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This paper develops an online inter-turn short-circuit fault (ISF) detection method for inverter-fed permanent magnet synchronous machine (PMSM) systems. A high-frequency (HF) mathematical model of a PMSM with an ISF is established. Based on the presented HF model, the HF responses of the PMSM with various severity of ISF under HF voltage injection are analyzed in detail, where both the short-circuit ratio and the fault resistance are considered to evaluate the fault severity. In the proposed fault diagnosis method, the amplitude of the HF negative-sequence component is used to determine the fault severity, and the initial phase is used to locate the position of the faulty phase. The simulation and experimental results verify the effectiveness of the proposed method.

Self-bearing machines do not contain physical bearings but magnetic bearings. Both rotor rotary and spatial positions displacement are required in these types of machines to control the rotor position while it is levitating. Self-bearing machines often use external sensors for x (horizontal) and y (vertical) spatial position measurement, which will result in additional cost, volume, complexity, and number of parts susceptible to failure. To overcome these issues, this paper proposes a xy-position estimation self-sensing technique based on both main- and cross-inductance variation. The proposed method estimates x and y position based on inductive saliency between two sets of three-phase coils. The proposed idea is applied on a combined winding self-bearing machine which does not require additional suspension force winding. No additional search coil placement for xy-position estimation is required. Therefore, the proposed algorithm can result in a compact size self-bearing machine that does not require external sensors for xy-position measurement and suspension force winding.

Active magnetic bearings (AMBs) are a class of electromechanical equipment that effectively integrate Magnetic Bearing technology with PMSM technology, particularly for applications involving high-power and high-speed permanent magnet motors. However, as the rotor operates in a suspended state, the motor’s trajectory changes continuously. The installation of a speed sensor poses a risk of collisions with the shaft, which inevitably leads to rotor damage due to imbalance, shaft wear, or other mechanical effects. Consequently, for the rotor control system of PMSM, it is crucial to adopt a sensorless speed estimation method to achieve high-performance speed and position closed-loop control. This study uses the rotor system of a 75 kW AMB high-speed motor as a case study to provide a detailed analysis of the principles of high-frequency square wave signal injection (HFSWSII) and current signal injection for speed estimation. The high-frequency current response signal is derived, and a speed observer is designed based on signal extraction and processing methods. Subsequently, a speed estimation model for PMSM is constructed based on HFSWSII, and the issue of “filter bandwidth limitations and lagging effects in signal processing” within the observer is analyzed. A scheme based on the high-frequency pulse array current injection method is then proposed to enhance the observer’s performance. Finally, to assess the system’s anti-interference capability as well as the motor’s static and dynamic tracking performance, its dynamic behavior is tested under conditions of increasing and decreasing speed and load. Simulation and experimental results demonstrate that the PMSM control system based on HFSWSII achieves accurate speed estimation and shows excellent static and dynamic performance.

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.

Among various maximum torque per ampere (MTPA) control schemes for interior permanent magnet synchronous motor (IPMSM) drives, the signal-injection-based methods exhibit relatively high overall performance due to their high control accuracy and satisfactory dynamic performance. However, the high current spectrum peaks induced by the fixed-frequency signal injection may cause electromagnetic interference and even audible noise problems in applications, such as electric vehicles, vessels, and aircraft. To address this problem, an MTPA control method using pseudorandom frequency-switching sinusoidal signal injection is proposed in this paper. The sinusoidal signals with two different frequencies are randomly injected into the d- and q-axis currents and the MTPA points can be tracked according to the resultant system response. In this way, a high-performance MTPA control can be achieved regardless of motor parameter variations. Since the injection frequency of the proposed method varies randomly, the induced harmonic components in phase currents no longer concentrate at certain frequencies, and the current spectrum peaks caused by signal injection can be reduced accordingly. The experimental results demonstrate the validity of the presented method.

A permanent fault identification scheme based on LCC signal injection for high-voltage direct current (HVDC) systems is proposed to avoid secondary damage when it recloses to a permanent fault. Firstly, using the fault control ability of LCC, the additional control strategy is applied to the trigger angle of LCC to realize signal injection. The frequency, duration, and amplitude of the injection signal are analyzed and determined, and a signal injection strategy based on LCC is proposed. Secondly, the differences in voltage after signal injection under different fault properties are analyzed under the distributed parameter model. There is a significant difference in the amplitude of the measured voltage at the local end and the calculated voltage at the remote end under different fault properties due to differences in line models. Finally, a normalized area differential is constructed based on the above amplitude difference to realize permanent fault identification. PSCAD/EMTDC simulation results show that the proposed scheme utilizes single end data and is not affected by data communication. There is no need to set a threshold through simulation, and it can reliably identify permanent faults under 400 Ω fault resistance and 40 dB noise. It is suitable for line lengths of 1500 km and below.

