Iet Electric Power Applications最新文献

The application of model predictive control (MPC) for the control of modular multilevel converters (MMCs) is widely explored because it offers flexibility in integrating multiobjective control and delivers superior dynamic response. Nonetheless, the increase in computational complexity due to the rise in the number of submodules (SMs) is one of the major drawbacks of this technique. This paper presents a finite control set model predictive control (FCS-MPC) that significantly reduces the computational complexity by employing sparse identification of non-linear systems (SINDy) to obtain a simplified linear model for the MMC. The SINDy model reduces the complexity of performing the prediction step by integrating input terms into the dynamics of load current and circulating current. This simplifies the implementation compared to the conventional FCS-MPC approaches by eliminating the need to evaluate the voltage dynamics. The computational burden is further reduced while maintaining $��2�N�+�1�$ voltage levels at the output by restricting the number of combinations for the inserted SMs to only $��\left(�\frac{N}{3}�+�1�\right)�$ instead of $��{�\left(��N�+�1��\right)�}^2�$. A detailed comparison between the proposed technique and the existing strategies demonstrates that the proposed technique offers a more computationally efficient solution for implementing FCS-MPC on MMCs, while improving the circulating current suppression due to more accurate predictions. Simulation and experimental results are presented to validate the performance of the proposed approach.

Permanent Magnet Synchronous Machine (PMSM) is widely utilised in numerous industrial applications due to its precise control capabilities. However, these motors frequently encounter operational faults, potentially leading to severe safety and performance issues. Consequently, effective health monitoring techniques for early fault detection are essential to maintain optimal performance and extend the lifespan of these systems. This study presents a qualification-based methodology for diagnosing faults in three-phase PMSMs through vibration–current data fusion analysis. The stator faults, specifically inter-turn short circuits (ITSC) induced via bypassing resistances, were investigated using experimental data from a custom-built test rig. The collected current and vibration signals were transformed into statistical features. Various operating scenarios were diagnosed utilising a deep regulated neural network (RegNet), an improved convolutional neural network based on an enhanced residual architecture. The proposed approach was assessed through various metrics including training efficiency, precision, recall, f1-score, and accuracy, and compared against several neural network methods. The findings reveal that the proposed RegNet model achieves perfect accuracy, attaining 100%. This research highlights the efficacy of data fusion analysis and deep learning in fault diagnosis, facilitating proactive maintenance strategies and improving the reliability of PMSMs in diverse industrial applications and renewable energy systems.

The variation of configuration parameters in the insulation structure of transformers significantly impacts the distribution of electric fields. Current research on electric field distribution calculations often overlooks the influence of electric field intensity and temperature on this distribution. However, considering that the direct current conductivity of oil-paper materials is easily affected by both electric field intensity and temperature, any changes in their conductivity will inevitably affect the electric field distribution. Therefore, this paper incorporates the influence of electric field intensity and temperature on the calculation process of the electric field distribution in the main insulation structure of the valve-side winding. Additionally, it calculates the distribution of multi-fields for a wide range of structural configuration parameters. Consequently, the influence law of insulation structure configuration parameters on the electric field distribution of the main insulation structure of valve-side winding for converter transformer when considering electro-thermal coupling is obtained. This finding can serve as a crucial reference for designing and optimising converter transformers.

The authors present an effective induction machine (IM) drive system and intelligent supervisory control for application to smart water-pumped storage and networks. The main IM drive unit comprises reduced voltage starter, regenerative clutch/variable speed mode fluid coupling and reversible pump to allow bidirectional power flow. Under the wide umbrella of supervisory control, the following objectives are achieved: (1) Recurrent neural network (RNN) and RNN-aided Kalman filter are proposed for wired and wireless sensor fault-tolerant control of the plant based on Wald's likelihood ratio test. (2) Cheap fuzzy logic-based availability assessment scheme is presented and deployed on cloud to arrange for preventive maintenance of the starter. (3) Robust energy saving mode decision-making algorithm is developed for best energetic cost reduction. (4) Remote synchronisation method of multi-motor starting with a mechanically switched capacitor is introduced to minimise voltage dip. Further, an auxiliary inverter fed pump unit is adopted to improve system availability and revenue. The validity and superiority of the proposed drive system and control are confirmed through theoretical analysis, computer simulations, cost study, efficiency evaluation and application example to a single-pump water network during motor and generator operational modes using preliminary test results.

