Cuckoo Search Algorithm with Quantum Mechanism and its Application in the Fault Diagnosis of a Hydroelectric Generating Unit

Background: The fault of a hydroelectric generating unit is mostly expressed in the form of vibration, and the reason is very complicated. Therefore, it is difficult to describe the mapping relationship between the fault cause and fault symptom using the traditional approach. Methods: To improve the accuracy of fault diagnosis for a hydroelectric generating unit, we proposed a hybrid intelligent diagnosis technology in which the BP neural network is trained by cuckoo search algorithm with a quantum mechanism (QCSBP). Results: Through the experimental study, we demonstrate that cuckoo search with a quantum mechanism (QCS) is superior to the five comparable approaches, and the proposed QCSBP model has the highest diagnostic accuracy. Conclusion: The QCSBP model can effectively identify the fault state of a hydroelectric generating unit, and is a fault diagnosis method with application prospect.