Distribution feeder reconfiguration for loss minimization based on modified honey bee mating optimization algorithm

This paper presents an efficient algorithm for multi-objective distribution feeder reconfiguration based on Modified Honey Bee Mating Optimization (MHBMO) approach. The main objective of the Distribution feeder reconfiguration (DFR) is to minimize the real power loss, deviation of the nodes’ voltage. Because of the fact that the objectives are different and no commensurable, it is difficult to solve the problem by conventional approaches that may optimize a single objective. So the metahuristic algorithm has been applied to this problem. This paper describes the full algorithm to Objective functions paid, The results of simulations on a 32 bus distribution system is given and shown high accuracy and optimize the proposed algorithm in power loss minimization.

A hybrid evolutionary algorithm based on ACO and SA for distribution feeder reconfiguration

This paper deals with an efficient hybrid evolutionary optimization algorithm in accordance with combining the ant colony optimization (ACO) and the simulated annealing (SA), so called ACO-SA. The distribution feeder reconfiguration (DFR) is known as one of the most important control schemes in the distribution networks, which can be affected by distributed generations (DGs) for the multi-objective DFR. In such a case, DGs is used to minimize the real power loss, the deviation of nodes voltage and the number of switching operations. The approach is carried out on a real distribution feeder, where the simulation results show that the proposed evolutionary optimization algorithm is robust and suitable for solving the DFR problem.

Distribution harmonic state estimation based on a hybrid PSO and SA algorithm considering parameters uncertainty

This paper presents a new algorithm based on a Hybrid Particle Swarm Optimization (PSO) and Simulated Annealing (SA) called PSO-SA to estimate harmonic state variables in distribution networks. The proposed algorithm performs estimation for both amplitude and phase of each harmonic currents injection by minimizing the error between the measured values from Phasor Measurement Units (PMUs) and the values computed from the estimated parameters during the estimation process. The proposed algorithm can take into account the uncertainty of the harmonic pseudo measurement and the tolerance in the line impedances of the network as well as uncertainty of the Distributed Generators (DGs) such as Wind Turbines (WT). The main feature of proposed PSO-SA algorithm is to reach quickly around the global optimum by PSO with enabling a mutation function and then to find that optimum by SA searching algorithm. Simulation results on IEEE 34 bus radial and a realistic 70-bus radial test networks are presented to demonstrate the speed and accuracy of proposed Distribution Harmonic State Estimation (DHSE) algorithm is extremely effective and efficient in comparison with the conventional algorithms such as Weight Least Square (WLS), Genetic Algorithm (GA), original PSO and Honey Bees Mating Optimization (HBMO) algorithm.

Damage evaluation based on a wave energy flow map using multiple PZT sensors

A new wave energy flow (WEF) map concept was proposed in this work. Based on it, an improved technique incorporating the laser scanning method and Betti’s reciprocal theorem was developed to evaluate the shape and size of damage as well as to realize visualization of wave propagation. In this technique, a simple signal processing algorithm was proposed to construct the WEF map when waves propagate through an inspection region, and multiple lead zirconate titanate (PZT) sensors were employed to improve inspection reliability. Various damages in aluminum and carbon fiber reinforced plastic laminated plates were experimentally and numerically evaluated to validate this technique. The results show that it can effectively evaluate the shape and size of damage from wave field variations around the damage in the WEF map.

Fluid-structure interaction analysis by coupled FE-SPH model based on a novel searching algorithm

Fluid–Structure Interaction (FSI) problem is significant in science and engineering, which leads to challenges for computational mechanics. The coupled model of Finite Element and Smoothed Particle Hydrodynamics (FE-SPH) is a robust technique for simulation of FSI problems. However, two important steps of neighbor searching and contact searching in the coupled FE-SPH model are extremely time-consuming. Point-In-Box (PIB) searching algorithm has been developed by Swegle to improve the efficiency of searching. However, it has a shortcoming that efficiency of searching can be significantly affected by the distribution of points (nodes in FEM and particles in SPH). In this paper, in order to improve the efficiency of searching, a novel Striped-PIB (S-PIB) searching algorithm is proposed to overcome the shortcoming of PIB algorithm that caused by points distribution, and the two time-consuming steps of neighbor searching and contact searching are integrated into one searching step. The accuracy and efficiency of the newly developed searching algorithm is studied on by efficiency test and FSI problems. It has been found that the newly developed model can significantly improve the computational efficiency and it is believed to be a powerful tool for the FSI analysis.

Nanoparticle manipulation in plasma-assisted nanofabrication of electron field emitters based on single crystalline carbon nanotip patterns

Nanoparticle manipulation by various plasma forces in near-substrate areas of the Integrated Plasma-Aided Nanofabrication Facility (IPANF) is investigated. In the IPANF, high-density plasmas of low-temperature rf glow discharges are sustained. The model near-substrate area includes a variable-length pre-sheath, where a negatively charged nanoparticle is accelerated, and a self-consistent collisionless sheath with a repulsive electrostatic potential. Conditions enabling the nanoparticle to overcome the repulsive barrier and deposit onto the substrate are investigated numerically and experimentally. Under certain conditions the momentum gained by the nanoparticle in the pre-sheath area appears to be sufficient for the driving ion drag force to outbalance the repulsive electrostatic and thermophoretic forces. Numerical results are applied for the explanation of size-selective nanoparticle deposition in the Ar+H2+CH4 plasma-assisted chemical vapor deposition of various carbon nanostructure patterns for electron field emitters and are cross-referenced by the field emission scanning electron microscopy. It is shown that the nanoparticles can be efficiently manipulated by the temperature gradient-controlled thermophoretic force. Experimentally, the temperature gradients in the near-substrate areas are measured in situ by means of the temperature gradient probe and related to the nanofilm fabrication conditions. The results are relevant to plasma-assisted synthesis of numerous nanofilms employing structural incorporation of the plasma-grown nanoparticles, including but not limited to nanofabrication of ordered single-crystalline carbon nanotip arrays for electron field emission applications.

