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基于粗糙集和遗传算法的变压器故障诊断方法

期刊名称：仪器仪表学报,Vol. 29, No.10, 2034-2040,2008,EI：20084611
时间：0
分类：TM711[电气工程—电力系统及自动化] TP182[自动化与计算机技术—控制科学与工程;自动化与计算机技术—控制理论与控制工程]
作者机构：[1]东北大学信息科学与工程学院,沈阳110004
相关基金：国家自然科学基金（60325311）、863重点课题（2006AA04Z183）资助项目
相关项目：智能控制理论

关键词：油中溶解气体分析, 粗糙集, 遗传算法, 变朦器故障诊断, 可辨识矩阵, dissolved gas analysis, rough set, genetic algorithm, transformer fault diagnosis, discernibility matrix

中文摘要：

本文针对基于变压器油中溶解气体的故障诊断方法不能有效地把溶解气体的绝对值和比值进行有机结合、断点划分过于绝对且缺少针对性、应用粗糙集进行约简时采用的方法不适合进行大规模数据的全局最优解求取等问题，提出了一种粗糙集和遗传算法相结合的约简算法，将溶解气体的绝对值和比值作为原始规则进行有针对性的处理，从而获得最终的结果。算法首先将连续属性的离散化、属性约简和值约简都统一转化为可辨识矩阵的化简问题，利用遗传算法的全局并行寻优能力对这一类问题进行统一处理，有效地提高了算法的执行效率和连贯性，从而保证得到全局最优结果。将建立的专家系统应用于某特定型号220kV主变压器（隔膜式），获得了较为满意的诊断结果，与当前应用较多的另外7类方法进行比较，验证了本文提出方法的有效性和实用性。

英文摘要：

Focusing on the combination problem of value and ratio research in the dissolved gas-in-oil, a novel reduction method is put forward based on the combination of genetic algorithm and rough set. The reduction method utilizes the capability of searching for global optimum of genetic algorithm and achieves better reduction result com- pared with classical rough set reduction algorithm. In addition, the break point is divided considering different factors of transformer fault. In transformer fault diagnosis system, it is very important to make unified reduction for value and value signals using rough sets. In this way, the precision of fault identification can be improved. The problems of continuous attribute discretization, attribute reduction and attribute value reduction are translated into the simplification problem of discernibility matrix and genetic algorithm can be employed. Experiment on a certain 220 kV main transformer （diaphragm type） shows that the reduction method using genetic algorithm accelerates the evolutionary process and avoids premature convergence effectively for the system with 16 attributes and 200 records. Compared with other 7 kinds of methods, this method possesses feasibility and effectiveness for the fault diagnosis of complex dissolved gas analysis system with thousands of data.

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