Sensorless Speed Estimation Research Articles

Sensorless Speed Estimation of Induction Motors through Signal Analysis Based on Chaos Using Density of Maxima.

Three-phase induction motors are widely used in various industrial sectors and are responsible for a significant portion of the total electrical energy consumed. To ensure their efficient operation, it is necessary to apply control systems with specific algorithms able to estimate rotation speed accurately and with an adequate response time. However, the angular speed sensors used in induction motors are generally expensive and unreliable, and they may be unsuitable for use in hostile environments. This paper presents an algorithm for speed estimation in three-phase induction motors using the chaotic variable of maximum density. The technique used in this work analyzes the current signals from the motor power supply without invasive sensors on its structure. The results show that speed estimation is achieved with a response time lower than that obtained by classical techniques based on the Fourier Transform. This technique allows for the provision of motor shaft speed values when operated under variable load.

Relegated Thrust Ripples for Linear Induction Motors Based-Four Voltage Vectors Finite-Set Predictive Control and Model Reference Adaptive System

—These days, there are extensive signifies to the evolution of newfound control procedures like model predictive control. The finite-state predictive thrust control (FSPTC) for LIMs is still not deftly addressed by scholars and limited research was acted to exploit the FSPTC for LIM drive systems. Therefore, the current study proposes a novel finite-state predictive thrust control (NFSPTC) for linear induction motors (LIMs) with reduced numbers of voltage vectors (VVs) in the prediction phase. The NFSPTC utilizes the conventional direct thrust control (DTC) concept to reduce the required VVs from eight to only four which consist of two active vectors and two zero vectors. Decreasing the number of predicted VVs leads to a significant reduction in computational time. Moreover, the weighting factor is removed to reduce the effort in selecting the best weighting factor. Since the LIM drive system can be succeeded by developing a sensorless speed estimation, the linear speed is estimated depending on the model reference adaptive system (MRAS) before using actual sensors; thus, low cost can be attained. The suggested control approach is validated by simulation proofs based on a 3-kW arc machine. The gained findings clarified the effectiveness of the proposed four VVs control over the eight VVs procedure in dipping the thrust ripple. Besides, using the four VVs control under variable speed and fixed thrust load, the execution time was decreased by 50%. Furthermore, the proposed control procedures ensued in maintaining the primary flux linkage constant alongside succeeding a glowing-tracking speed profile throughout the tackled speed and thrust load variations.”

Optimized fractional order PID controller with sensorless speed estimation for torque control in induction motor

With rising concern on environmental protection, the exploitation of EV has been widely focused. The IM is said to be the best in comparing varied electric motors for EV applications due to its lower price and robustness. In addition, sensor-less controlling schemes like DTC and FOC are found to be appropriate for crucial appliances owing to their energy savings, improved efficiency, and reliability. The “Optimized Fractional Order Proportional Integral Controller (FoPID)” with a sensor-less speed estimate mechanism is proposed in this work. Additionally, the parameters of FoPID are adjusted using a brand-new hybrid method called the Wolf based Elephant Herding method (W-EHA), which combines the ideas of Grey wolf optimization (GWO) model and Elephant herding optimization (EHO). The superiority of the recommended technique is finally shown in terms of controller performance and error. The RMSE obtained by the developed model at speed = 100 is 3.344, which is 53.89 %, 24.22 %, 1.09 %, and 38.96 % superior to existing approaches like P-controller, MFI-DA + FoPID, EHO-FoPID, and GWO-FoPID respectively.

A Precise, General, Non-Invasive and Automatic Speed Estimation Method for MCSA Diagnosis and Efficiency Estimation of Induction Motors

Efficiency estimation and diagnosis via MCSA require precise knowledge of speed. In an industrial environment, speed must be obtained with a non-invasive, automatic and general method. Recent studies have shown that Sensorless Speed Estimation techniques based on detecting Rotational Frequency Sideband Harmonics (RFSHs) or Rotor Slot Harmonics (RSHs) are best suited to these purposes. RFSHs-based methods are easier to apply as they only depend on the number of poles. RSHs-based are much more accurate due to their wider bandwidth. Yet, their use is not trivial as they require to identify the RSHs family, assign to each RSH its order of the current harmonic ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\nu$</tex-math></inline-formula> ) and determine the number of rotor slots ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R$</tex-math></inline-formula> ), a rarely known parameter. This paper ends with this trade-off between accuracy and applicability by proposing a novel RSHs-based technique that, for the first time in technical literature, eliminates the need to estimate the number of rotor slots and provides a reliable and automatic procedure to locate the RSHs family and determine their <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\nu$</tex-math></inline-formula> indices. Finally, the method is validated under all types of conditions and motor designs, by simulations, lab tests and with 105 industrial motors, highlighting its high accuracy (errors below 0.05 rpm), and applicability.

