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This study presents a simple sensorless algorithm based on the high-frequency signal injection for an interior permanent magnet synchronous motor. The sensorless drive using a square-wave-type injection signal has an enhanced control bandwidth and reduced acoustic noise owing to the reduction of filters and availability of high injection frequency. However, this method still needs discrete filters to extract the fundamental and the injected frequency component currents; so it has a limitation in enhancing the sensorless control performance. Therefore this study proposes a simple algorithm, which eliminates these filters and further simplifies the signal process for estimating the rotor position. As a result, the overall sensorless control can be implemented easily without any filters while providing an enhanced dynamics. Additionally, a detection method of an initial rotor position for start-up by using the same square-wave-type voltage injection is introduced. The experimental result shows that the speed control bandwidth in the sensorless drive simplified by the proposed algorithm becomes very close to the one achieved in sensored drives.

A new linear regression form is derived for a flux observer and a position observer is designed. In general, the observability of the permanent-magnet synchronous motor is lost at zero speed. In this work, the proposed regressor vector contains current derivative terms in both directions (dq-axis), and it gives the chance for the model-based flux observer to operate at zero speed. When an excitation signal is injected into d and q axes with the proposed flux observer, it helps to satisfy the persistent excitation condition in the low-speed range. Therefore, the sensorless performance of the model-based is improved greatly, even at zero speed. However, it appears with a disturbance term, which depends on the derivative of the d-axis current. Thus, the disturbance does not vanish when an excitation signal is injected. In this work, the disturbance term is also taken care of in constructing an observer. It results in an observer which allows signal injection. Thus, high frequency signal can be injected in the low speed region and turned off when it is unnecessary as the speed increases. This model-based approach utilizes the signal injection directly without recurring to a separate high frequency model. In other words, it provides a seamless transition without switching to the other algorithm. The validity is demonstrated by simulation and experimental results under various load conditions near zero speed.

This paper proposes a novel maximum power point tracking algorithm applied to photovoltaic systems. The proposed method uses the derivative of power versus voltage to define the tracking path and has the advantage of requiring only a voltage sensor to be implemented. The absence of the current sensor and the auxiliary circuitry employed for conditioning the current signal imply cost reduction, configuring the main contribution of the proposed method, whose performance is kept close to the classical incremental conductance method, even with the reduced number of components. A DC-DC zeta converter is introduced in the content of this work as an interface between a photovoltaic array and a resistive load. The paper describes the operating principle and presents the mathematical formulation related to the proposed algorithm. Interesting simulation and experimental results are presented to validate the theory by comparing the proposed method with its traditional version under several scenarios of solar irradiance and temperature.

The paper presents a sensorless control of permanent magnet synchronous generator (PMSG) in a variable-speed wind energy conversion system. The system of wind turbine consists of PMSG and back-to-back power converter. The back-to-back converter system is composed of machine side converter (MSC) and grid side converter (GSC). In the control of MSC and GSC the methods of vector control have been applied. For operation of MSC the method of Rotor Field Oriented Control (RFOC) with MPPT algorithm has been used. For estimation of angular rotor position and angular speed the flux linkage estimator with synchronous frame phase locked loop (SF-PLL) has been used. In the control of GSC the method of Voltage Oriented Control (VOC) has been considered. Simulation studies have been carried out in order to evaluate the system of sensorless strategy. The results of simulation studies demonstrate the high efficiency and high accuracy of the sensorless control system considered.

This paper introduces a novel approach to speed-sensorless predictive torque control (PTC) in an autonomous wind energy conversion system, specifically utilizing an asymmetric double star induction generator (ADSIG). To achieve accurate estimation of non-linear quantities, the Gaussian Process Regression algorithm (GPR) is employed as a powerful machine learning tool for designing speed and flux estimators. To enhance the capabilities of the GPR, two improvements were implemented, (a) hyperparametric optimization through the Bayesian optimization (BO) algorithm and (b) curation of the input vector using the gray box concept, leveraging our existing knowledge of the ADSIG. Simulation results have demonstrated that the proposed GPR-PTC would remain robust and unaffected by the absence of a speed sensor, maintaining performance even under varying magnetizing inductance. This enables a reliable and cost-effective control solution.

