摘要
油中溶解气体分析(DGA)是目前最为常用的变压器内部故障诊断依据,但传统的各种DGA方法适用情况不同,其诊断准确性非常依赖于使用者的经验,而基于人工智能的DGA方法研究尚不成熟,自适应性较差。因此,本文提出了一种基于基因表达编程的DGA诊断模型,该模型综合多种常用的DGA方法得到最终诊断结果,优化了变压器油色谱分析故障诊断的流程。实验结果表明,文中所提出的模型具有较高的准确性,能为变压器的故障诊断和检修工作提供依据。
Dissolved gas analysis in oil (Dissolved Gases Analysis, the DGA) is one of the most commonly used transformer internal fault diagnoses. However, all of these techniques rely on personnel experience more than standard mathematical formulation. And the DGA method based on artificial intelligence research is not enough mature. This paper introduces an improved model which is built based on incorporating all conventional DGA interpretation techniques in one expert system to identify the fault type in a more consistent and reliable way. Gene Expression Programming is employed to establish this expert system. The experimental results show that the proposed model has high accuracy and provides a basis for transformer fault diagnosis and maintenance.
出处
《计算机科学与应用》
2019年第10期1815-1822,共8页
Computer Science and Application