Implementation and performance evaluation of controller signal embedded sensorless speed estimation for wind turbine doubly fed induction generators

This paper reports a novel sensorless speed estimation method for wind turbine doubly fed induction generators that is highly non-invasive and utilises the readily available generator drive controller signals. A generalised analytic analysis is first presented that allows the assessment, identification and study of the controller signal spectral content needed to allow the establishment of real time speed estimation. An overlapping window parabolic interpolation algorithm is then proposed for tracking the controller signal speed dependent spectral content, with a view to provision of an increased real-time estimation rate. The reported method is implemented on a vector controlled doubly fed induction generator laboratory system and tested in transient operating regimes representative of wind generator application. The test results show that high accuracy high estimation rate is achievable in field application characteristic transientdynamics.

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

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.

Implementation of disturbance observer for sensorless speed estimation in induction motor

The use of speed sensors in induction motors is considered to be less effective because of the high price and diminishing reliability. Over time, a speed estimator was developed to increase the speed of the sensor. This paper describes the application of a disturbance observer algorithm as an estimator of the speed of an induction motor. In this case, the estimator is seen as an embedded system that only uses current information to measure speed of induction motor. To test the performance of the disturbance observer-based estimator, a comparison was made with the estimator based on the extended Kalman filter. From the experiment, it is found that the speed estimation using the disturbance observer has a smaller root mean square error in the low-speed operation than one of the extended Kalman filter, i.e. the root mean squared error (RMSE) value is below 4% in the range of 250 revolution per minute (RPM)–600 RPM.

ANN-based position and speed sensorless estimation for BLDC motors

BLDC motor applications require precise position and speed measurements, traditionally obtained with sensors. This article presents a method for estimating those measurements without position sensors using terminal phase voltages with attenuated spurious, acquired with a FPGA that also operates a PWM-controlled inverter. Voltages are labelled with electrical and virtual rotor states using an encoder that provides training and testing data for two three-layer ANNs with perceptron-based cascade topology. The first ANN estimates the position from features of voltages with incremental timestamps, and the second ANN estimates the speed from features of position differentials considering timestamps in an acquisition window. Sensor-based training and sensorless testing at 125–1,500 rpm with a loaded 8-pole-pair motor obtained absolute errors of 0.8 electrical degrees and 22 rpm. Results conclude that the overall position estimation significantly improved conventional and advanced methods, and the speed estimation slightly improved conventional methods, but was worse than in advanced ones.

Sensorless Speed Estimation for the Diagnosis of Induction Motors via MCSA. Review and Commercial Devices Analysis

Sensorless speed estimation has been extensively studied for its use in control schemes. Nevertheless, it is also a key step when applying Motor Current Signature Analysis to induction motor diagnosis: accurate speed estimation is vital to locate fault harmonics, and prevent false positives and false negatives, as shown at the beginning of the paper through a real industrial case. Unfortunately, existing sensorless speed estimation techniques either do not provide enough precision for this purpose or have limited applicability. Currently, this is preventing Industry 4.0 from having a precise and automatic system to monitor the motor condition. Despite its importance, there is no research published reviewing this topic. To fill this gap, this paper investigates, from both theoretical background and an industrial application perspective, the reasons behind these problems. Therefore, the families of sensorless speed estimation techniques, mainly conceived for sensorless control, are here reviewed and thoroughly analyzed from the perspective of their use for diagnosis. Moreover, the algorithms implemented in the two leading commercial diagnostic devices are analyzed using real examples from a database of industrial measurements belonging to 79 induction motors. The analysis and discussion through the paper are synthesized to summarize the lacks and weaknesses of the industry application of these methods, which helps to highlight the open problems, challenges and research prospects, showing the direction in which research efforts have to be made to solve this important problem.

Rotational speed monitoring of brushed DC motor via current signal

Sensor-less speed estimation of brushed DC motor is preferred for dynamic control and state monitoring. Ripple-based and model-based methods are widely applied for sensor-less speed estimation. This paper firstly offers a ripple-based technique, analyzes their features and performance, then presents a modified Kalman filter to fuse the ripple-base and model-based results. The two source data are fused through modification of the noise covariance matrices of the conventional Kalman filter. Experimental validates the proposed algorithm. Test shows that the proposed method reduces speed estimation to less 3% and owns roughly 8 times of accuracy comparing with conventional ripple-based and model-based methods. The proposed method is suitable for speed monitoring of industrial applications involving brushed DC motor.

