supply Voltage (USV) conditions. A 6PIM is put through its paces in both healthy and faulty conditions. The obtained results demonstrate the effectiveness of the proposed method for diagnosing the occurrence of ITSC faults and USV operating conditions with high reliability, fastness and high precision.

Encoder position feedback based indirect integral method for motor parameter identification subject to asymmetric friction

Servo motors have been widely used in the automation industry for many years. Its control performance will directly affect the quality of the produced products. For the design of the servo motor controller, accurate modeling and parameter estimation will be one of the key design steps. Although a considerable amount of literature has discussed the modeling and parameter identification of servo motors, most of them focus on symmetrical friction parameter models and assume that motor velocity information is available. Based on the practical experimental examination, the displacement movements of the servo motor driven by harmonic input appear to be a drift phenomenon, which concludes that the friction force should be asymmetric. Moreover, coarse encoder quantization error during the practical measurement is also a problem that causes noisy velocity and acceleration estimations. These measurement imperfections would lead to inaccuracy of parameter identification results. In order to solve these issues, this paper presents an asymmetric friction model and an indirect integral method (IIM). The asymmetric friction model is able to capture the nonlinear position drifting phenomenon. For the proposed IIM, the use of velocity information is avoided. Moreover, an optimization algorithm is developed to minimize the quantized output prediction. Compared with the direct difference method (DDM) and the filtered regression model (FRM) in the existing literature, the numerical simulations, as well as the experimental validations, reveal that the proposed IIM has better parameter estimation performance than both the DDM and the FRM.

Online Interturn Short-Circuit Fault Diagnosis in Induction Motors Operating Under Unbalanced Supply Voltage and Load Variations, Using the STLSP Technique

It is well known that the most common reason for electrical machines breakdown is the stator windings’ fault occurrence. Indeed, this type of fault represents almost 40% of faults occurring in induction machines. One of the major causes of stator defects is the occurrence of interturn short-circuit (ITSC) faults that have critical and dangerous effects on the motor itself, as well as on the related electrical equipment. Therefore, early detection and a precise severity estimation of the occurrence of ITSC faults for all working conditions can prevent failure breakdowns and increase reliability and safety of industrial facilities. In this context, this article proposes an efficient online diagnostics method based on calculating and monitoring a pertinent severity factor defined as the ratio between the zero and positive voltage symmetrical components. The online implementation of this method is performed on a LabVIEW environment, using the short-time least square Prony's (STLSP) method. It does not need any estimation of motor parameters and it requires only voltage sensors. Several tests under healthy and faulty conditions are carried out on a three-phase 3-kW induction motor. The obtained results demonstrate the effectiveness of the proposed method for diagnosing the occurrence of ITSC faults with high reliability, fastness, and high precision, even under load variations or unbalanced supply voltage.

Induction Motor Parameter Estimation Using Harmony Search Algorithm

This study uses the Harmony Search Algorithm to carry out induction motor parameter estimation (HSA). In order to maximize the starting torque and minimize copper losses in the stator and rotor, the parameters rotor resistance R2 and rotor reactance X2 are determined. For parameter estimation of an induction motor, both conventional and intelligent methods are being examined. It is then subjected to the harmony search algorithm to obtain maximum beginning torque and efficiency, respectively.

Adaptive Contactor – A New Scheme to Improve Induction Motor Immunity to Voltage Sags

One of the consequences of voltage sags is unexpected motor trips or stalls. This is caused by the simple voltage-magnitude-based tripping logic used in the motor contactors. Since a motor has momentum during a voltage sag, a deep voltage sag with a short duration does not necessarily lead to motor misoperation. Based on this reasoning, a novel motor contactor operation scheme is proposed in this paper. The proposed scheme takes into account both the magnitude and duration of a voltage sag plus the momentum of the rotor, in real-time, to decide if a motor will be tripped. The core idea of the scheme is to utilize the critical clearance time (CCT) of motors as the tripping time of AC contactors. The proposed scheme combines an offline analysis and an online calculation to minimize the computation burden in real-time operation. An improved motor parameter estimation method is also proposed to ensure the accuracy of the calculated CCT. The performance of the proposed scheme is evaluated and demonstrated through comparative case studies. Moreover, some practical issues are discussed to facilitate the real implementation.

Synchronous Reluctance Motor Parameter and State Estimation Using Extended Kalman Filter and Current Derivative Measurement

The synchronous reluctance motor is becoming a very attractive alternative to the ac induction machine. This is due to the lack of rare-earth metals in their construction and a higher efficiency, which was brought about by recent progress in rotor design. However, in order to achieve an efficient and low-cost operation of the synchronous reluctance motor drive, an adaptive sensorless algorithm should be utilized to cope with machine nonlinearities. This article describes an adaptive observer, which can provide an estimation of rotor position and speed, as well as core loss and inductance parameters. A modified pulsewidth modulation (PWM) switching scheme and a current derivative measurement method are proposed, together with an extended Kalman Filter design. Experimental results are shown to demonstrate the method performance and feasibility.

