Direct power control of a multilevel inverter based active power filter

A cascaded H-bridge multilevel inverter based active power filter with a novel direct power control is proposed in this paper. It can be directly connected to medium/high voltage power line without using the bulky transformer or passive filter. Due to the limited switching frequency (typically below 1 kHz) of high-power solid-state devices (GTO/IGCT), multiple synchronous/stationary reference frame current controllers are reviewed and derived. Based on this, a novel current controller is proposed for harmonic current elimination and system power factor compensation. Furthermore, a synchronous/stationary hybrid structure can be derived with fundamental de-coupling control. The instantaneous reactive power theory and synchronous reference frame based control are compared based on mathematical models. A direct power control concept is then derived and proposed. It is equivalent as the hybrid synchronous/stationary frame current controller, but has a simpler implementation. It has clear physical meaning and can be considered as a simplified version of the hybrid frame current controller. Simulations on a 4160 V/1.2 MVA system and experimental results on a 208 V/6 kVA laboratory prototype are presented to validate the proposed active power filter design

Collaborative Opportunistic Scheduling in Heterogeneous Networks: A Distributed Approach

We consider a collaborative opportunistic scheduling problem in a decentralized network with heterogeneous users. While most related researches focus on solutions for optimizing decentralized systems’ total performance, we proceed in another direction. Two problems are specifically investigated. (1) With heterogenous users having personal demands, is it possible to have it met by designing distributed opportunistic policies? (2) With a decentralized mechanism, how can we prevent selfish behaviors and enforce collaboration? In our research, we first introduce a multiuser network model along with a scheduling problem constrained by individual throughput requirement at each user’s side. An iterative algorithm is then proposed to characterize a solution for the scheduling problem, based on which collaborative opportunistic scheduling scheme is enabled. Properties of the algorithm, including convergence, will be discussed. Furthermore in order to keep the users staying with the collaboration state, an additional punishment strategy is designed. Therefore selfish deviation can be detected and disciplined so that collaboration is enforced. We demonstrate our main findings with both analysis and simulations

Applications of Tao General Difference in Discrete Domain

Numerical difference computation is one of the cores and indispensable in the modern digital era. Tao general difference (TGD) is a novel theory and approach to difference computation for discrete sequences and arrays in multidimensional space. Built on the solid theoretical foundation of the general difference in a finite interval, the TGD operators demonstrate exceptional signal processing capabilities in real-world applications. A novel smoothness property of a sequence is defined on the first- and second TGD. This property is used to denoise one-dimensional signals, where the noise is the non-smooth points in the sequence. Meanwhile, the center of the gradient in a finite interval can be accurately location via TGD calculation. This solves a traditional challenge in computer vision, which is the precise localization of image edges with noise robustness. Furthermore, the power of TGD operators extends to spatio-temporal edge detection in three-dimensional arrays, enabling the identification of kinetic edges in video data. These diverse applications highlight the properties of TGD in discrete domain and the significant promise of TGD for the computation across signal processing, image analysis, and video analytic.Comment: This paper is the application part of the paper "Tao General Differential and Difference: Theory and Application". The theory part of the paper is renamed as "A Theory of General Difference in Continuous and Discrete Domain", which is Arxived in arXiv:2305.08098v

