Current Issue : October - December Volume : 2015 Issue Number : 4 Articles : 5 Articles
This paper presents a distributed approach to optimal power flow (OPF) in an electrical network, suitable for\napplication in a future smart grid scenario where access to resource and control is decentralized. The non-convex OPF\nproblem is solved by an augmented Lagrangian method, similar to the widely known ADMM algorithm, with the key\ndistinction that penalty parameters are constantly increased. A (weak) assumption on local solver reliability is required\nto always ensure convergence. A certificate of convergence to a local optimum is available in the case of bounded\npenalty parameters. For moderate sized networks (up to 300 nodes, and even in the presence of a severe partition of\nthe network), the approach guarantees a performance very close to the optimum, with an appreciably fast\nconvergence speed. The generality of the approach makes it applicable to any (convex or non-convex) distributed\noptimization problem in networked form. In the comparison with the literature, mostly focused on convex SDP\napproximations, the chosen approach guarantees adherence to the reference problem, and it also requires a smaller\nlocal computational complexity effort....
Femtocell is a novel technology that is used for escalating indoor coverage as well as the capacity of traditional cellular\nnetworks. However, interference is the limiting factor for performance improvement due to co-channel deployment\nbetween macrocells and femtocells. The traditional network planning is not feasible because of the random deployment\nof femtocells. Therefore, self-organization approaches are the key to having successful deployment of femtocells.\nThis study presents the joint resource block (RB) and power allocation task for the two-tier femtocell network in a\nself-organizing manner, with the concern to minimizing the impact of interference and maximizing the energy\nefficiency. In this study, we analyze the performance of the system in terms of the energy efficiency, which is\ncomposed of both the transmission and circuit power. Most of the previous studies investigate the performance\nregarding the throughput requirement of the two-tier femtocell network while the energy efficiency aspect is\nlargely ignored. Here, the joint allocation task is modeled as a non-cooperative game which is demonstrated to\nexhibit pure and unique Nash equilibrium. In order to reduce the complexity of the proposed non-cooperative\ngame, the joint RB and power allocation task is divided into two subproblems: an RB allocation and a particle\nswarm optimization-based power allocation. The analysis of the proposed game is carried out in terms of not\nonly energy efficiency but also throughput. With practical 3rd Generation Partnership Project (3GPP) Long-Term\nEvolution (LTE) parameters, the simulation results illustrate the superior performance of the proposed game as\ncompared to the traditional methods. Also, the comparison is carried out with the joint allocation scheme which\nonly considers the throughput as the objective function. The results illustrate that significant performance\nimprovement is achieved in terms of energy efficiency with slight loss in the throughput. The analysis in regard\nto energy efficiency and throughput of the two-tier femtocell network is carried out in terms of the performance\nmetrics, which include convergence, impact of varying RBs, impact of femtocell density, and the fairness index....
Noisy low resolution (LR) images are always obtained in real applications, but many existing image magnification\nalgorithms can not get good result from a noisy LR image. We propose a two-step image magnification algorithm to\nsolve this problem. The proposed algorithm takes the advantages of both regularization-based method and\nlearning-based method. The first step is based on total variation (TV) regularization and the second step is based on\nsparse representation. In the first step, we add a constraint on the TV regularization model to magnify the LR image\nand at the same time to suppress the noise in it. In the second step, we propose an order-changed dictionary training\nalgorithm to train the dictionaries which is dominated by texture details. Experimental results demonstrate that the\nproposed algorithm performs better than many other algorithms when the noise is not serious. The proposed\nalgorithm can also provide better visual quality on natural LR images....
Bayesian filter is an efficient approach for multi-target tracking in the presence of clutter. Recently, considerable\nattention has been focused on probability hypothesis density (PHD) filter, which is an intensity approximation of\nthe multi-target Bayesian filter. However, PHD filter is inapplicable to cases in which target detection probability\nis low. The use of this filter may result in a delay in data processing because it handles received measurements\nperiodically, once every sampling period. To track multiple targets in the case of low detection probability and to\nhandle received measurements in real time, we propose a sequential measurement-driven Bayesian filter. The\nproposed filter jointly propagates the marginal distributions and existence probabilities of each target in the filter\nrecursion. We also present an implementation of the proposed filter for linear Gaussian models. Simulation results\ndemonstrate that the proposed filter can more accurately track multiple targets than the Gaussian mixture PHD\nfilter or cardinalized PHD filter....
Electric grids are interconnected complex systems consisting of generation, transmission, distribution, and active\nloads, recently called prosumers as they produce and consume electric energy. Additionally, these encompass a\nvast array of equipment such as machines, power transformers, capacitor banks, power electronic devices, motors,\netc. that are continuously evolving in their demand characteristics. Given these conditions, signal processing is\nbecoming an essential assessment tool to enable the engineer and researcher to understand, plan, design, and\noperate the complex and smart electronic grid of the future. This paper focuses on recent developments associated\nwith signal processing applied to power system analysis in terms of characterization and diagnostics. The following\ntechniques are reviewed and their characteristics and applications discussed: active power system monitoring,\nsparse representation of power system signal, real-time resampling, and time-frequency (i.e., wavelets) applied to\npower fluctuations....
Loading....