Enhanced Model Predictive Direct Torque Control Applied to IPM Motor With Online Parameter Adaptation

This paper presents an improved model-based predictive direct torque control (MPDTC) to improve torque accuracy and reduce torque ripples which is a major issue in conventional direct torque control (DTC). Hysteresis controllers and traditional DTC switching tables are replaced by a model predictive controller to achieve an online optimization for voltage space vector selection and optimal duty ratio modulation method for torque ripple reduction. In order to provide an accurate motor model for MPDTC, novel offline and online motor parameter estimation methods are proposed to improve performance of the proposed MPDTC. The proposed parameter estimation adopts Popov's hyper stability theorem to estimate accurate motor parameters, such as stator resistance, stator inductance and rotor flux linkage, which are critical for torque and flux estimation. The parameter adaptive MPDTC is verified by a hardware in the loop emulation platform, and experiment result is demonstrated using a dynamometer test bench, which therefore proves the feasibility of the proposed method.

On-Line Parameter Identification of a Squirrel Cage Induction Motor

Based on the principle of coordinate transformation, a mathematical model of induction motors in the three-phase stationary coordinate system was transformed to that in the synchronous rotating coordinate system. For motors with low-speed rotors, the multi-variable, high-order, strong-coupling and nonlinear equation of motors was further transformed to a linear equation. However, it was found difficult to solve the deduced linear equation through the method of least squares due to its high singularity. The steady-state stator resistance could be predetermined by temperature revision on the stator resistant in the motor nameplate. Identification of four motor parameters (Ls,M, Rr, Lr) was followed by the recursive least-squares method. Simulations of motor parameter estimation were carried out in Simulink of Matlab. The recursive least-squares method was used to deal with the measured data. The results demonstrate that the real-time electrical parameters (Ls, σ, Tr) can be accurately predicted. Given the assumption of Ls = Lr in the classical motor theory, the other three electrical parameters (M, Rr, Lr) can also be identified. The identification of these parameters is of great importance for the analysis, control and fault diagnosis of induction motors.

A Torque Compensation Control Scheme of PMSM Considering Wide Variation of Permanent Magnet Temperature

This paper presents a torque control method that compensates the reduction of the power output of a permanent magnet synchronous motor (PMSM) caused by the wide variation of rotor-mounted permanent magnet temperature. The characteristics of the permanent magnet according to temperature were analyzed, and the need for motor parameter estimation was identified. Numerical and computational techniques using the steepest descent method and the affine projection algorithm were used for online motor parameter estimator. The estimated parameters were used to achieve a torque compensation control scheme based on field-oriented control of PMSM considering the wide variation of permanent magnet temperature. The proposed control scheme was verified by the dynamometer test of the test motor under each rotor-mounted permanent magnet temperature condition.

Parametric Identification of an Induction Motor Based on a Phase-Locked-Loop Frequency Control Algorithm

A method of identification of induction motor parameters based on a phase-locked loop frequency control algorithm was considered. The existing identification methods of the equivalent circuit parameters were analyzed and classified. The methods operating in real time and in the adaptation mode of electric drive during the first operative startup of electric drive were distinguished. In addition, using the methods of active identification, in which an additional test information signal is injected into the power circuit of an electric drive, is unacceptable during electric drive operation and they are used at the time of prior adjustment of electric drive until its operative startup or for a short time to adaptation of changing parameters. An evaluation algorithm of motor parameters was proposed, and functional and structural diagrams of parametric identification system were developed. The developed identification method based on a phase-locked loop frequency control algorithm is constructed upon leveling of the phase difference between the real (measured) stator current vector and the stator current vector calculated by the model. The simulation results of the implemented parametric identification system are presented. According to the results of model experiments, the error of estimation of motor parameters did not exceed 1%. The proposed parametric identification system can be used as part of a vector control system to determine the coefficients of internal regulators of electric drives.

Temperature Estimation of Stator Winding in Permanent Magnet Synchronous Motors Using d-Axis Current Injection

This paper presents a stator winding temperature detection method for permanent magnet synchronous motors (PMSMs) using a motor parameter estimation method. PMSM performance is highly dependent on the motor parameters. However, the motor parameters vary with temperature. It is difficult to measure motor parameters using a voltage equation without additional sensors. Herein, a stator winding temperature estimation method based on a d-axis current injection method is proposed. The proposed estimation method can be used to obtain stator temperatures and to achieve reliable operation. The validity of the proposed method is verified through simulations and experimental results.

Deadbeat control based on a multipurpose disturbance observer for permanent magnet synchronous motors

Robustness against parameter mismatches and position-sensorless operation are two important research topics for permanent magnet synchronous motor (PMSM) drives. Usually, the existing observers are designed for achieving a specific function. While here, both the above two functions are integrated into the proposed sliding-mode disturbance observer: (i) if a position sensor is equipped, accurate current regulation can be achieved by deadbeat predictive current control despite mismatched motor parameters; (ii) if the position sensor is not equipped but with a good estimation of motor parameters, the observer can serve as a back electromotive force estimator. Then, the rotor position can be extracted for position-sensorless control. Usually, a low-pass filter is required to suppress high-frequency noises in the conventional sliding-mode observer. This inevitably leads to phase delay in the estimation. By comparison, a complex coefficient filter is inherently embedded in the proposed method, which can provide accurate estimation without phase delay or magnitude error. Experimental results obtained from a 2.4 kW PMSM drive platform indicate that high-performance current control can be achieved with good robustness for position sensor-based operation. Also, rotor position can be accurately estimated with good steady and dynamic performance for position-sensorless operation.

