摘要随着科学技术水平的不断突破，各种以前难以想象的技术都被应用到现实生 活中来并在生活中发挥着举足轻重的作用。而电作为每个人都需要使用的能源， 更加让人不得不注意。电力能否稳定且有保障的供应也越来越为人们所关注。而 电的不中断供应离不开设备的不中断运行。只有保障了源头的安全稳定才能为电 力供应的稳定打下良好的基础，让人们享受稳定的电能，为我国的经济和工业发 展保驾护航。而发电厂的电气设备不仅种类繁多，而且结构也十分的复杂，工作 的状态环境也比较特殊。所以发电厂设备的故障也比较难以提前检测。发电厂设 备一旦发生故障，能对国家造成难以想象的大量财富损失，同时也能打乱个人的 日常事物。因此提高发电厂故障的研究水平，在诊断技术上的不断突破也越来越 成为人们所关注的问题。69948

本文利用遗传算法 (Genetic Algorithm) 改变强化支持向量机 (Support Vector Machine SVM)搜查选取变量 c 和 g 的准确率，并将这个方法用来改进发 电厂设备运行错误的应对决策[1]。通过汽轮机的振动故障诊断系统设计表明，经 遗传算法优化后的支持向量机故障分类诊断有着较快的速度和准确度，给发电厂 电气设备的检测诊断研究注入了推动能，为我国电力产业走向现代化做出不可估 量的支持。69948

该论文有图 11 幅，表 3 个，参考文献 32 篇。

毕业论文关键词：支持向量机 遗传算法 发电厂设备 汽轮机 诊断

Using intelligent information processing method to solve the fault diagnosis of power plant equipment

Abstract As science and technology continues to break, all kinds of previously unimaginable technologies are being applied to real life and play a significant role in life. Electricity as a everyone needs energy makes great attention. Can stable and secure electricity supply is also growing concern for people. Let people enjoy stable power and for China's economic and industrial development escort. Power plant electrical equipment is not only a wide variety, and the structure is very complex, the work of the state environment is also more special. So it is difficult to detect the faults of power plant equipment. Once a fault occurs in a power plant, it can result in a huge loss of wealth to the country, and it can also disrupt the inpidual's daily life. Therefore, to improve the research level of the fault of power plant, the continuous breakthroughs in the diagnosis technology have become more and more concerned by people.

In this paper, genetic algorithm is used to improve the accuracy of C and G by using the enhanced support vector machine, and the method is used to improve the operational errors of power plant equipment. Through the turbine vibration fault diagnosis system design show that the genetic algorithm optimized support vector machine classification of fault diagnosis has a faster speed and accuracy, to the power plant electrical equipment detection and diagnosis of injected to promote, for our country electric power industry modernization make invaluable support.

Key Words: GA SVM Electric field equipment Steam turbine Diagnosis

目录

摘要 I

Abstract II

目录 III

图清单 V

表清单 V

1 基于智能信息处理的发电厂设备的故障诊断:http://www.751com.cn/zidonghua/lunwen_79041.html