This paper presents a torque error compensation algorithm for a surface mounted permanent magnet synchronous machine (SPMSM) through real time permanent magnet (PM) flux linkage estimation at various temperature conditions from medium to rated speed. As known, the PM flux linkage in SPMSMs varies with the thermal conditions. Since a maximum torque per ampere look up table, a control method used for copper loss minimization, is developed based on estimated PM flux linkage, variation of PM flux linkage results in undesired torque development of SPMSM drives. In this paper, PM flux linkage is estimated through a stator flux linkage observer and the torque error is compensated in real time using the estimated PM flux linkage. In this paper, the proposed torque error compensation algorithm is verified in simulation and experiment.

In a single-phase grid-tied inverter, the direct current (DC) offset error included in the measured grid side phase current has various causes, such as a non-ideal current sensor, unbalanced power supply of an operational amplifier, and nonlinear features of analog components in interface circuits, etc. If the DC offset error is included in the measured current, it causes the secondary harmonic of fundamental frequency and the DC component in grid phase current which result in degradation of inverter performance. In this paper, a theoretical detection method of the secondary harmonic of the fundamental frequency and a DC component in grid phase current for a proportional-resonant (PR) current control system is introduced. Based on the detection method, an algorithm for compensating DC offset error is also presented for single-phase grid-tied inverters. Simulation results and experimental verification of the DC offset error compensation algorithm are shown in this paper.

This paper presents a stability analysis and dynamic characteristics investigation of deadbeat-direct torque and flux control (DB-DTFC) of interior permanent magnet synchronous motor (IPMSM) drives with respect to machine parameter variations. Since a DB-DTFC algorithm is developed based on a machine model and parameters, stability with respect to machine parameter variations should be evaluated. Among stability evaluation methods, an eigenvalue (EV) migration is used in this paper because both the stability and dynamic characteristics of a system can be investigated through EV migration. Since an IPMSM drive system is nonlinear, EV migration cannot be directly applied. Therefore, operating point models of DB-DTFC and CVC (current vector control) IPMSM drives are derived to obtain linearized models and to implement EV migration in this paper. Along with DB-DTFC, current vector control (CVC), one of the widely used control algorithms for motor drives, is applied and evaluated at the same operating conditions for performance comparison. For practical analysis, the US06 supplemental federal test procedure (SFTP), one of the dynamic automotive driving cycles, is transformed into torque and speed trajectories and the trajectories are used to investigate the EV migration of DB-DTFC and CVC IPMSM drives. In this paper, the stability and dynamic characteristics of DB-DTFC and CVC IPMSM drives are compared and evaluated through EV migrations with respect to machine parameter variations in simulation and experiment.

This paper introduces a sensitivity-enhanced fault detection algorithm, termed the Fault Diagnosis and Amplifying (FDnA) method, designed to amplify fault signals in induction motors with broken rotor bars. The FDnA algorithm employs a cascaded state observer structure, integrating stator flux linkage and stator current observers based on an analytical model of an induction motor. By multiplying the output signals of these observers, the algorithm significantly enhances the sensitivity of fault detection. The FDnA method is robust against external environmental conditions and excels in accurately detecting broken rotor bar faults. Additionally, it offers a cost-effective and retrofittable solution by replacing traditional sensors and interfacing circuits with state observers, eliminating the need for additional hardware. Comprehensive validation through simulations and rapid control prototyping highlights the superior fault detection sensitivity of the FDnA method compared to conventional approaches, establishing it as a reliable and efficient advancement in fault detection for induction motors.

This paper proposes a stationary reference frame current control algorithm for a single-phase grid-connected inverter (GCI) for improvement of transient dynamic performance. Disturbance, i.e., grid voltage in a target system, is estimated using a stator current observer, and the estimated disturbance is applied to a current controller for implementation of disturbance rejection control (DRC). In the proposed current control algorithm, the disturbance rejection control algorithm is applied to reduce the overcurrent occurring in the single-phase grid-connected inverter when grid faults happen. In this paper, the AC phase current of a single-phase inverter is controlled, instead of the current vector, which is a DC signal. To compensate for the drawbacks of controlling the AC phase current, such as phase lag and steady-state error, command feedforward control is also applied in the proposed control system. The proposed control algorithm is mathematically derived and represented in transfer functions and implemented via simulation and experiment.