93 resultados para fuel and power generation


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It is anticipated that the current workforce of teachers in Victoria, Australia will retire within the next 5-15 years. The paradox for teachers at the career entry point is that while they are expected to quickly assume responsibility for education in this state, beginning teachers are reporting dissatisfaction with teaching and describing it as an ‘unprofessional’ profession. Drawing from recently commissioned research for the Victorian Institute of Teaching, a study of sixty beginning teachers and a micro study of the ‘internship’ experience of teacher educators, this paper explores the consequences of what counts as professional knowledge. By problematising identity issues for beginning teachers it is hoped that greater understanding of the complexities of their realities is revealed. The aspirations for the (re) generation of a profession are entangled in discordant displacement of meanings of what it is to become a teacher. What do ‘othering’ and power(less) positions of beginning teachers mean for the immediate future of the profession? What then are the implications for school contexts, colleague support and pre-service teacher education?

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Previous research suggests that there is a relationship between social contexts (e.g., economic growth, engagement in wars) and motives within populations. In particular, high achievement motive is associated with subsequent economic growth, which in turn increases power motive. Increased national achievement and power motives have been argued to precede social changes that lead to decreased affiliation motives, and engagement in wars. The present study aimed to examine differences in achievement, power, and affiliation motives between 266 college students in China (a nation with sustained high economic growth) and 255 college students in the USA (a nation with previously strong but now slowing economic growth, and engaged in war). Analysis of personal strivings suggested that Chinese college students showed significantly higher levels of achievement motive than the American college students, but American college students showed significantly higher levels of affiliation motive than Chinese college students. Overall, males exhibited higher achievement motivation than females. No significant interaction effects were found for gender by location for any of the three motives. The findings are discussed in relation to previous research.

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Data analysis using intelligent systems is a key solution to many industrial problems. In this paper, a mutation-based evolving artificial neural network, which is based on an integration of the Fuzzy ARTMAP (FAM) neural network and evolutionary programming (EP), is proposed. The proposed FAMEP model is applied to detect and classify possible faults from a number of sensory signals of a circulating water system in a power generation plant. The efficiency of FAM-EP is assessed and compared with that of the original FAM network in terms of classification accuracy as well as network complexity. In addition, the bootstrap method is used to quantify the performance statistically. The results positively demonstrate the usefulness of FAM-EP in tackling data classification problems.

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This paper describes the application of an adaptive neural network, called Fuzzy ARTMAP (FAM), to handle fault prediction and condition monitoring problems in a power generation station. The FAM network, which is supplemented with a pruning algorithm, is used as a classifier to predict different machine conditions, in an off-line learning mode. The process under scrutiny in the power plant is the Circulating Water (CW) system, with prime attention to monitoring the heat transfer efficiency of the condensers. Several phases of experiments were conducted to investigate the `optimum' setting of a set of parameters of the FAM classifier for monitoring heat transfer conditions in the power plant.

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Positive Unit commitment and economic dispatch are two important decisions in thermal power generation scheduling. The tasks involve determination and allocation of power generation to thermal units that minimize the total power generation cost and satisfy the production constraints.This paper presents a cascade Genetic Algorithm and Particle Swarm Optimization (GA-PSO) approach for solving thermal power generation scheduling based on a layered matrix encoding structure.The proposed hybrid method is compared to layered matrix encoding GA using the thermal power generation problem given in Williams [1] to demonstrate its effectiveness in generating an optimal, cost-effective power generation schedule.The results showed that cascade GA-PSO outperformed the layered matrix encoding GA in minimizing the total power production cost for unit commitment and power dispatch problems.

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A hybrid network, based on the integration of Fuzzy ARTMAP (FAM) and the Rectangular Basis Function Network (RecBFN), is proposed for rule learning and extraction problems. The underlying idea for such integration is that FAM operates as a classifier to cluster data samples based on similarity, while the RecBFN acts as a “compressor” to extract and refine knowledge learned by the trained FAM network. The hybrid network is capable of classifying data samples incrementally as well as of acquiring rules directly from data samples for explaining its predictions. To evaluate the effectiveness of the hybrid network, it is applied to a fault detection and diagnosis task by using a set of real sensor data collected from a Circulating Water (CW) system in a power generation plant. The rules extracted from the network are analyzed and discussed, and are found to be in agreement with experts’ opinions used in maintaining the CW system.

