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Search of optimum gas mixture ratio as gas insulating medium by genetic algorithm

机译:用遗传算法搜索最优混合气混合气体比例。

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摘要

Genetic algorithm is applied to find the optimum gas mixture ratio as gas insulating medium substituting pure SF/sub 6/. Genetic algorithm is very useful to find the optimum solution from vast searching possibilities. Testing each gas mixture by experiment requires a long time. The present method is very efficient to preselect the candidates of gas mixtures before more thorough but time-consuming investigation via experiment is carried out. The gas mixture ratio is coded as a series of bits simulating a genetic sequence of a life form. Two-term Boltzmann equation is used to calculate the effective ionization coefficient of each gas mixture that is used to evaluate the degree of adaptation of each individual representing one set of mixture ratios. Two types of degree of adaptation are used to evaluate each individual, the effective ionization coefficient at the critical ratio of the electric field to the gas density of SF/sub 6/ of 359.3/spl times/10/sup 21/Vm/sup 2/, and the global warming potential. Based on the degree of adaptation, better individuals can be selected as parents of the next generation, leaving their genes to future generations. After some generations, the group of individuals converges into the optimum with the best degree of adaptation.
机译:应用遗传算法寻找最佳气体混合比作为气体绝缘介质代替纯SF / sub 6 /。遗传算法对于从大量搜索可能性中找到最佳解决方案非常有用。通过实验测试每种气体混合物需要很长时间。在通过实验进行更彻底但耗时的研究之前,本方法非常有效地预选了气体混合物的候选者。气体混合比被编码为模拟生命形式遗传序列的一系列比特。两项Boltzmann方程用于计算每种气体混合物的有效电离系数,该电离系数用于评估代表一组混合比的每个个体的适应程度。两种适应程度用于评估每个人,电场强度与SF / sub 6 /的气体密度的临界比为359.3 / spl乘以/ 10 / sup 21 / Vm / sup 2时的有效电离系数。 /,以及全球变暖的潜力。根据适应程度,可以选择更好的个体作为下一代的父母,而将他们的基因留给后代。经过几代人之后,个体群体以最佳适应程度收敛到最优状态。

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