State-of-the-art model-based predictive control techniques for AC motor drives are reviewed in this paper. A plethora of MPC algorithms with vast number of complex ideas has emerged in the last decade and this work makes an attempt to present those concepts in an intuitive, comprehensive and hierarchical manner. More emphasis is laid on finite control set model predictive control (FCS-MPC) methods, especially predictive torque control (PTC) and predictive current control (PCC) because of their emergence as the prime focus of ongoing research in energy efficient drive control. The main focus of this review is to analyse the most recent work, signpost the future research directions, identify the core challenges and consolidate the ideas into a coherent and concise reference. A comprehensive classification based on actuation signals is presented and reviewed in detail. Then, the important challenges in MPC implementation, such as computational complexity reduction and delay compensation, weighting factor selection for multi-objective cost functions, steady state performance and ripple reduction, parameter variations/model mismatching and achieving extended prediction horizons, are surveyed and most relevant solutions are reviewed. A detailed analysis of the last five years related work is given at the end and it is concluded that the future course seems to be diverting towards voltage vector selection with optimised phase, magnitude and duty ratios. Computational burden is still one of the main hurdle towards MPC proliferation and adaptation in AC drive control at the industrial level. However, with advent of high speed and cheaper signal processors and development of efficient algorithms, MPC is rapidly becoming the control method of choice for energy-efficient drive control.

The aim of this paper is to design and Finite Element Analysis (FEA) of a novel rotor topology for a permanent magnet synchronous reluctance machine. According to the advantages and drawbacks of the PMSyncRM and the SPMSM, a conventional PMSyncRM rotor design combined with sur-face-mounted magnets is introduced as the SPMSyncRM. The surface-PM arc size is investigated, so that it is considered as multiplication of an integer coefficient and the angle between two adjacent slots of the stator. The analysis involves simulation of the electromagnetic characteristics and the numerical model of each structure using finite element method. Each E-Magnetic character is calculated for a better understanding of the benefits of the SPMSyncRM. Applying 2D FEA, the optimised surface-PM arc size is calculated and the static and dynamic operational behaviour of the SPMSyncRM is simulated. Moreover, short circuit analysis is performed to study the demagnetisation of magnets. All results are reported comparatively considering a conventional PMSyncRM, so that the proposed SPM-SyncRM topology presents high torque-power density, lower values of cogging torque, higher values of power factor and efficiency, better static and dynamic performance, and robustness towards demagnetisation.

The expansion of permanent magnet (PM) machines due to functional advantages such as power density and low losses (high efficiency) and better controllability in industrial applications has led to an increased need for research on possible faults in this type of machine and the development of effective methods to diagnose faults in different operational conditions. The performance characteristics of PM machines are strongly influenced by the PMs used in their structure. Accordingly, one of the most important PM machine faults is the demagnetisation fault which arises due to the physical, operational, and environmental conditions or their combination. This can result in damage to the functional characteristics of the machine. In this paper, the main topologies of PM machines, including the PM synchronous machines, the brushless DC motors, and the flux modulation machines, are first reviewed. Subsequently, the techniques for detecting demagnetisation faults in PM machines are then reviewed and summarised. This paper discusses the factors that cause faults in PM machines and their effects on machine performance. It also examines conventional modelling methods used to study demagnetisation faults. Finally, it presents a general structure for techniques developed for demagnetisation faults in different operating conditions (stationary and non-stationary) and based on intelligent methods. It also compares the proposed methods and highlights their strengths and weaknesses.

Switch reluctance motors (SRMs) have many advantages, such as easy construction, high reliability, low cost, and higher energy efficiency compared to the inductance motors. However, SRMs have a major drawback—severe torque ripple (TR), which causes a great deal of noise and movement. Hence, these motors are rarely used in industrial applications. Torque ripple reduction in reluctance motors has been studied since their invention in the 1950s, but the effectiveness of any such reduction methods remain controversial. Significant reductions in TR remain necessary to make the SRM more applicable for industrial use. The main objective of this study is to propose a new configuration of the SRM which greatly reduces TR and has low manufacturing costs. Although this new structure greatly reduces the TR, like most previous research, some aspects such as drive control and design optimisation methods could be used to more obviously reduce TR. The proposed motor has a simple configuration which is more efficient. Finite element method is used to verify the design, and a prototype has been fabricated and tested to experimentally verify its performance.

The sensorless control of synchronous reluctance motor (SynRM) in the zero and low-speed domain using the traditional high-frequency injection method has the problems of audible noise caused by the high-frequency injection and high-frequency loss caused by the change of working conditions, which limits the practical application of SynRM in the industrial field. To solve the problem of high-frequency noise and loss, a pseudo-random high-frequency injection method considering parameter variation is proposed in this paper. Firstly, the signal injection form was adjusted to expand the power spectral density of the high-frequency current, and the current energy spike was suppressed to reduce high-frequency noise. Secondly, the current demodulation was combined with the flux map model to complete the injection voltage amplitude adjustment, so that the response current was kept constant under multiple operating conditions to reduce the high-frequency loss. At the same time, the flux map model is applied to the observer to reduce the rotor position estimation error caused by the cross-coupling effect. Under the condition of satisfying the dynamic and steady performance requirements of sensorless control, the high-frequency loss and sharp noise caused by the high-frequency injection method are effectively suppressed. Finally, experiments were carried out on a 1.5 kW SynRM drive platform to verify the feasibility and effectiveness of the sensorless control scheme in this paper.