采用改进人工鱼群优化粗糙集算法的变压器故障诊断

传统的人工智能方法对变压器大量的不完备故障信息不能有效地分析,或在故障数据的离散化过程中由于区间分割不当而无法正确诊断故障甚至误诊。为此,提出了一种基于改进人工鱼群优化粗糙集的变压器故障诊断方法。该方法首先将变压器溶解气体分析(DGA)的值作为条件属性,将故障类型作为决策属性,建立故障决策表,利用鱼群的聚群寻优行为对决策表中的连续属性数据进行离散化;然后采用粗糙集理论对离散化后的决策表进行约简,建立故障诊断规则决策表,大大简化了决策表属性约简的难度,使诊断变得更加简便。最后通过实例验证表明:该方法能够有效地对样本进行离散和约简,与传统方法相比,提高了故障诊断的正确率。

Transformer Fault Diagnosis Using Improved Artificial Fish Swarm with Rough Set Algorithm

Facing a large number of incomplete fault data,the traditional artificial intelligence methods cannot effectively and timely analyze or accurately diagnosed because of the ill-conditioned problem caused by inefficient discretization approaches.We presented a method based on rough set theory integrated with improved artificial fish swarm algorithm(AFSA) for fault diagnosis of transformer.Firstly,the values of dissolved gas analysis(DGA) in oil were taken as conditional attributes and the type faults were taken as decision attributes.Various relations between fault and symptom were connected,and decision table was established.Then,the improved artificial fish swarm algorithm was used to discrete continuous attribute,and the rough set theory was used to reduce the decision table.Finally,the simplified decision rules were got,which greatly simplified the difficulty of diagnosis.The experimental results indicate that the method increases the diagnosis accuracy compared with the traditional algorithm.