Efficient Sensorless Speed Estimation of Electrical Servo Drives Using a Full-Order Nonsingular Terminal Sliding Mode Observer

This paper proposes an efficient sensorless speed estimation approach for electric servo drives based on the full-order nonsingular terminal sliding mode observer (FONTSM) with the application of DC motor drives. In this method, a specific full-order terminal sliding mode manifold is utilised for the observer design which results in the elimination of the chattering and avoiding the singularity phenomenon of conventional and terminal sliding modes. Here, speed and armature back emf can be directly estimated from the relevant observer’s inputs which are continuous instead of being discontinuous high-frequency “switching” signals. The efficiencies and advantages of this approach have been proven and validated in both simulation and experimental results.

Stator q-axis voltage error based sensorless speed estimation of field oriented vector controlled induction motor without using voltage transducer

The purpose of the study is to develop a high-performance speed sensorless indirect field oriented control for induction motors (IMs). The proposed method is a novel Model Reference Adaptive System (MRAS) and needs only steady-state stator q-axis voltage equation to estimate rotor speed. And also, the proposed speed estimator algorithm removes the voltage transducer requirement in calculations since the algorithm compares the current requlator PI controller output with the calculated q-axis stator voltage. So the system does not need a reference loop since the calculated voltage I adaptive sub-model is directly compared with controller output. This simple equation does not require any rotor parameter and this makes the system immune to the variation of rotor parameters. Moreover, this unique calculation eliminates the requirement of flux estimation thus, the method is less sensitive to pure integration problems. This makes the estimator quite accurate at very low and zero speeds. Moreover, the suggested MRAS technique eliminates the voltage transducer measurement noises so, the low speed accuracy of the speed estimator is increased. Which are validated in simulations using MATLAB/SIMULINK.

Modified LUT-based DTC of NPC 3-Level Inverter fed Sensorless IPMSM Drive with DC Link Voltage Balance

In the proposed work, a modified look-up table-based direct torque control (DTC) scheme is reported for a neutral point-clamped three-level inverter-fed interior permanent magnet synchronous motor drive. In the classical DTC scheme, there are significant torque and flux ripples in the drive due to low number of available switching states. To reduce the oscillations in torque and flux, a three-level inverter-operated IPMSM drive is discussed. In a three-level inverter, the increased count of active voltage vectors is results in more sinusoidal output waveforms. Moreover, stator flux plane has 12 sectors in the proposed work unlike classical DTC. Besides, the higher level of flux and torque hysteresis comparators is implemented. DC link capacitor voltage balancing scheme is also instigated to minimize voltage stress on semiconductor devices, improve stator current THD and increased capacitor life. Based on the sector information, flux and torque hysteresis output and capacitor voltage balance logic, an improved LUT is established and explained for a three-level DTC scheme. Consequently, improved stator current (THD) and decrement in torque and flux ripples are observed. To make the system robust, model reference adaptive control (MRAC) based sensorless speed estimation algorithm is realized. The proposed DTC scheme is compared with a two-level inverter-fed and a three-level-inverter-fed DTC scheme without DC link voltage balance strategy. To carry out the analysis, MATLAB/Simulink environment is utilized. Moreover, to validate the simulation results, experimental analysis of the proposed technique is carried out using dSPACE1104.

Adaptive non-linear high gain observer based sensorless speed estimation of an induction motor

An efficient sensor-less speed estimator, based on an adaptive non-linear high gain observer (HGO) which uses only the measured stator currents and control voltages in the presence of measurement noise, is proposed to estimate the speed of an induction motor. The proposed observer is an improved version of the common HGOs proposed in the literature. The observer gain, involving the use of a scalar time-varying design parameter governed by the Riccati differential equation, guarantees the best compromise between the fast convergence of the states and noise rejection with such adaptation of gains that the internal states of the observer exert low influence during the transient period and thus prevent system peaking. The design methodology of the proposed observer is given and its stability is studied. Results of simulation studies demonstrate that robustness and efficiency of the proposed observer are maintained under critical operating conditions, even where the efficiency of the fixed gain observer is decreased.

A new stator voltage error-based MRAS model for field-oriented controlled induction motor speed estimation without using voltage transducers

A sensorless speed estimation method for performing the indirect field-oriented control (IFOC) of induction motors (IMs) is presented in the current research. The method uses the stator voltage as state variable but eliminates the voltage transducers. Traditional MRAS methods use two sets of equations to estimate the motor speed. However, the proposed system utilizes the q-axis current regulator proportional–integral (PI) control output $$ \left( {V_{\text{sq}}^{*} } \right) $$ in the reference model as a new Model Reference Adaptive System (MRAS) technique and compares this value with the estimated voltage $$ \left( {\hat{V}_{\text{sq}} } \right) $$ in an adaptive model. The method calculates the steady-state q-axis stator voltage in adaptive model, so, the calculation burden in the estimation loop is significantly reduced. Choosing the q-axis stator voltage as state variable removes requirement of the rotor parameters in calculations, and therefore, the system becomes resistant to the alteration in rotor parameters. Furthermore, the mentioned special calculation ensures the elimination of the need for flux estimation, so the speed estimation technique becomes resistant to integral and derivative calculation-based problems. The MATLAB/Simulink simulation results and experiments under various operation conditions verifies the effectiveness of the studied technique.