Low-temperature assembly of ordered carbon nanotip arrays in low-frequency, high-density inductively coupled plasmas

High-density inductively coupled plasma (ICP)-assisted self-assembly of the ordered arrays of various carbon nanostructures (NS) for the electron field emission applications is reported. Carbon-based nano-particles, nanotips, and pyramid-like structures, with the controllable shape, ordering, and areal density are grown under remarkably low process temperatures (260-350 °C) and pressures (below 0.1 Torr), on the same Ni-based catalyst layers, in a DC bias-controlled floating temperature regime. A high degree of positional and directional ordering, elevated sp2 content, and a well-structured graphitic morphology are achieved without the use of pre-patterned or externally heated substrates.

Perturbing event logs to identify cost reduction opportunities: a genetic algorithm-based approach

Organisations are constantly seeking new ways to improve operational efficiencies. This research study investigates a novel way to identify potential efficiency gains in business operations by observing how they are carried out in the past and then exploring better ways of executing them by taking into account trade-offs between time, cost and resource utilisation. This paper demonstrates how they can be incorporated in the assessment of alternative process execution scenarios by making use of a cost environment. A genetic algorithm-based approach is proposed to explore and assess alternative process execution scenarios, where the objective function is represented by a comprehensive cost structure that captures different process dimensions. Experiments conducted with different variants of the genetic algorithm evaluate the approach's feasibility. The findings demonstrate that a genetic algorithm-based approach is able to make use of cost reduction as a way to identify improved execution scenarios in terms of reduced case durations and increased resource utilisation. The ultimate aim is to utilise cost-related insights gained from such improved scenarios to put forward recommendations for reducing process-related cost within organisations.

Water-based nanoparticulate solar cells using a diketopyrrolopyrrole donor polymer

Organic photovoltaic devices with either bulk heterojunction (BHJ) or nanoparticulate (NP) active layers have been prepared from a 1:2 blend of (poly{3,6-dithiophene-2-yl-2,5-di(2-octyldodecyl)-pyrrolo[3,4-c]pyrrole-1, 4-dione-alt-naphthalene}) (PDPP-TNT) and the fullerene acceptor, ([6,6]-phenyl C71-butyric acid methyl ester) (PC70BM). Atomic force microscopy (AFM) and scanning electron microscopy (SEM) have been used to investigate the morphology of the active layers of the two approaches. Mild thermal treatment of the NP film is required to promote initial joining of the NPs in order for the devices to function, however the NP structure is retained. Consequently, whereas gross phase segregation of the active layer occurs in the BHJ device spin cast from chloroform, the nanoparticulate approach retains control of the material domain sizes on the length scale of exciton diffusion in the materials. As a result, NP devices are found to generate more than twice the current density of BHJ devices and have a substantially greater overall efficiency. The use of aqueous nanoparticulate dispersions offers a promising approach to control the donor acceptor morphology on the nanoscale with the benefit of environmentally- friendly, solution-based fabrication. © 2014 the Owner Societies.

Simulation-based density estimation for time series using covariate data

This paper proposes a simulation-based density estimation technique for time series that exploits information found in covariate data. The method can be paired with a large range of parametric models used in time series estimation. We derive asymptotic properties of the estimator and illustrate attractive finite sample properties for a range of well-known econometric and financial applications.

A Memetic Algorithm for the location-based continuously operating reference stations placement problem in Network Real-Time Kinematic

Network Real-Time Kinematic (NRTK) is a technology that can provide centimeter-level accuracy positioning services in real time, and it is enabled by a network of Continuously Operating Reference Stations (CORS). The location-oriented CORS placement problem is an important problem in the design of a NRTK as it will directly affect not only the installation and operational cost of the NRTK, but also the quality of positioning services provided by the NRTK. This paper presents a Memetic Algorithm (MA) for the location-oriented CORS placement problem, which hybridizes the powerful explorative search capacity of a genetic algorithm and the efficient and effective exploitative search capacity of a local optimization. Experimental results have shown that the MA has better performance than existing approaches. In this paper we also conduct an empirical study about the scalability of the MA, effectiveness of the hybridization technique and selection of crossover operator in the MA.

Reliable fault diagnosis for incipient low-speed bearings using fault feature analysis based on a binary bat algorithm

In this paper, we propose a highly reliable fault diagnosis scheme for incipient low-speed rolling element bearing failures. The scheme consists of fault feature calculation, discriminative fault feature analysis, and fault classification. The proposed approach first computes wavelet-based fault features, including the respective relative wavelet packet node energy and entropy, by applying a wavelet packet transform to an incoming acoustic emission signal. The most discriminative fault features are then filtered from the originally produced feature vector by using discriminative fault feature analysis based on a binary bat algorithm (BBA). Finally, the proposed approach employs one-against-all multiclass support vector machines to identify multiple low-speed rolling element bearing defects. This study compares the proposed BBA-based dimensionality reduction scheme with four other dimensionality reduction methodologies in terms of classification performance. Experimental results show that the proposed methodology is superior to other dimensionality reduction approaches, yielding an average classification accuracy of 94.9%, 95.8%, and 98.4% under bearing rotational speeds at 20 revolutions-per-minute (RPM), 80 RPM, and 140 RPM, respectively.