Metodología para la determinación de la evapotranspiración integrada y la capacidad de canales en una zona de riego

La capacidad de la red de canales en un sistema de riego depende de satisfacer la demanda hídrica máxima de los cultivos. Los métodos para determinar la capacidad del canal requieren de la estimación de la variable agronómica: evapotranspiración de los cultivos. En grandes áreas de riego, con un padrón diversificado de cultivos, diferentes fechas de siembra y varios ciclos agrícolas no existe un procedimiento integrado para estimar esta variable agronómica, lo cual genera incertidumbre al ser requerida en los métodos. En este trabajo se desarrolla una propuesta para estimar dicha variable para grandes zonas de riego. La propuesta inicia con el cálculo de la evapotranspiración de los cultivos por fecha de siembra, y termina con la obtención de una curva general integral para un año agrícola, encontrándose la variable evapotranspiración de una zona de riego (ETzr). Esta metodología se aplicó para el canal principal del módulo de riego Santa Rosa, Distrito de Riego 075, Sinaloa, México en que la ETzr resultó de 4,1 mm d-1. Por los resultados se concluye la veracidad de la propuesta en determinar la evapotranspiración para el cálculo en la capacidad del canal.

Optimal allocation and sizing of capacitors to minimize the transmission line loss and to improve the voltage profile

To allocate and size capacitors in a distribution system, an optimization algorithm, called Discrete Particle Swarm Optimization (DPSO), is employed in this paper. The objective is to minimize the transmission line loss cost plus capacitors cost. During the optimization procedure, the bus voltage, the feeder current and the reactive power flowing back to the source side should be maintained within standard levels. To validate the proposed method, the semi-urban distribution system that is connected to bus 2 of the Roy Billinton Test System (RBTS) is used. This 37-bus distribution system has 22 loads being located in the secondary side of a distribution substation (33/11 kV). Reducing the transmission line loss in a standard system, in which the transmission line loss consists of only about 6.6 percent of total power, the capabilities of the proposed technique are seen to be validated.

Optimal allocation and sizing of DGs in distribution networks

In this paper, the placement and sizing of Distributed Generators (DG) in distribution networks are determined optimally. The objective is to minimize the loss and to improve the reliability. The constraints are the bus voltage, feeder current and the reactive power flowing back to the source side. The placement and size of DGs are optimized using a combination of Discrete Particle Swarm Optimization (DPSO) and Genetic Algorithm (GA). This increases the diversity of the optimizing variables in DPSO not to be stuck in the local minima. To evaluate the proposed algorithm, the semi-urban 37-bus distribution system connected at bus 2 of the Roy Billinton Test System (RBTS), which is located at the secondary side of a 33/11 kV distribution substation, is used. The results finally illustrate the efficiency of the proposed method.

Optimal allocation and sizing of DGs in a distribution system using PSO

In this paper, the optimal allocation and sizing of distributed generators (DGs) in a distribution system is studied. To achieve this goal, an optimization problem should be solved in which the main objective is to minimize the DGs cost and to maximise the reliability simultaneously. The active power balance between loads and DGs during the isolation time is used as a constraint. Another point considered in this process is the load shedding. It means that if the summation of DGs active power in a zone, isolated by the sectionalizers because of a fault, is less than the total active power of loads located in that zone, the program start shedding the loads in one-by-one using the priority rule still the active power balance is satisfied. This assumption decreases the reliability index, SAIDI, compared with the case loads in a zone are shed when total DGs power is less than the total load power. To validate the proposed method, a 17-bus distribution system is employed and the results are analysed.

Is there a difference between narrowing of the spinal canal and neurological deficits comparing Denis and Magerl classifications?

We have read with great interest the retrospective study by Caffaro and Avanzi1 evaluating the relation between narrowing of the spinal canal and neurological deficits in patients with burst-type fractures of the spine. The authors are to be commended for obtaining detailed neurological and radiological data in a large cohort of 227 patients. The authors conclude: “The percentage of narrowing of the spinal canal proved to be a pre-disposing factor for the severity of the neurological status in thoracolumbar and lumbar burst-type fractures according to the classifications of Denis and Magerl.” Although this conclusion is mainly in accordance with previous findings, we would like to comment on the methodological approach applied in the current study.

