Application of dynamic tunneling fuzzy C-means algorithm in dissolved gas of transformer oil

Dissolved gas analysis in transformer oil (DGA) is an important method for power transformer insulating diagnosis. Aiming at the problem that fuzzy C-means (FCM) clustering algorithm is likely to fall into local minimum point when being used for dissolved gas analysis, dynamic tunneling algorithm was introduced for its high global optimization performance. Then a FCM clustering algorithm was presented based on these two algorithms. On the basis of local minimum obtained by optimization searching of FCM algorithm, using dynamic tunneling process to search a lower energy valley, then the value was submitted to FCM algorithm for iterative optimization until global minimum point was found by repeating the process. Through the application of this algorithm for DGA in transformer oil, transformer fault diagnosis can be achieved. These tests for fault diagnosis of chromatography transformer oil and noise samples show that, the algorithm can cluster samples quickly and effectively and with high accuracy for diagnosis.