Stress arch effect on the productivity of the vertical fractured well

Rock permeability impacts by effective stress. Permeability modulus is used to evaluate the level of permeability reduction due to effective stress change. And the permeability modulus is always obtained by the experiment which assumes that the overburden pressure is constant during production. Actually, the overburden pressure reduces during production due to stress arch effect and it is easy to form a stress arch in the overburden when the reservoir is small and soft compared with surrounding’s rock. Based on the definition of the permeability modulus, we obtain an expression between permeability modulus bγ considering stress arch effect and permeability modulus b0 without stress arch. There lies a linear ship between bγ and b0, which is also proved by the experiment data. Based on the relationship between bγ and b0, a delivery equation for vertical fractured well is established. Compared with the absolute open flow with stress arch ratio of 0, the absolute open flow increases by 2.87 %, 6.79 %, 12.32 %, 20.12 % and 25.44 % for the stress arch ratio of 0.12, 0.28, 0.5, 0.8 and 1, respectively, with permeability modulus b0 of 0.0397 MPa-1. And it increases by 7.31 %, 18.1 %, 34.88 %, 61.02 % and 79.97 % for the stress arch ratio of 0.12, 0.28, 0.5, 0.8 and 1, respectively, when b0= 1. So absolute open flow with high permeability modulus b0 is more sensitive to stress arch ratio. Stress arch also impacts the optimum fracture half-length. Vertical well has the maximum absolute open flow when it has the optimum fracture half-length. The maximum absolute open flow increases with the increasing of stress arch ratio, while optimum fracture half-length decreases with increasing of stress arch ratio for the same permeability modulus b0. Compared with case with no stress arch, the optimum fracture half-length reduces by 2.86 %, 5.7 %, 11.43 %, 17.14 % and 22.86 % for the stress arch ratio of 0.12, 0.28, 0.5, 0.8 and 1 respectively when b0 equals to 0.0397 MPa-1. While the maximum absolute open flow increases by 1.6 %, 3.8 %, 7.16 %, 12.02 % and 15.60 % for the stress arch ratio of 0.12, 0.28, 0.5, 0.8 and 1 respectively. Thus, vertical well considering stress arch needs smaller fracture half-length than that with no stress arch. Meanwhile, the maximum absolute open flow and optimum fracture conductivity both increase as stress arch ratio increases. Compared with the case without stress arch, the optimum fracture conductivity increases by 50 %, while the maximum absolute open flow increases by 21.40 % with stress arch ratio of 0.5 when b0 equals to 0.0397 MPa-1. The stress arch greatly impacts on the stress sensitive permeability, permeability modulus and well performance, which can’t be neglected especially in the low and ultra-low permeability reservoir

Diffusion Shape Prior for Wrinkle-Accurate Cloth Registration

Registering clothes from 4D scans with vertex-accurate correspondence is challenging, yet important for dynamic appearance modeling and physics parameter estimation from real-world data. However, previous methods either rely on texture information, which is not always reliable, or achieve only coarse-level alignment. In this work, we present a novel approach to enabling accurate surface registration of texture-less clothes with large deformation. Our key idea is to effectively leverage a shape prior learned from pre-captured clothing using diffusion models. We also propose a multi-stage guidance scheme based on learned functional maps, which stabilizes registration for large-scale deformation even when they vary significantly from training data. Using high-fidelity real captured clothes, our experiments show that the proposed approach based on diffusion models generalizes better than surface registration with VAE or PCA-based priors, outperforming both optimization-based and learning-based non-rigid registration methods for both interpolation and extrapolation tests.Comment: Project page: https://www-users.cse.umn.edu/~guo00109/projects/3dv2024

OccTransformer: Improving BEVFormer for 3D camera-only occupancy prediction

This technical report presents our solution, "occTransformer" for the 3D occupancy prediction track in the autonomous driving challenge at CVPR 2023. Our method builds upon the strong baseline BEVFormer and improves its performance through several simple yet effective techniques. Firstly, we employed data augmentation to increase the diversity of the training data and improve the model's generalization ability. Secondly, we used a strong image backbone to extract more informative features from the input data. Thirdly, we incorporated a 3D unet head to better capture the spatial information of the scene. Fourthly, we added more loss functions to better optimize the model. Additionally, we used an ensemble approach with the occ model BevDet and SurroundOcc to further improve the performance. Most importantly, we integrated 3D detection model StreamPETR to enhance the model's ability to detect objects in the scene. Using these methods, our solution achieved 49.23 miou on the 3D occupancy prediction track in the autonomous driving challenge.Comment: Innovation Award in the 3D Occupancy Prediction Challenge (CVPR23

Aggregated demand-side response in residential distribution areas based on tiered incentive prices

The residential area refers to the power supply area from distribution transformers to the end users that contains multiple types of flexible resources, such as photovoltaics, energy storage, and power users. Focusing on the challenge of insufficient demand response incentives to multiple types of users in residential distribution areas, a tiered incentive price-based demand-side aggregated response method is proposed in this paper. Users in residential distribution areas are classified with an improved k-means clustering method for obtaining typical types of users. Thereafter, initial scores of users are calculated, and their grades are assigned based on their scores. Corresponding tiered incentive prices are designed for different grades. On this basis, a leader–follower game is proposed to obtain the demand response base price, and tiered incentives are provided to users of different grades to increase their enthusiasm for participating in demand response. In the case study, an actual urban residential distribution area is studied. The results show that the proposed user clustering method has an accuracy of 99.8% in classifying users in a residential distribution area. In addition, the proposed method has better performance in terms of improving the benefit of the load aggregator and users in the residential distribution area compared with methods such as potential game, hidden Markov, and Monte Carlo. Specifically, from the results, the benefit of load aggregators is increased by 101.96%, 76.07%, and 112.37%, and the income of the users is increased by 54.51%, 36.94%, and 64.91%