Fault diagnosis of hydroelectric unit based on AFSA-BP hybrid algorithm

The fault of hydropower unit is affected by many factors, it is difficult to find the correspondent fault symptoms and causes through the theoretical analysis. Considering the disadvantages of BP neural network, such as slow convergence rate and getting into local extremum, the initial parameters are optimized by the improved artificial fish swarm algorithm and the model for fault diagnosis is established. The vibration symptom and fault sets of hydropower unit are formed through the extraction of frequency spectrum feature. By the method of improved artificial fish swarm algorithm and BP neural network, the fault of hydropower unit is diagnosed. The results show that this method has high diagnostic accuracy.