A permanent magnet synchronous motor (PMSM) is typically run at low speed with a sensorless control system using a high-frequency signal injection method. However, current harmonic and gain errors compromise rotor position observation accuracy. In this paper, we analyze the reasons for rotor observation angle error and propose a new rotor position observer with error compensation. This new sensorless control tool obtains the compensation error angle by extracting the negative high-frequency current in order to estimate the rotor position information accurately. The experimental results show that the error compensation strategy proposed in this paper can improve the accuracy of rotor position observation and achieve operation of the PMSM in both steady-state working conditions and dynamic working conditions at low speed.

In order to ease the structure and manufacturing process of the variable reluctance (VR) resolver, the three-phase symmetrical single-layer winding commonly used in the stator winding of permanent magnet synchronous motors (PMSM) is applied to the VR resolver in this paper. The proposed resolver has the same winding direction and number of turns on all teeth. And the non-overlapping distribution of the three-phase windings of the resolver is ensured. For this novel resolver, the resolver-to-digital conversion (RDC) method references the ultra-high-frequency (UHF) signal injection method used when a PMSM is powered off and restarted. Instead of the need for the orthogonal envelope RDC required by conventional resolvers, the absolute position of the rotor can be obtained. In this paper, the prototype of the proposed resolver and the peripheral circuits are fabricated and compared with the position detected by the optical encoder, and the validity of the proposed resolver and the accuracy of the RDC are verified by the results of the comparison experiments.

The operating temperature of new energy vehicles fluctuates significantly, and variations in motor temperature lead to changes in parameters. These changes introduce errors into the motor’s mathematical model, reducing torque accuracy and causing deviations in the Maximum Torque Per Ampere (MTPA). This paper proposes a Gated Recurrent Unit (GRU) neural network-based torque observer that employs virtual signal injection. Specifically, this method innovatively injects a virtual constant signal into the d-q axis current inputs processed by the neural network to derive the partial derivatives of torque concerning the d-axis and q-axis currents. Subsequently, it calculates the derivative of torque concerning the current vector angle (β) using the total differential equation. By leveraging these partial derivatives, the motor parameters are identified online, and the MTPA current reference value is dynamically adjusted based on the identified parameters. Additionally, the GRU’s internal parameters are fine-tuned in real time using the least mean square (LMS) algorithm, which adjusts based on the derivative of torque concerning the current angle and the error between the observed and actual values, thereby enhancing the accuracy of torque observation, and bringing results closer to the true shaft-end torque. Finally, experimental validation confirms the effectiveness and superiority of the proposed method.

This study focuses on the maximum torque current ratio control of synchronous reluctance motors and proposes an optimized control method for the maximum torque current ratio of synchronous reluctance motors based on virtual signal injection. Firstly, the research on the maximum torque current ratio control of synchronous reluctance motors based on the virtual signal injection method is conducted, and the existing virtual unipolar square wave signal injection method is analyzed and studied. Secondly, a non-parametric maximum torque current ratio control strategy based on a synchronous reluctance motor combined with the virtual signal injection method is proposed. This strategy does not involve complex parameter calculations, and the control accuracy is not limited by the accuracy of the parameters in the model. The experimental results showed that under the control of virtual bipolar and unipolar square wave signal injection methods, the load torque was converted from 2 Nm to 6 Nm at t = 2:5 s, and there was a significant change in the current amplitude and waveform of the current vector. Under the control of the bipolar injection method, the current amplitude waveform of the motor was lower than that of the unipolar waveform, and the current was smaller. After the load suddenly changed, it could enter a stable state faster. After the load changed at t = 2:5 s, the phase angle of the current vector was quickly adjusted and stabilized under the control of the bipolar signal. The designed method has a good optimization effect compared to the traditional virtual signal injection method, and can achieve high-performance maximum torque current ratio optimization control on synchronous reluctance motors.