Iet Electric Power Applications最新文献

The selected electromagnetic field model of a hydro generator directly affects the calculation of the no-load magnetic field, which in turn affects the grid-connected power quality of the hydrogenerator and the power transmission safety. Tubular hydro generators have a horizontal structure with less internal space than the conventional vertical hydro generator, which results in a particularly complex and strong internal magnetic field distribution. This study investigated the influence of the selected electromagnetic field model on the calculation of the no-load magnetic field of a tubular hydro generator. Straight and skewed stator slots were considered for the structure of the hydro generator. Three models were considered: the transient motion electromagnetic field-circuit coupling model, the transient motion electromagnetic field model, and the static electromagnetic field model. The calculation accuracy and computational efficiency of the three models were evaluated by comparison to measured data. The results were used to make reasonable suggestions for the selection of a suitable electromagnetic field model in different scenarios. The findings are expected to support the electromagnetic field analysis and design of hydro generators.

This paper presents two novel hybrid rare-earth and ferrite permanent magnet (HPM) asymmetric V-shape and U-shape interior PM synchronous machines (IPMSMs) with high ferrite PM (FEPM) torque contribution accounting for the enhanced demagnetisation withstand capability of FEPM at both open circuit and overload conditions. The proposed topologies are designed and compared with a rare-earth PM (REPM)-based symmetrical V-shape baseline in terms of electromagnetic performances, mechanical strength, demagnetisation withstand capability, and PMs cost. All machines are optimised for the same torque with the minimum volume of high-cost REPM at the same specification and size as a commercialised electric vehicle (EV) IPMSM. It is shown that the synergies of magnetic field shifting effect and HPM utilisation have improved the torque per REPM usage in both proposed machines. However, the magnetic field shifting of the proposed HPM asymmetric U-shape IPMSM is twice of that in the V-shape IPMSM counterpart along with a slightly better FEPM demagnetisation withstand capability. Meanwhile, the results show that the proposed HPM asymmetric V-shape IPMSM would be cheaper than the U-shape counterpart as the former and latter topologies require ∼31% and ∼23.5% less REPM volume than the baseline, respectively. Finally, two small laboratory size prototypes are made and tested to verify the finite element analyses.

With the growing share of wind power generation in the global energy structure, sub-synchronous oscillations (SSOs) triggered by the integration of wind power into the grid, as a potential dynamic instability phenomenon, have a non-negligible impact on the performance and safety of the power system and even lead to serious operational risks. Starting from the operation mechanism of the doubly-fed induction generator (DFIG) and in combination with the control of the rotor-side converter (RSC), an equivalent impedance model of the DFIG is constructed. Employing the Morris method and the extended Fourier amplitude sensitivity test (EFAST) method, a comprehensive global sensitivity analysis of the system impedance is conducted, progressing from qualitative to quantitative analysis. High-sensitivity parameters are identified, and the system dynamic interval is partitioned through the joint adjustment among these parameters. Subsequently, a cooperative optimisation method for high-sensitivity parameters is proposed to optimise the parameter set within the instability region to effectively suppress SSO. Finally, the simulation model of the DFIG grid-connected system with series compensation is established, and the feasibility of the optimisation method is verified using the Middlebrook criterion. The results demonstrate that the optimisation method exhibits strong adaptability under different operating conditions, effectively mitigating the risk of SSOs and ensuring stable operation of the wind power system.

In recent years, a significant amount of research on modelling of electrical machines was dedicated to high fidelity hybrid models that incorporate pre-calculated FEA data in the form of lookup tables (LUTs). Despite the increasing interest in this dynamic modelling approach, the literature largely disregards how the construction process of LUTs can impact both the accuracy of the model and the computational efficiency. This paper explores the LUT accuracy level attainable by application of various relevant data fitting interpolation algorithms and data calibration parameters using a standard permanent magnet machine geometry. It is demonstrated that an optimal trade-off between high accuracy LUT demand and the inherent high computational requirements associated with creating the requisite FEA datasets is important for facilitating effective development of hybrid model LUTs.

This paper proposed a fault diagnosis and system reconfiguration based on switching sequence technology for active neutral point clamped (ANPC) three-level cascaded inverter. In this paper, the flow path of current and the clamp voltage under the single-switch and double-switch open-circuit faults for the power switches are analysed in the ANPC bridge arm. The combination of the clamp voltages corresponding to the four switch modes in each fault mode is determined uniquely. According to the uniqueness of the combination of clamp voltages with the four switch modes, a fault diagnosis method based on the switching sequence technology is proposed which can realise fault diagnosis with 10 fault types in one bridge arm. The faulty bridge arm is bypassed by the clamp power switches to achieve the goal of topology reconfiguration, and carrier phase shift strategy for the remaining module is adopted to achieve the goal of modulating reconfiguration. Finally, the correctness of theoretical analysis is verified by the simulation and experiments.