Optimal sitting and sizing of distributed generators considering plug-in electric vehicles

Some uncertainties such as the stochastic input/output power of a plug-in electric vehicle due to its stochastic charging and discharging schedule, that of a wind unit and that of a photovoltaic generation source, volatile fuel prices and future uncertain load growth, all together could lead to some risks in determining the optimal siting and sizing of distributed generators (DGs) in distributed systems. Given this background, under the chance constrained programming (CCP) framework, a new method is presented to handle these uncertainties in the optimal sitting and sizing problem of DGs. First, a mathematical model of CCP is developed with the minimization of DGs investment cost, operational cost and maintenance cost as well as the network loss cost as the objective, security limitations as constraints, the sitting and sizing of DGs as optimization variables. Then, a Monte Carolo simulation embedded genetic algorithm approach is developed to solve the developed CCP model. Finally, the IEEE 37-node test feeder is employed to verify the feasibility and effectiveness of the developed model and method. This work is supported by an Australian Commonwealth Scientific and Industrial Research Organisation (CSIRO) Project on Intelligent Grids Under the Energy Transformed Flagship, and Project from Jiangxi Power Company.

Porous Ca-Si-based nanospheres : a potential intra-canal disinfectant-carrier for infected canal treatment

The aim of this study is to develop a new intra-canal disinfectant-carrier for infected canal treatment. To achieve this purpose, a new porous Ca-Si (CS)-based nanosphere was synthesized and characterized. Results showed that the nanospheres can infiltrate into dentinal tubules and released the ampicillin over one week time in a sustained manner. The release of ampicillin from spheres has significantly antibacterial property. Extensive and well-organized in vitro mineralization and crystallization of apatite were induced on the surface of dentin slices covered by CS nanospheres. All these features indicate that the porous CS nanospheres may be developed into a new intra-canal disinfectant-carrier for infected canal treatment.

A new technique for optimal allocation and sizing of capacitors and setting of LTC

An iterative based strategy is proposed for finding the optimal rating and location of fixed and switched capacitors in distribution networks. The substation Load Tap Changer tap is also set during this procedure. A Modified Discrete Particle Swarm Optimization is employed in the proposed strategy. The objective function is composed of the distribution line loss cost and the capacitors investment cost. The line loss is calculated using estimation of the load duration curve to multiple levels. The constraints are the bus voltage and the feeder current which should be maintained within their standard range. For validation of the proposed method, two case studies are tested. The first case study is the semi-urban 37-bus distribution system which is connected at bus 2 of the Roy Billinton Test System which is located in the secondary side of a 33/11 kV distribution substation. The second case is a 33 kV distribution network based on the modification of the 18-bus IEEE distribution system. The results are compared with prior publications to illustrate the accuracy of the proposed strategy.

Adaptive sizing of populations and number of islands in distributed genetic algorithms

Deciding the appropriate population size and number of is- lands for distributed island-model genetic algorithms is often critical to the algorithm’s success. This paper outlines a method that automatically searches for good combinations of island population sizes and the number of islands. The method is based on a race between competing parameter sets, and collaborative seeding of new parameter sets. This method is applicable to any problem, and makes distributed genetic algorithms easier to use by reducing the number of user-set parameters. The experimental results show that the proposed method robustly and reliably finds population and islands settings that are comparable to those found with traditional trial-and-error approaches.

Maximum loadability achievement in SWER networks using optimal sizing and locating of batteries

This paper presents an optimisation algorithm to maximize the loadability of single wire earth return (SWER) by minimizing the cost of batteries and regulators considering the voltage constraints and thermal limits. This algorithm, that finds the optimum location of batteries and regulators, uses hybrid discrete particle swarm optimization and mutation (DPSO + Mutation). The simulation results on realistic highly loaded SWER network show the effectiveness of using battery to improve the loadability of SWER network in a cost-effective way. In this case, while only 61% of peak load can be supplied without violating the constraints by existing network, the loadability of the network is increased to peak load by utilizing two battery sites which are located optimally. That is, in a SWER system like the studied one, each installed kVA of batteries, optimally located, supports a loadability increase as 2 kVA.