油浸倒立式电流互感器主绝缘电场分析与优化设计

发布时间：2018-03-14 13:04

本文选题：油浸倒立式电流互感器　切入点：主绝缘　出处：《沈阳工业大学》2012年硕士论文　论文类型：学位论文

【摘要】：油浸倒立式电流互感器区别于传统的正立式结构,将二次绕组与一次绕组集中置于整个产品的上部,避免了正立式电流互感器主绝缘位于产品底部易受潮的问题,减少了主绝缘因受潮而被击穿的可能性,但同时也给产品的绝缘设计和工艺制造增添了一定的难度。油浸式电流互感器在110kV电压等级以上通常采用电容型油纸绝缘结构,即在主绝缘中嵌入电容屏来改善电场分布。针对于倒立式的特殊结构,增加电容屏的方式一般分为两种:一种为端屏式结构,另一种则为主屏式结构。本文采用主屏式结构,以220kV油浸倒立式电流互感器为研究对象,分析了主绝缘的物理结构特点,采用数值和解析两种方法求解屏间电容,并验证了两种方法的准确性。根据倒立式电流互感器主屏式结构的特点,采用分层分段的方式建立电场分析的有限元模型,将二次侧绕组与下引线部位分开,对于二次侧绕组部位进行三维有限元分析,对于下引线部位进行二维有限元分析,并采用解析法验证了有限元计算电场的准确性。根据电场分布以及最大场强位置,分析影响绝缘性能的主要因素,提出了优化方案。本文采用RBF神经网络动态响应模型与遗传算法相结合的方法对主屏结构的油浸倒立式电流互感器的主绝缘结构进行优化。根据动态神经网络的思想,建立动态响应模型,使响应模型跟随最优点的位置不断更新、细化,避免了传统响应模型采样点过多的缺陷;采用幂律标定方法增强了遗传算法的选择功能。通过优化降低了最大场强,同时也使主绝缘整体的电场分布得到了改善。
[Abstract]:The oil-immersed inverted current transformer is different from the traditional vertical structure by concentrating the secondary winding and the primary winding on the upper part of the whole product, thus avoiding the problem that the main insulation of the vertical current transformer is located at the bottom of the product. It reduces the possibility of the main insulation being broken down by moisture, but it also adds some difficulties to the insulation design and process manufacture of the product. The oil-immersed current transformer usually adopts capacitive oil-paper insulation structure above 110 kV voltage level, that is, to improve electric field distribution by embedding capacitance screen in the main insulation. There are two ways to increase the capacitance screen: one is the end screen structure, the other is the main screen structure. In this paper, the main screen structure is adopted and the 220 kV oil-immersed inverted current transformer is taken as the research object, and the physical structure characteristics of the main insulation are analyzed. Numerical and analytical methods are used to solve the capacitance between screens, and the veracity of the two methods is verified. According to the characteristics of the main screen structure of the inverted current transformer, the finite element model of electric field analysis is established in a layered and piecewise manner. The secondary side winding is separated from the lower lead part, the secondary side winding part is analyzed by three-dimensional finite element method, and the lower lead part is analyzed by two-dimensional finite element method. The accuracy of finite element calculation of electric field is verified by analytical method. According to the distribution of electric field and the position of maximum field strength, the main factors affecting insulation performance are analyzed, and the optimization scheme is put forward. In this paper, RBF neural network dynamic response model combined with genetic algorithm is used to optimize the main insulation structure of oil-immersed inverted current transformer with main screen structure. According to the idea of dynamic neural network, the dynamic response model is established. The response model is updated and refined according to the optimal position, which avoids the defect that the traditional response model has too many sampling points. The power law calibration method is used to enhance the selection function of genetic algorithm, and the maximum field strength is reduced by optimization. At the same time, the electric field distribution of the main insulation is improved.
【学位授予单位】：沈阳工业大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：TM452.3

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