A cognitive genetic algorithm for power distribution system planning

Power systems are large scale nonlinear systems with high complexity. Various optimization techniques and expert systems have been used in power system planning. However, there are always some factors that cannot be quantified, modeled, or even expressed by expert systems. Moreover, such planning problems are often large scale optimization problems. Although computational algorithms that are capable of handling large dimensional problems can be used, the computational costs are still very high. To solve these problems, in this paper, investigation is made to explore the efficiency and effectiveness of combining mathematic algorithms with human intelligence. It had been discovered that humans can join the decision making progresses by cognitive feedback. Based on cognitive feedback and genetic algorithm, a new algorithm called cognitive genetic algorithm is presented. This algorithm can clarify and extract human's cognition. As an important application of this cognitive genetic algorithm, a practical decision method for power distribution system planning is proposed. By using this decision method, the optimal results that satisfy human expertise can be obtained and the limitations of human experts can be minimized in the mean time.

Optimal dispatch of spinning reserve in a competitive electricity market using genetic algorithms

Ancillary service plays a key role in maintaining operation security of the power system in a competitive electricity market. The spinning reserve is one of the most important ancillary services that should be provided effectively. This paper presents the design of an integrated market for energy and spinning reserve service with particular emphasis on coordinated dispatch of bulk power and spinning reserve services. A new market dispatching mechanism has been developed to minimize the cost of service while maintaining system security. Genetic algorithms (GA) are used for finding the global optimal solutions for this dispatch problem. Case studies and corresponding analyses have been carried out to demonstrate and discuss the efficiency and usefulness of the proposed method.

The role of carbon in fuel cells

Carbon possesses unique electrical and structural properties that make it an ideal material for use in fuel cell construction. In alkaline, phosphoric acid and proton-exchange membrane fuel cells (PEMFCs), carbon is used in fabricating the bipolar plate and the gas-diffusion layer. It can also act as a support for the active metal in the catalyst layer. Various forms of carbon - from graphite and carbon blacks to composite materials - have been chosen for fuel-cell components. The development of carbon nanotubes and the emergence of nanotechnology in recent years has therefore opened up new avenues of matenials development for the low-temperature fuel cells, particularly the hydrogen PEMFC and the direct methanol PEMFC. Carbon nanotubes and aerogels are also being investigated for use as catalyst support, and this could lead to the production of more stable, high activity catalysts, with low platinum loadings (< 0.1 Mg cm(-2)) and therefore low cost. Carbon can also be used as a fuel in high-temperature fuel cells based on solid oxide, alkaline or molten carbonate technology. In the direct carbon fuel cell (DCFC), the energy of combustion of carbon is converted to electrical power with a thermodynamic efficiency close to 100%. The DCFC could therefore help to extend the use of fossil fuels for power generation as society moves towards a more sustainable energy future. (c) 2006 Elsevier B.V. All rights reserved.

Urinary arsenic, porphyrins and malondialdehyde in a population 16 years after arsenic mitigation program in Xinjian, China

Arsenic contamination of groundwater (0.05 to 0.84 mg/L) in Kuitun, Xinjiang was first found in 1970’s. Alternative clean surface water was introduced in 1985. We aimed to assess the exposure and heath outcome since the mitigation. In 2000, we collected a total of 360 urine samples from villagers from the endemic area and a nearby control area for arsenic (As), porphyrins and malondialdehyde (MDA) measurements. The averaged urinary As level of villagers from the endemic site (117±8.3 μg/g creatinine; 4.2 to 943.8 μg/g creat) was higher than that of the control site (73.6±3.2 μg/g creat). No significant differences were found in urinary porphyrins or MDA between the endemic and control sites. However, when the urinary arsenic was higher than 150 μg/g creat, these two biomarkers were higher in the exposed group than the control. Within the exposed group, villagers with arsenic-related skin symptoms had higher arsenic, uroporphyrin and MDA compared to those who had not shown symptoms. Sine the water mitigation, villagers whose urinary arsenic levels were 270 μg/g creat dropped from 20% to 10% of the population. Population with arsenic-related skin symptoms remained unchanged at 31%. We noted that 7.8% of those who had skin lesions were born after the implementation of intervention and that some villagers still prefer to drink the groundwater. Further, in the dry season, lack of surface water and electrical power breakdowns are to blame for failure to ensure continuous supply of clean water. It is concluded that despite the prompt action and successful water mitigation program to curb arsenic poisonings, it is essential to continue to monitor the health outcome of this population.

Influence of charge size distribution on net-power draw of tumbling mill based on DEM modelling

Modelling and optimization of the power draw of large SAG/AG mills is important due to the large power draw which modern mills require (5-10 MW). The cost of grinding is the single biggest cost within the entire process of mineral extraction. Traditionally, modelling of the mill power draw has been done using empirical models. Although these models are reliable, they cannot model mills and operating conditions which are not within the model database boundaries. Also, due to its static nature, the impact of the changing conditions within the mill on the power draw cannot be determined using such models. Despite advances in computing power, discrete element method (DEM) modelling of large mills with many thousands of particles could be a time consuming task. The speed of computation is determined principally by two parameters: number of particles involved and material properties. The computational time step is determined by the size of the smallest particle present in the model and material properties (stiffness). In the case of small particles, the computational time step will be short, whilst in the case of large particles; the computation time step will be larger. Hence, from the point of view of time required for modelling (which usually corresponds to time required for 3-4 mill revolutions), it will be advantageous that the smallest particles in the model are not unnecessarily too small. The objective of this work is to compare the net power draw of the mill whose charge is characterised by different size distributions, while preserving the constant mass of the charge and mill speed. (C) 2004 Elsevier Ltd. All rights reserved.