Iet Electric Power Applications最新文献

Standardised drive cycles are commonly used to assess passenger and other vehicles' performances. However, real vehicle drive cycles cannot be tested in laboratories with smaller machines because of torque and speed range limitations. Thus, for experimental analysis in the laboratory, these drive cycles need to be scaled down. This paper presents a method of down-scaling such transient cycles, maintaining information on the cycles' transient characteristics, to enable experimental investigations of real vehicle driving scenarios in small laboratory-scale settings. It examines the method's suitability to analyse the machine performances in terms of electrical energy conversion and thermal aspects, and thus subsequently the influence of different control and operation approaches. Three test case speed-over-time drive cycles are chosen and analysed for the cases of two different electric vehicles (EVs). The down-scaling method is studied with respect to two different laboratory-based test case motors: a permanent magnet synchronous motor (PMSM) and an induction motor (IM). Thus, a total of 12 example case scenarios are considered.

In recent decades, multi-level inverter such as a five-level inverter has gained much attention for its high equivalent switching frequency and low voltage stress. This paper proposes a novel five-level active-neutral-point-clamped converter utilising reverse blocking IGBT (RB-IGBT), targeting auxiliary power supply in high-speed train and metro system. Compared to the conventional five-level inverters, the proposed inverter configuration exhibits lower conduction loss. Unlike the five-level topologies of previous inverters, only two switches are on the current paths in the designed inverter, leading to the reduced conduction loss. Meanwhile, an optimised state machine-based modulation strategy was proposed, which can implement reliable current commutation among adjacent states and maintain flying capacitor voltage balance within multiple carrier cycles. Furthermore, a detailed comparative analysis of loss breakdown and voltage stress characteristics in five-level inverters were presented. Finally, a 27 kVA experimental prototype was built to verify the validity and feasibility of the proposed topology and modulation strategy.

Deep learning methods for fault diagnosis in transformer windings have drawn growing research interest in recent years. However, the scarcity of FRA fault data limits the application of deep learning in this field. To address this challenge, we propose a data augmentation model based on the diffusion model, namely, CDFF-TW, to generate enough FRA fault data used in the field. First, this study designs a new input data format in the proposed model, which replaces traditional data preprocessing with the direct input of data. Moreover, the forward diffusion process of the proposed model is designed to be reusable, greatly reducing the overall training time compared to traditional diffusion models. Subsequently, a classifier is incorporated into the reverse diffusion process, and a screening component is designed to screen generated data based on the morphological similarity and the resonance peak similarity. Furthermore, a series of comparative tests is conducted. In addition, the model used the CDFF-TW to augment data has better performance compared to existing models in transformer winding fault diagnosis. Finally, the optimal ratio of generated to original data is provided as a guideline for augmenting data in fault diagnosis models using the proposed method.

This paper analyses Vernier structures with open-slot and split-tooth stators for generator applications. A comparative evaluation of these structures is conducted in terms of dimensions and materials, and their performance results are compared with each other to identify methods for enhancing voltage regulation, power factor, and cogging torque. Research has demonstrated that, within the framework of an open slot structure, the reduction of self and mutual inductance within each phase has the potential to enhance the power factor. This configuration has been shown to exhibit significantly superior voltage regulation properties in comparison to the alternative structures. Consequently, it may be particularly well-suited for wind turbine generators, where cogging torque and power factor represent two of the primary design considerations. Subsequently, a 200 W sample is constructed and verified with the desired structure. Finally, the performance of the constructed structure is benchmarked against two magnet arrangements, spoke and consequent pole, in terms of cost-effectiveness.

The massive integration of user-side resources such as electric vehicles and photovoltaic, with complex time-series complementary and price response characteristics, has fundamentally changed distribution network structure and imposed new challenges on transformer capacity configuration. Traditional optimisation methods suffer from issues such as transformer overload, capacity redundancy and waste of resources. To resolve above issues, a transformer capacity configuration method considering multidimensional interaction characteristics of user-side resources is proposed. Firstly, a multidimensional interaction model of user-side resources and a hierarchical capacity optimisation architecture of transformers are constructed. Secondly, a hierarchical transformer capacity configuration method is proposed, which consists of a dedicated transformer capacity configuration method based on improved soft actor-critic (SAC) and a public transformer capacity configuration method based on knowledge collaborative multi-agent proximal policy optimisation (MAPPO). Simulation results indicate that compared with the baseline algorithms, the proposed algorithm improves the optimal public transformer capacity by 13.39%, 28.65%, 19.13% and 42.36% and reduces the optimal regulation cost by 2.46%, 5.83%, 3.93% and 7.84%, respectively. Furthermore, it reduces the load deviation variance by 21.05%, 50.49%, 40.73% and 46.74% and decreases the peak-valley load difference by 17.14%, 48.21%, 34.09% and 40.82%, respectively.