The study discusses the problem of speed and flux estimation for the induction motor (IM) drive and presents the design of two sliding mode observers (SMO) with compound manifolds. Both observers are developed using the IM model in the stationary reference frame. The first observer is a single-manifold SMO – it estimates the motor fluxes and yields an approximate value of the speed; however, it is not a converging observer. The single-manifold design is transformed into a double-manifold observer by adding extra feedback terms – this leads to a fully convergent observer that also yields an accurate estimate of the speed. The observers are designed using compound manifolds, which are chosen as a combination of the estimated fluxes and current mismatches. Observers with compound manifolds have been rarely investigated because they cannot be designed using a standard procedure; however, they are shown to have interesting properties. Observer uniqueness is also discussed. The methods proposed are suited to a sensorless IM drive control algorithm where the speed, the flux magnitude and the rotor flux angle are needed. The theoretical developments are supported with simulations and experiments.

A novel diagnosis method based on the rotor slip is proposed in the paper to correctly detect current and speed sensor failures during the induction motor drive (IMD) operation. In order to enhance reliability and avoid confusion in the diagnosis algorithm due to the influence of measured signal quality, each fault type is determined in a priority order defined by the diagnosis method. Based on the features of the IMD applying the field-oriented control (FOC) technique, an innovative model uses the measured currents and reference speed as the input signals to estimate the rotor slip for the current sensor fault detection. After verifying the quality of the feedback of the current signals, a speed sensor fault function is continued, and performs according to relations among the reference speed, estimated speed based on the sliding mode method, and measured rotor speeds. Finally, the estimated quantities are selected to replace the wrong measured current or speed signals. The feasibility of the proposed approach is verified by simulations using Matlab-Simulink software as well as by practical experiments using an IMD prototype with a rated power of 2.2 kW and a DSC-TMS320F28335-based control system. The obtained simulation and experimental results demonstrated the feasibility, effectiveness, and reliability of the proposed diagnosis technique in detecting sensor failures and maintaining the stable operation of the IMD.

Accurate dynamic parameter identification and the development of disturbance observers are essential for model-based sensorless force estimation and collision detection in industrial robots. Current methods predominantly rely on least squares and weighted least squares for dynamic parameters identification, but their accuracy is limited. Moreover, existing disturbance observers face challenges related to suboptimal convergence speed and the accurate capture of time-varying disturbances. Therefore, the novelty and highlight of this study lies in proposing an iteratively semi-linearized method for dynamic parameter identification and a finite-time nonlinear extended state momentum observer (FTNESMO), effectively resolving the aforementioned issues. The iteratively semi-linearized algorithm effectively integrates various nonlinear friction models. During the iteration process, this algorithm consistently updates the dynamic and friction parameters, enhancing the precision of the dynamic model and laying the groundwork for the development of disturbance observers. The FTNESMO introduces the second-order extended term for external disturbances, and the second-order expansion update law of the disturbances enables rapid convergence of observation error. Theoretically, through Lyapunov stability analysis, FTNESMO can converge in finite-time. To cope with noise and model errors, a novel adaptive bandpass filter and time-varying threshold are proposed under model uncertainties. To confirm the efficiency and superiority of the suggested algorithm, experiments were carried out using a 9-DOF redundant robot. The experimental results show a marked enhancement in the accuracy of identifying the dynamic model with the iteratively semi-linearized algorithm. FTNESMO exhibits excellent convergence speed and observation accuracy. The proposed algorithm has the potential to enhance industrial production safety and reduce associated costs.

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.

This paper presents an improvement of sensorless techniques based on anisotropy for the estimation of the electrical angular position of synchronous machines by means of an iterative algorithm. The presented method reduces the effect of the fourth saliency harmonics on the measured signals avoiding the use of an observer or filter, thus, no additional dynamics are introduced on the system. Instead, a static algorithm based on iterative steps is proposed, minimizing the angular position error. The algorithm is presented and applied using the DFC (Direct Flux Control) technique but it is not limited to this choice. The advantages and limitations of this method are presented within this paper. The proof of the algorithm convergence is given. Simulations and experimental tests are performed in order to prove the effectiveness of the proposed algorithm.