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This paper proposes an innovative optimized parametric method for construction of prediction intervals (PIs) for uncertainty quantification. The mean-variance estimation (MVE) method employs two separate neural network (NN) models to estimate the mean and variance of targets. A new training method is developed in this study that adjusts parameters of NN models through minimization of a PI-based cost functions. A simulated annealing method is applied for minimization of the nonlinear non-differentiable cost function. The performance of the proposed method for PI construction is examined using monthly data sets taken from a wind farm in Australia. PIs for the wind farm power generation are constructed with five confidence levels between 50% and 90%. Demonstrated results indicate that valid PIs constructed using the optimized MVE method have a quality much better than the traditional MVE-based PIs.

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Aim The purpose of this study was to determine the changes in running mechanics that occur when highly trained runners run barefoot and in a minimalist shoe, and specifically if running in a minimalist shoe replicates barefoot running.

Methods Ground reaction force data and kinematics were collected from 22 highly trained runners during overground running while barefoot and in three shod conditions (minimalist shoe, racing flat and the athlete's regular shoe). Three-dimensional net joint moments and subsequent net powers and work were computed using Newton-Euler inverse dynamics. Joint kinematic and kinetic variables were statistically compared between barefoot and shod conditions using a multivariate analysis of variance for repeated measures and standardised mean differences calculated.

Results There were significant differences between barefoot and shod conditions for kinematic and kinetic variables at the knee and ankle, with no differences between shod conditions. Barefoot running demonstrated less knee flexion during midstance, an 11% decrease in the peak internal knee extension and abduction moments and a 24% decrease in negative work done at the knee compared with shod conditions. The ankle demonstrated less dorsiflexion at initial contact, a 14% increase in peak power generation and a 19% increase in the positive work done during barefoot running compared with shod conditions.

Conclusions Barefoot running was different to all shod conditions. Barefoot running changes the amount of work done at the knee and ankle joints and this may have therapeutic and performance implications for runners.

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Renewable energy resources, especially wind power, are expected to provide a considerable portion of the world energy requirements in the near future. Large-scale wind power penetration impacts the electricity industry in many aspects and raises a number of technical challenges for the electricity network. A day-ahead network-constrained market clearing formulation is proposed which considers demand side resources. The proposed approach can provide flexible load profile and reduce the need for ramp up/down services by the conventional generators. This method can potentially facilitate a large penetration of wind power by shifting the wind power generation from the off-peak periods to the high-peak hours. The validity of the proposed approach has been verified using the IEEE 30 bus and 57 bus test systems.

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Effect of weighted vest suit worn during daily activities on running speed, jumping power, and agility in young men. J Strength Cond Res 26(11): 3030-3035, 2012-Previous weighted vest interventions using exercise in addition to hypergravity have been successful in improving postural balance and power production capacity. The purpose of this study was to investigate if hypergravity alone in daily activities excluding sporting activities is effective in improving neuromuscular performance in young adults. Eight male subjects (age = 32 [SD: 6] years, height = 178 [5] cm, and body mass = 81 [8] kg) wore weighted vests 3 d.wk-1 for 3 weeks during waking hours, excluding sporting activities. Control group comprised 9 male subjects (age = 32 [6] years, height = 179 [5] cm, and body mass = 83 [9] kg). Performance was assessed with countermovement jump (body mass normalized peak power), figure-of-8 running test (running time), and running velocity test at baseline and at the end of the intervention. At baseline, the groups did not differ from each other (multivariate analysis of variance [MANOVA] p = 0.828). A significant group × time interaction (MANOVA F = 5.1, p = 0.015) was observed for performance variables. Analysis of covariance indicated that the intervention improved the figureof- 8 running time (p = 0.016) (22.2 vs. 0.5%), whereas normalized peak power (0.0 vs. 1.6%) and running velocity (1.3 vs. 0.1%) were unaffected (p ≥ 0.095). Wearing weighted vests was effective in slightly improving agility-related performance in young men. Because the effect was small, applying hypergravity only during exercise probably suffices. It appears that a proper volume and intensity of hypergravity could be in the order of 5-10% body weight vest worn during up to 50% of the training sessions for a period of 3-4 weeks.