Sensorless Predictive Current Control of PMSM EV Drive Using DSOGI-FLL Based Sliding Mode Observer

Conventional back electromotive force (EMF) estimation based active flux concept for sensorless control has various limitations due to pure integrator problems and performance deterioration at low speeds. The fundamental back EMF component extraction with a conventional low pass filter, is difficult due to the presence of lower order harmonics, dc offset, saturation, etc., and causes significant phase delay in the estimated position and speed. This article deals with a sensorless speed and position estimation technique for a surface mounted permanent magnet synchronous motor (SPMSM) based electric vehicle (EV) drive using a sliding mode observer (SMO) with a dual second order generalized integrator frequency locked loop (DSOGI-FLL). A novel method of utilizing the DSOGI-FLL, along with feedforward term is shown to achieve better harmonic elimination of estimated back EMF components and faster dynamic speed estimation. A delay compensated predictive current controller-based PMSM EV drive with the presented estimation technique is used for improved dynamic response. This system is simulated using Simulink and its efficacy is experimentally validated using dSPACE 1202. Test results show that the DSOGI-FLL-based SMO has better performance compared to the conventional technique.

Design of a Fuzzy Logic-based MPPT Controller for a PV System Employing Sensorless Control of MRAS-based PMSM

The permanent magnet synchronous motors (PMSM) are widely employed in industrial, robotic, water pumping and HVAC applications due to their numerous benefits such as small size, high-energy efficiency, high performance, low inertia and the ability to operate in full load at low speeds. In case the PMSM drive system is supplied from photovoltaic (PV) modules, it can be a perfect match for water pumping or HVAC applications. In such a system, in order to extract full energy from PV modules, a maximum power point tracking (MPPT) algorithm must be employed. This article presents a PV system-fed PMSM drive system with sensorless speed control. The proposed system consists of two main parts. The first part deals with MPPT algorithm based on fuzzy logic controller and the second part deals with PMSM drive system with a sensorless speed estimator by using Model Reference Adaptive System (MRAS) approach to eliminate the use of an encoder. The operation of PMSM is accomplished by using the vector control method to obtain a similar dynamic of the DC motor. The overall system is modelled in Matlab/Simulink environment and simulation results are collected under various operating conditions.

Sensorless Speed Estimation of Brushless Doubly-Fed Reluctance Generator Using Active Power Based Mras by Pi and Anfis Controller

Sensorless Speed Estimation of Brushless Doubly-Fed Reluctance Generator Using Active Power Based Mras by Pi and Anfis Controller

Sensorless Speed Estimation of an Inverter-Fed Induction Motor Using the Supply-Side Current

The supply-side current of an inverter-fed induction motor (IM) is easily accessible and less affected by noises than the motor stator current. This paper proposes a novel sensorless speed estimation method for an IM using the spectrum of the supply-side current. However, due to the influence of fundamental supply frequency, it is difficult to carry out the speed detection straight from the supply-side current signal. In this paper, an analytic model for characterizing the supply-side current of an inverter-fed IM using the modulation theory and the switching function concept is developed. Then, the Hilbert transform is applied to extract the speed harmonic component from the supply-side current. Finally, an interpolated Goertzel algorithm is put forward to enhance the estimation precision of IM speed. The performance of the proposed sensorless speed estimation method is proved by the speed estimation results obtained from hardware experiments of an IM fed by an inverter under different load conditions.

Sensorless Speed Estimation of Brushless Doubly-Fed Reluctance Generator Using Active Power Based MRAS

This paper proposes an active power based model reference adaptive system for sensorless speed estimation of primary field oriented brushless doubly-fed reluctance generator (BDFRG). In this regard, the active power associated with secondary winding of BDFRG is used for the process of speed estimation. BDFRG is more reliable and requires less maintenance. Its structural advantages demonstrate higher working efficiency and lower losses as compared to equivalent doubly-fed induction machines. Moreover, active power of BDFRG can be controlled by suitable speed regulation, which in turn can be achieved by a low rating bidirectional converter connected to the stator secondary (i.e., control) winding. These inherent features make BDFRG as a prospective candidate in the applications like wind and tidal power generation. The efficacy of the proposed control technique is established by a 1.6 kW BDFRG in Matlab /Simulink. A dSPACE-1103 based laboratory prototype is used for the hardware validation.