Electrical machines play the role of energy conversion for wind power generation, rail transit systems, electric vehicle traction, and ship propulsion applications. While there has been a steady growth in the use of electrical machine systems, their performance and reliability can become degraded due to the effects of complex operating conditions, which imposes a significant challenge for the applications of electrical machine systems in modern industry. Considering this, new machine topology, optimisation design, condition monitoring methods, and diagnostic techniques as well as control technologies must be developed. In line with the trend of reliable operation of electrical machine system, this Special Issue aims to present state-of-the-art research works on reliability-oriented study of electrical machine systems, including diagnostic techniques, topology, monitoring, and control. Through careful peer reviews and revisions, there are 17 papers accepted for publication in this Special Issue, which have been categorised into four topics, that is, advanced fault diagnosis techniques vibration and noise suppression method, emerging condition monitoring approach, and high-performance sensorless control strategy. The summary of every topic is given below. However, it is strongly encouraged to read the full paper if interested.

Rare earth elements (REEs) are central to the current solutions used for traction applications. However, REEs have fragile supply chains, which exposes them to supply interruptions and price spikes. Research has been focusing on REE-free solutions, either exploring REE-free topologies, such as induction and electromagnetised machines, or investigating the use of alternative hard magnetic materials, such as ferrite permanent magnets (PMs). This paper presents a novel methodology for designing and optimising spoke type permanent magnets synchronous machines (spoke machines) with ferrite PMs. The novelty of the methodology is the unique strategy used to integrate mechanical and demagnetisation constraints. Using this methodology, a novel rotor is optimised using FEM simulations to directly substitute a previous REE-based motor. The optimised design represents a unique rotor, mainly due to its large magnet size, which enables a demagnetisation-safe high-torque motor. A prototype is built and tested to verify the FEM results experimentally. The experimental results show a prototype magnetically resilient to permanent demagnetisation and with higher efficiencies at field weakening when compared with an equivalent REE machine.

This paper investigates the thermal performance of modular flux-switching permanent magnet (FSPM) machines under forced air cooling. Unlike conventional designs with continuous stator iron core, the modular configuration with segmented stator core introduces flux gaps between stator segments that can be used as extra cooling channels to increase the internal heat exchange surface area. To assess the impact of this innovative cooling design, models with varying flux gap widths (0–8 mm) were analysed using 3D computational fluid dynamics (CFD) modelling. Results indicate that at constant inlet air speed, the lowest machine temperature is achieved at 1 mm flux gap. Under constant pressure loss, the optimal cooling is achieved at a 4 mm flux gap. Although extreme flux gap widths hinder the cooling efficiency, the modular FSPM machines still outperform their nonmodular counterparts thermally. The study also examines the effect of rotor speed, revealing that higher speeds induce greater turbulence and reduce machine temperature, particularly beyond 2800 rpm, albeit with increased system pressure loss. The CFD simulation results were validated through a series of thermal experiments, confirming the accuracy of the CFD models and demonstrating the feasibility of using flux gaps as cooling channels in modular FSPM machines.

Non-singular fast terminal sliding mode control (NFTSMC) is a promising control method for permanent magnet synchronous motor (PMSM) control due to its fast convergence speed. However, the presence of unknown disturbances poses a significant challenge to its performance. This article focuses on the fast position tracking and improved anti-disturbance performance for PMSM control system. The main contribution is a composite logarithmic sliding mode control (CLnSMC) scheme incorporating a disturbance observer (DOB) based on a novel sliding mode reaching law. This approach effectively mitigates the impact of unknown disturbances on the control system. The proposed scheme stands out by effectively integrating a disturbance observer (DOB) into the traditional LnSMC framework. This integration not only enhances the control performance but also enables the direct estimation and suppression of complex disturbances, thereby improving the robustness and reliability of the control system. Extensive simulation and semi-physical experimental studies have been carried out to verify the effectiveness of the proposed control strategy. The results show that the proposed CLnSMC outperforms NFTSMC in both transient response and steady-state performance.