Self-Sensing Induction Motors for Condition Monitoring

Motors have been exploited as their own sensors for diagnostic and operating conditions at least, since the dawn of modern computing. Contracting systems theory offers a new level of precision in detecting small parameter and state changes in an electric machine for load fault detection and diagnostics from the motor terminals. The presented offline method successfully solves the motor inverse problem to reconstruct the characteristic instantaneous angular speed and load torque signals of the motor during periodic operation. The solution includes a motor parameter estimation step that reflects the specific temperatures and magnetic saturation of the motor during data acquisition. This identification or inversion method is suitable for induction motors driving periodic loads with and without rotor angle-dependent loading. A practical condition monitoring application is demonstrated: valve and cylinder fault detection in reciprocating compressors.

Induction motor parameter estimation using disrupted black hole artificial bee colony algorithm

The most widespread motors in industries are induction motors nowadays. The design, performance evaluation and control of induction motors are based on circuit parameters. The accurate measurement of electrical parameters, like resistance (or reactance), is a tedious job. So, researchers have found induction motor parameter estimation as a significant optimisation aspect. Though conventional techniques produce good results, the swarm intelligence-motivated techniques produce still better results for real-world optimisation problems nowadays. In this paper, swarm intelligence-motivated artificial bee colony (ABC) algorithm is modified with physics's phenomena. The proposed algorithm is named as disrupted black hole ABC (DBHABC) algorithm. Further, this proposed algorithm is applied for optimising induction motor parameter estimation. The obtained outcomes reveal that DBHABC may be a good choice for induction motor parameter estimation.

Induction Motor Parameter Estimation Using Sparse Grid Optimization Algorithm

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 noninvasive 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 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.

Induction Motor Stator Fault Detection by a Condition Monitoring Scheme Based on Parameter Estimation Algorithms

This article presents a simple, low-cost, and effective method for the early diagnosis of stator short-circuit faults. The approach relies on the combination of an induction motor mathematical model and parameter estimation algorithm. The kernel of the method is the efficient search for the characteristic parameters that indicate stator short-circuit faults. However, the non-linearity of a machine model may imply multiple local minima of an objective function implemented in the estimation algorithm. Taking this into consideration, the suitability of two industry-proven optimization algorithms (pattern search algorithm and genetic algorithm) as applied in the proposed condition monitoring method was investigated. Experimental results show that the proposed diagnosis method is capable of detecting stator short-circuit faults and estimating level and location of faults. The study also indicates that the proposed method is robust to motor parameters offset and unbalanced voltage supply. Application of the pattern search algorithm is suitable for a continuous monitoring system, where the previous result can be used as starting point of the new search. The genetic algorithm requires longer computation time and is suitable for the offline diagnostic system. It is not sensitive to the starting point, and achieving global solution is guaranteed.

Accuracy Enhancement of Parameter Estimation and Sensorless Algorithms Based on Current Shaping

Dead time is typically incorporated in voltage source inverter systems to prevent short circuit cases. However, dead time causes an error between the output voltage and reference voltage. Hence, voltage equation-based algorithms, such as motor parameter estimation and back electromotive force (EMF)-based sensorless algorithms, are prone to estimation errors. Several dead-time compensation methods have been developed to reduce output voltage errors. However, voltage errors are still common in zero current crossing areas, and an effect of the error is much worse in a low speed region. Therefore, employing voltage equation-based algorithms in low speed regions is difficult. This study analyzes the conventional dead-time compensation method and output voltage errors in low speed operation areas. A current shaping method that can reduce output voltage errors is also proposed. Experimental results prove that the proposed method reduces voltage errors and improves the accuracy of the parameter estimation method and the performance of the back EMF-based sensorless algorithm.

Squirrel-Cage Induction Motor Parameter Estimation Using a Variable Frequency Test

This paper presents a method for squirrel-cage induction motor parameter estimation using a phase-to-phase standstill variable frequency test. The measured resistance and reactance at different frequencies are the data of the minimization error function to be minimized for single- and double-cage model parameters estimation. It is observed that the single-cage model is unable to fit the measured data for frequencies above several tenths of Hertz, whereas the double-cage model fits the measured data accurately in all the frequency ranges (from 0 to 150 Hz). The single- and double-cage estimated parameters are validated by comparison with data from two additional tests: 1) steady-state torque and current measurement test at different speeds; and 2) dynamic free-acceleration test. Again, the agreement between measured and predicted torque (in the first test) and current (in both tests) is satisfactory only for the double-cage model.