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Traditional distribution networks were not originally designed to accommodate power generation facilities. The installation of distributed generation (DG) units with significatn capacity in these passive networks can cause reverse power flows which will result in some conflicts with the operation of the existing protection system. In this context, utilities around the world have started establishing requirements to ensure safe and reliable interconnection of generators in low- and medium-voltage networks. Grid interconnection is presently one of the most important issues involving DG. This paper presents a critical review of the requirements adopted by distribution companies in selected countries such as the USA, the UK, germany and Australia to facilitate the connection of DG. Critical issues such as voltage regulation, islanding operation, dynamic interactions among DG and loads are discussed to identify a few points where attention is still needed to improve the reliability of distribution systems

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The phenomenal growth in economy experienced in developed countries throughout the 20th century has largely been driven by the availability of conventional energy sources for electricity generation. However, increased concern about fossil fuels and adverse effect of carbon dioxide emission in to atmosphere changed the conventional power system to a viable one by integrating renewable energy sources into the existing system. Among the Renewable Energy (RE) sources, wind energy is one of the fastest growing technologies in reducing the Green House Gas (GHG) emissions in to the atmosphere due to its continuous availability throughout a period. Hence, this paper discusses the performance of a wind-grid connected system in a semi-arid region by conducting a case study. Wilson promontory, one of the best locations for wind generation in Victoria is considered as a case study. Hybrid Optimization Model for Electric Renewable (HOMER) is used as a simulating tool for this analysis. This study also presents the influences of storage system in the proposed Hybrid Power System (HPS) allowing energy to be stored during higher generations or lower load demands. In addition this paper also discusses the major integration issues to facilitate the large scale wind energy into the grid for reliable power generation and distribution.

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A statistical optimized technique for rapid development of reliable prediction intervals (PIs) is presented in this study. The mean-variance estimation (MVE) technique is employed here for quantification of uncertainties related with wind power predictions. In this method, two separate neural network models are used for estimation of wind power generation and its variance. A novel PI-based training algorithm is also presented to enhance the performance of the MVE method and improve the quality of PIs. For an in-depth analysis, comprehensive experiments are conducted with seasonal datasets taken from three geographically dispersed wind farms in Australia. Five confidence levels of PIs are between 50% and 90%. Obtained results show while both traditional and optimized PIs are hypothetically valid, the optimized PIs are much more informative than the traditional MVE PIs. The informativeness of these PIs paves the way for their application in trouble-free operation and smooth integration of wind farms into energy systems. © 2014 Elsevier Ltd. All rights reserved.

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 Partial shading is one of the unavoidable complications in the field of solar power generation. Although the most common approach in increasing a photovoltaic (PV) array’s efficiency has always been to introduce a bypass diode to the said array, this poses another problem in the form of multi-peaks curves whenever the modules are partially shaded. To further complicate matters, most conventional Maximum Power Point Tracking methods develop errors under certain circumstances (for example, they detect the local Maximum Power Point (MPP) instead of the global MPP) and reduce the efficiency of PV systems even further. Presently, much research has been undertaken to improve upon them. This study aims to employ an evolutionary algorithm technique, also known as particle swarm optimization, in MPP detection. VC 2014 Author(s).

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The forecasting behavior of the high volatile and unpredictable wind power energy has always been a challenging issue in the power engineering area. In this regard, this paper proposes a new multi-objective framework based on fuzzy idea to construct optimal prediction intervals (Pis) to forecast wind power generation more sufficiently. The proposed method makes it possible to satisfy both the PI coverage probability (PICP) and PI normalized average width (PINAW), simultaneously. In order to model the stochastic and nonlinear behavior of the wind power samples, the idea of lower upper bound estimation (LUBE) method is used here. Regarding the optimization tool, an improved version of particle swam optimization (PSO) is proposed. In order to see the feasibility and satisfying performance of the proposed method, the practical data of a wind farm in Australia is used as the case study.