Accelerated Particle Swarm Optimization for Photovoltaic Maximum Power Point Tracking under Partial Shading Conditions

This paper presents an accelerated particle swarm optimization (PSO)-based maximum power point tracking (MPPT) algorithm to track global maximum power point (MPP) of photovoltaic (PV) generation under partial shading conditions. Conventional PSO-based MPPT algorithms have common weaknesses of a long convergence time to reach the global MPP and oscillations during the searching. The proposed algorithm includes a standard PSO and a perturb-and-observe algorithm as the accelerator. It has been experimentally tested and compared with conventional MPPT algorithms. Experimental results show that the proposed MPPT method is effective in terms of high reliability, fast dynamic response, and high accuracy in tracking the global MPP

On the crossing numbers of Km□Cn and Km,l□Pn

AbstractRingeisen and Beineke have proved that cr(C3□Cn)=n and cr(K4□Cn)=3n. Bokal has proved that cr(K1,l□Pn)=(n-1)⌊l2⌋⌊l-12⌋. In this paper we study the crossing numbers of Km□Cn and Km,l□Pn, and show (i) cr(Km□Cn)⩾n·cr(Km+2) for n⩾3 and m⩾5; (ii) cr(Km□Cn)⩽n4⌊m+22⌋⌊m+12⌋⌊m2⌋⌊m-12⌋ for m=5,6,7 and for m⩾8 with even n⩾4, and equality holds for m=5,6,7 and for m=8,9,10 with even n⩾4 and (iii) cr(Km,l□Pn)⩽(n-1)(⌊m+22⌋⌊m+12⌋⌊l+22⌋⌊l+12⌋-ml)+2(⌊m+12⌋⌊m2⌋⌊l+12⌋⌊l2⌋-⌊m2⌋⌊l2⌋) for min(m,l)⩾2, and equality holds for min(m,l)=2

Design and Assessment of an Electric Vehicle Powertrain Model Based on Real-World Driving and Charging Cycles

In this paper, an advanced analytical model for an electric vehicle (EV) powertrain has been developed to illustrate the vehicular dynamics by combining electrical and mechanical models in the analysis. This study is based on a Nissan Leaf EV. In the electrical system, the powertrain has various components including a battery pack, a battery management system, a dc/dc converter, a dc/ac inverter, a permanent magnet synchronous motor, and a control system. In the mechanical system, it consists of power transmissions, axial shaft, and vehicle wheels. Furthermore, the driving performance of the Nissan Leaf is studied through the real-world driving tests and simulation tests in MATLAB/Simulink. In the analytical model, the vehicular dynamics is evaluated against changes in the vehicle velocity and acceleration, state of charge of the battery, and the motor power. Finally, a number of EVs involved in the power dispatch is studied. The greenhouse gas emissions of the EV are analyzed according to various energy power and driving features, and compared with the conventional internal combustion engine vehicle. In this case, Nissan Leaf is a pure EV. For a given drive cycle, Nissan Leaf can reduce CO2 emissions by 70%, depending on the way electricity is generated and duty cycles

SparseNeuS: Fast Generalizable Neural Surface Reconstruction from Sparse Views

We introduce SparseNeuS, a novel neural rendering based method for the task of surface reconstruction from multi-view images. This task becomes more difficult when only sparse images are provided as input, a scenario where existing neural reconstruction approaches usually produce incomplete or distorted results. Moreover, their inability of generalizing to unseen new scenes impedes their application in practice. Contrarily, SparseNeuS can generalize to new scenes and work well with sparse images (as few as 2 or 3). SparseNeuS adopts signed distance function (SDF) as the surface representation, and learns generalizable priors from image features by introducing geometry encoding volumes for generic surface prediction. Moreover, several strategies are introduced to effectively leverage sparse views for high-quality reconstruction, including 1) a multi-level geometry reasoning framework to recover the surfaces in a coarse-to-fine manner; 2) a multi-scale color blending scheme for more reliable color prediction; 3) a consistency-aware fine-tuning scheme to control the inconsistent regions caused by occlusion and noise. Extensive experiments demonstrate that our approach not only outperforms the state-of-the-art methods, but also exhibits good efficiency, generalizability, and flexibility.Comment: Project page: https://www.xxlong.site/SparseNeuS

A novel HVDC circuit breaker for HVDC application

Hybrid high voltage direct current circuit breakers (DCCBs) are capable of interrupting fault current within a few milliseconds, but this technology has high capital cost, especially in a meshed HVDC grid. To increase the economic competitiveness of hybrid DCCBs, this paper proposes a capacitor commutated dc circuit breaker (CCCB). The CCCB mainly comprises an auxiliary branch with a fast dis-connector in series with semiconductor devices and the main branch with the series connection of a dc capacitor and diode valves. This paper provides a detailed depiction of the CCCB. The topology and operating principles are discussed. The impact of snubber circuits and stray inductances on the commutation process is analyzed. The general sizing method for the main components in the CCCB is detailed. Reclosing to transmission lines with different operating conditions is studied. Several extended topologies are proposed to further reduce the semiconductor cost and on-state operation power loss. The power loss and cost of CCCB are assessed. Extensive simulations on PSCAD/EMTDC verified the dc fault isolation and reclosing of the CCCB

Centroidal Voronoi Tesselation of Line Segments and Graphs

Centroidal Voronoi Tesselation (CVT) of points has many applications in geometry processing, including re-meshing and segmentation to name but a few. In this paper, we propose a new extension of CVT, generalized to graphs. Given a graph and a 3D polygonal surface, our method optimizes the placement of the vertices of the graph in such a way that the graph segments best approximate the shape of the surface. We formulate the computation of CVT for graphs as a continuous variational problem, and present a simple approximated method to solve this problem. Our method is robust in the sense that it is independent of degeneracies in the input mesh, such as skinny triangles, T-junctions, small gaps or multiple connected components. We present some applications, to skeleton fitting and to shape segmentation