This paper presents a transient power quality (PQ) disturbance classification approach based on a generalized S-transform and probabilistic neural network (PNN). Specifically, the width factor used in the generalized S-transform is feature oriented. Depending on the specific feature to be extracted from the S-transform amplitude matrix, a favorable value is determined for the width factor, with which the S-transform is performed and the corresponding feature is extracted. Four features obtained this way are used as the inputs of a PNN trained for performing the classification of 8 disturbance signals and one normal sinusoidal signal. The key work of this research includes studying the influence of the width factor on the S-transform results, investigating the impacts of the width factor on the distribution behavior of features selected for disturbance classification, determining the favorable value for the width factor by evaluating the classification accuracy of PNN. Simulation results tell that the proposed approach significantly enhances the separation of the disturbance signals, improves the accuracy and generalization ability of the PNN, and exhibits the robustness of the PNN against noises. The proposed algorithm also shows good performance in comparison with other reported studies.

This study presents an improved hybrid parallel observer (HPO) based model predictive current control (MPCC) strategy to realise the source voltage sensorless control of grid‐connected converters under inductance and resistance uncertainties. Since there is a little research on the sensorless control of MPCC‐based converters, in this study, according to the linear extended‐state observer theory and the adaptive filtering theory, a HPO which combines two different types of observers is constructed. With the new observer, the multivariable observation of system disturbance and model parameters is achieved. On this basis, through the combination of the HPO and the MPCC, a new HPO‐MPCC control strategy is formed. The stability and parameter tuning of the HPO are analysed. To determine the initial value of the source voltage angle in the startup process of the converter, a new startup algorithm is proposed. A two‐level grid‐connected converter is built to verify the feasibility and effectiveness of the proposed strategy. The results show that, compared with the traditional algorithms, the new HPO has higher accuracy and stability; the HPO‐MPCC achieves the source voltage sensorless control of the grid‐connected converters and ensures the high precision and fast dynamic response characteristics of the conventional MPCC; the new startup strategy avoids the over‐current problem of the conventional methods and improves the reliability of the whole system.

In order to solve the problem that the position synchronous performance of dual permanent magnet synchronous motor servo system is easily affected by parameter perturbation and load disturbance, an anti-disturbance integrated position synchronous control strategy of dual permanent magnet synchronous motor system is proposed. Firstly, the integral mathematical model of a dual permanent magnet synchronous motor system is established. After that, the third-order linear extended state observer is constructed to observe and compensate for disturbance in order to improve the anti-disturbance performance of the position loop. At the same time, a cross-coupled position synchronous compensator is added to improve the synchronous performance of the system, and through the Bode diagram analysis method in the frequency domain, the system stability and anti-disturbance performance are researched. Finally, the effectiveness of the proposed algorithm is verified by experimental research.

This article proposed a predictive flux control strategy for permanent magnet synchronous machines based on flux trajectory optimization. Instead of minimizing the instantaneous flux tracking error at the end of each sampling period, the effect of different control sets on flux trajectories within the entire sampling interval is taken into account in the cost function. As a result, steady-state torque and flux ripples can be suppressed. Besides, inaccurate sector determination found in the conventional predictive control strategies can be successfully avoided. As well as highly dynamic performances inheriting from the predictive characteristics, enhanced steady-state torque control performances can be guaranteed under different operating conditions. Although the extended control set is utilized to augment the control freedom and further reduce torque ripple, the control effect of the proposed algorithm does not rely on the increase of switching frequency. Compared with the traditional predictive control strategies based on either finite control set or an extended control set, a superior control effect can be obtained while keeping a low switching frequency. Moreover, duty cycle signals are directly obtained in the prediction process, and no additional space vector pulsewidth modulation method is needed. Experimental validations and comprehensive comparison are conducted to demonstrate the effectiveness of the proposed strategy.

Finite control set model predictive control (FCS‐MPC) for three‐phase power converters uses a discrete mathematical model of the power converter to predict the future current value for all possible switching states. The circuit parameters and measured input currents are necessary components. For this reason, parameter error and time delay of current signals may degrade the performance of the control system. In the previous studies of the FCS‐MPC, few articles study these aspects in detail and almost no method is proposed to avoid these negative influences. This paper, first, investigates the negative impacts of inductance inaccuracy and AC‐side current distortion due to the time delay caused by filter on FCS‐MPC system. Then, it proposes an observer‐based FCS‐MPC approach with which the inductance error can be corrected, the current signal’s time delay caused by filter can be compensated, and therefore the performance of FCS‐MPC will be improved. At last, as an example, it illustrates the effectiveness of the proposed approach with experimental testing results for a power converter.

Three-phase four-leg inverters are effective solutions to solve three-phase uncertain loads problem. However, the irregular three-dimensional vector space of four-leg inverter makes it difficult to achieve high steady-state performance and ripple reduction. To overcome this deficiency, a duty-cycle-optimized voltage predictive control method (DCO-VPC) is proposed to improve the performance of conventional modulated model predictive control (C-M $^\text{2}$ PC) including output ripple reduction. It maintains a fixed switching frequency meanwhile inheriting the fast dynamic response and over-modulation capability of finite control set model predictive control (FCS-MPC). Virtual vectors are generated by angular errors between vectors, which partition the spatial tetrahedron into finer subspace and iterative operation is performed to improve prediction accuracy. In addition, a Lagrange function with Karush-Kuhn-Tucker (KKT) conditions is constructed and solved to obtain the output voltage boundary of inverter in the direction of current reference vector, which is used as a criterion to determine over-modulation region of four-leg inverter. Comprehensive experiments with different control schemes under various operating conditions show that the proposed DCO-VPC has advanced steady-state and transient performance, as well as over-modulation capability.