Three Step Homotopy Perturbation Iteration Algorithm for Nonlinear Equations

In this paper, an improved iterative three step method with sixth order convergence based on Homotopy perturbation technique is suggested. It is named three step Homotopy Perturbation iteration algorithm (TSHPI). Four nonlinear test examples are solved with the proposed method and compared to other methods. The obtained results show that TSHPI method is a powerful tool and can generate highly accurate solutions with less iteration

Adjustable dynamic range for paper reduction schemes in large-scale MIMO-OFDM systems

In a multi-input-multi-output (MIMO) communication system there is a necessity to limit the power that the output antenna amplifiers can deliver. Their signal is a combination of many independent channels, so the demanded amplitude can peak to many times the average value. The orthogonal frequency division multiplexing (OFDM) system causes high peak signals to occur because many subcarrier components are added by an inverse discrete Fourier transformation process at the base station. This causes out-of-band spectral regrowth. If simple clipping of the input signal is used, there will be in-band distortions in the transmitted signals and the bit error rate will increase substantially. This work presents a novel technique that reduces the peak-to-average power ratio (PAPR). It is a combination of two main stages, a variable clipping level and an Adaptive Optimizer that takes advantage of the channel state information sent from all users in the cell. Simulation results show that the proposed method achieves a better overall system performance than that of conventional peak reduction systems in terms of the symbol error rate. As a result, the linear output of the power amplifiers can be minimized with a great saving in cost

Automated Fault Location In Smart Distribution Systems

Fault location in distribution systems is a critical component of outage management and service restoration, which directly impacts feeder reliability and quality of the electricity supply. Improving fault location methods supports the Department of Energy (DOE) “Grid 2030” initiatives for grid modernization by improving reliability indices of the network. Improving customer average interruption duration index (CAIDI) and system average interruption duration index (SAIDI) are direct advantages of utilizing a suitable fault location method. As distribution systems are gradually evolving into smart distribution systems, application of more accurate fault location methods based on gathered data from various Intelligent Electronic Devices (IEDs) installed along the feeders is quite feasible. How this may be done and what is the needed methodology to come to such solution is raised and then systematically answered. To reach this goal, the following tasks are carried out: 1) Existing fault location methods in distribution systems are surveyed and their strength and caveats are studied. 2) Characteristics of IEDs in distribution systems are studied and their impacts on fault location method selection and implementation are detailed. 3) A systematic approach for selecting optimal fault location method is proposed and implemented to pinpoint the most promising algorithms for a given set of application requirements. 4) An enhanced fault location method based on voltage sag data gathered from IEDs along the feeder is developed. The method solves the problem of multiple fault location estimations and produces more robust results. 5) An optimal IED placement approach for the enhanced fault location method is developed and practical considerations for its implementation are detailed

Automated Fault Location In Smart Distribution Systems

Fault location in distribution systems is a critical component of outage management and service restoration, which directly impacts feeder reliability and quality of the electricity supply. Improving fault location methods supports the Department of Energy (DOE) “Grid 2030” initiatives for grid modernization by improving reliability indices of the network. Improving customer average interruption duration index (CAIDI) and system average interruption duration index (SAIDI) are direct advantages of utilizing a suitable fault location method. As distribution systems are gradually evolving into smart distribution systems, application of more accurate fault location methods based on gathered data from various Intelligent Electronic Devices (IEDs) installed along the feeders is quite feasible. How this may be done and what is the needed methodology to come to such solution is raised and then systematically answered. To reach this goal, the following tasks are carried out: 1) Existing fault location methods in distribution systems are surveyed and their strength and caveats are studied. 2) Characteristics of IEDs in distribution systems are studied and their impacts on fault location method selection and implementation are detailed. 3) A systematic approach for selecting optimal fault location method is proposed and implemented to pinpoint the most promising algorithms for a given set of application requirements. 4) An enhanced fault location method based on voltage sag data gathered from IEDs along the feeder is developed. The method solves the problem of multiple fault location estimations and produces more robust results. 5) An optimal IED placement approach for the enhanced fault location method is developed and practical considerations for its implementation are detailed

Launch window analysis of satellites in high eccentricity or large circular orbits

Numerical methods and computer programs for studying the stability and evolution of orbits of large eccentricity are presented. Methods for determining launch windows and target dates are developed. Mathematical models are prepared to analyze the characteristics of specific missions

Measurement error models for time series

Estimation for multivariate linear measurement error models with serially correlated observations is addressed;The asymptotic properties of some standard linear errors-in-variables regression parameter estimators are developed under an ultrastructural model in which the random components of the model follow a linear process. Under the same assumptions, the asymptotic properties of weighted method-of-moments estimators are derived. The large-sample results rest on the asymptotic properties of the sum of a linear function and a quadratic function of a sequence of serially correlated random vectors;Maximum likelihood estimation for the normal structural and functional models is addressed. For each model, first- and second-derivative matrices of the log-likelihood functions are given and Newton-Raphson maximum likelihood estimation procedures are considered. For the structural model, the assumption that the random components follow a multivariate autoregressive moving average process is used to develop autoregressive moving average and state-space models for the observation sequence. The state-space representation of the structural model leads to innovation sequences and associated derivative sequences that provide the basis for a Newton-Raphson procedure for the estimation of regression parameters and autocovariance parameters of the structural model. A modified state-space approach leads to a similar procedure for the estimation for the functional model. An extension of the state-space approach to maximum likelihood estimation for a structural model with combined time series and cross-sectional data is given