This paper reports on a study of a novel magnetic lead screw (MLS) with a quasi-Halbach array and a rotor made of a soft magnetic composite (SMC). The proposed topology has an array formed from a helix of discretized pieces of permanent magnets (PMs) axially and radially magnetized in its slider. The rotor is formed from stacked discs of a soft magnetic composite and is experimentally tested. These are arranged to form the lead screw. Using finite element analysis (FEA) and static magnetic simulations, prototypes were assessed. The results show a good agreement in favour of the quasi-Halbach MLS with an SMC rotor.
Finite element formulations using the magnetic vector potential are numerically inefficient for modelling 3-D magnetic hysteresis, as they rely on computationally expensive inverse material models. This article presents a robust and efficient (H) over right arrow -formulation that overcomes this limitation by incorporating the direct form of the energy-based hysteresis model. A detailed implementation that resolves key numerical challenges, ensuring a well-conditioned system and a stable nonlinear solver via a quasi-Newton scheme is provided. Through a comparative analysis in both magnetostatic and magnetoquasistatic cases, the results show that the (H) over right arrow -formulation achieves a significant performance gain. While maintaining the same level of accuracy as the (A) over right arrow -formulation, it reduces the required computation time by a factor of over five when energy-based hysteresis models are used. This work establishes the (H) over right arrow -formulation as a viable alternative to accurately and efficiently perform 3-D simulations where hysteretic effects are non-negligible.
To address the real-time assessment requirements for cumulative deformation and mechanical damage in transformer windings under multiple short-circuit impacts, this paper proposes a fast method for elastoplastic bending of transformer windings considering the cumulative effect of force transmission. During transformer operation, axial electromagnetic forces in windings achieve dynamic transmission and superposition through the force chain network formed by spacers, reconstructing the electromagnetic force distribution characteristics of transformer windings. To quantify the influence of this effect, this study analyses force transmission mechanisms and quantifies the interlayer transmission coefficient through a combined approach of finite element simulations and pressurisation tests. Subsequently, the image method for rapid electromagnetic force calculation is refined by introducing the interlayer transmission coefficient, thereby improving the accuracy of axial electromagnetic force distribution. Following, a fast computational model for elastoplastic bending is established to analyse the cumulative deformation patterns of windings. The model's accuracy is validated through multiple short-circuit impact tests. The results demonstrate that the interlayer transmission coefficient stabilises around 0.9, leading to an increase in the peak axial electromagnetic force of each disc winding after correction; the computational results for elastoplastic bending under cumulative effects align well with experimental data, with significantly enhanced computational efficiency.
A robust, unconditionally stable, and accurate single-phase P-class phasor measurement unit (PMU) algorithm is proposed in this paper. It relies on fixed-tuned cascaded delayed signal cancellation (CDSC) operators. The designed CDSC operators can suppress the harmonics as demanded by the IEC/IEEE 60255-118-1:2018 standard. To tackle the fundamental frequency variation, two similar CDSC blocks are cascaded via fixed-tuned Park transformation. This algorithm avoids the use of integrators, matrix inversion, feedback paths and computationally demanding trigonometric functions or their inverse operations in its structure. It can be used for PMU data rates up to 100 frames per second (fps) for 50 Hz power systems. Among all types of standard tests, the maximum total vector error, frequency error, and rate of change of frequency error are obtained as 0.0774%, 1.6731 mHz, and 0.0415 Hz/s, respectively. This algorithm also meets the requirements of the input step test with a broad safety margin. Moreover, it can provide results within the allowable error limits for a signal-to-noise ratio greater than about 55 dB in the signal frequency test.
Currently, partial discharge (PD) signal denoising algorithms face issues such as complex parameter selection, incomplete noise suppression, and low operational efficiency. A hybrid denoising method (SVMD_STNLM_GSTVD) is proposed to address these issues, specifically integrating Sequential Variational Mode Decomposition (SVMD), Structure Tensor-based Non-Local Means (STNLM), and Group Sparse Total Variation Denoising (GSTVD). First, SVMD is used to adaptively decompose the original noisy PD signal, and then the correlation coefficient criterion is used to distinguish the modal components into high-quality components dominated by effective signals and low-quality components dominated by noise, achieving preliminary separation of signal and noise. For high-quality components, in order to preserve signal structural features and effectively suppress noise, STNLM is introduced, and the chaotic sparrow search algorithm (CSSA) is used to intelligently optimise its key parameters. For low-quality components, the GSTVD method is adopted, which introduces group sparse regularisation terms and total variational constraints to remove noise and avoid excessive smoothing of signal edge features. Finally, the SVMD_STNLM_GSTVD denoised signal is subjected to secondary filtering using a soft threshold function to further eliminate residual noise and improve the overall denoising effect. The simulation and actual measurement results show that the proposed method outperforms the compared methods in terms of signal-to-noise ratio, waveform similarity, root mean square error, and running time. It can effectively extract PD features and provide a reliable basis for subsequent fault diagnosis.