Considering the various unknown and uncertain parameters as well as load disturbances of permanent magnet linear synchronous motor (PMLSM) drive systems, this paper proposes a novel model-free adaptive super-twisting (MFAST) speed control strategy based on radial basis function neural network (RBFNN) estimator to ensure the satisfactory performance and strong robustness of the speed control. First, by considering all possible unknown and uncertain parameters, the ultralocal model of PMLSM is constructed. Next, the RBFNN estimator is designed to estimate the unknown parameters of the above-mentioned ultralocal model. Finally, the RBFNN-based MFAST control law is proposed to guarantee PMLSM drive systems' robustness against various internal and external disturbances. StarSim HIL experiment results demonstrate that the synthesised RBFNN-based MFAST control strategy can enable PMLSM drive systems to possess high accuracy, remarkable rapidity and strong robustness.

This paper investigates the torque characteristic and multi-objective optimisation (MOO) of the multi-layer flux-barrier less-rare-earth permanent magnet synchronous machine (MLFB-LRE-PMSM) used for the electric vehicles (EVs). This study explores the variation of torque characteristics with current angle under different winding current conditions, and thoroughly analyses the influence of permanent magnet (PM) structure parameters on reluctance torque, PM torque and the proportion of reluctance torque in the electromagnetic torque. On this basis, the sensitivity analysis method based on Sobol sequence and joint Sobol index is adopted, which not only simplifies the traditional analysis process, but also effectively considers the interaction effect between the optimisation objectives. Finally, a cooperative optimisation strategy of improved whale optimisation algorithm (WOA) and genetic algorithm (GA) is proposed, which is successfully applied to the MOO design of MLFB-LRE-PMSM. The results show that the improved WOA algorithm shows excellent optimisation performance and can be used as a new solution in the field of motor optimisation. At the same time, the engineering practicability of the proposed collaborative optimisation scheme is verified by finite element simulation.

A direct-drive selective compliance assembly robot arm (DDSCARA) poses difficulties in system optimisation due to numerous design parameters and strong coupling between components. This article presents a new optimisation framework based on the characteristic parameters surrogate model to solve the computation burden of the DDSCARA motion system optimisation. The framework divides the optimisation problem into three sub-optimisations. Firstly, we construct a characteristic parameters surrogate model of the direct-drive motor (DDM) by multi-objective optimisation to reduce the training dataset size at the component level. Secondly, in the system-level optimisation, we take the cost and reliability indicators as the optimisation objectives and obtain the optimal characteristic parameters of the DDMs, motion trajectory parameters, and design parameters of other components. A pre-optimisation of the DDM characteristic parameters using gradient descent is used to accelerate the convergence and improve optimisation results. Thirdly, the closest point method and Bayesian optimisation are used to recover the DDM design parameters. For the given optimisation problem, the new framework saves 97.7% computation time compared to the traditional framework. We design an optimised prototype and conduct comparative experiments with the original prototype. The optimised prototype achieves 33% and 12.5% improvements in reliability and unit production per hour, respectively.

The electromagnetic force plays a significant role in contributing to torque ripple and electromagnetic vibration in permanent magnet synchronous machines (PMSMs), thereby impacting the noise, vibration, and harshness (NVH) performance. However, in most cases, existing design and analysis approaches predominantly focus on torque ripple, radial force and vibration under the stator reference frame, which is not sufficiently comprehensive. To address this gap, this paper presents the derivation of the spatial and temporal orders of force harmonics under both the stator stationary reference frame and the rotor rotating reference frame. Through theoretical analysis and finite element method (FEM) simulations, this paper highlights the differences in force harmonics and vibrations between the two reference frames. Furthermore, it explores the relationship between torque ripple and vibrations under different reference frames. Additionally, the superposition and cancellation effects of radial and tangential forces in producing vibrations are investigated. Finally, to validate the theoretical analysis, two prototypes of PMSMs with 6 poles and 9 slots have been manufactured and subjected to performance testing.

In this paper, a prediction model of 20 Hz low-frequency transformer core loss based on the grey wolf optimisation algorithm-optimised back propagation neural network is proposed. Firstly, the loss characteristics of silicon steel sheet materials at different low-frequency temperatures and normal temperatures at different frequencies were compared. The general law of the variation of no-load iron loss with frequency and temperature is analysed. Finally, the BP neural network prediction model of low-frequency transformer core loss is established. The loss data obtained by experiment and simulation are used as training and verification samples to predict transformer core loss. The results show that the GWO-BP neural network loss model proposed in this paper successfully predicted the no-load loss of the transformer at different temperatures. When the prediction effect of the GWO-BP model was optimal, the determination coefficient R² reached 0.9169, and the mean relative error and root mean square error were only 1.15% and 0.0085, respectively. Moreover, the MRE of the GWO-BP model is within 9%. Compared with the BP model and whale optimization algorithm-BP model, the prediction accuracy of the loss is improved by the GWO-BP model, and the calculation time of the loss is reduced by the finite element method.