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

机译：Cuckoo搜索量子机制的算法及其在水电发电机的故障诊断中的应用

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Background: The fault of a hydroelectric generating unit is mostly expressed in the formof vibration, and the reason is very complicated. Therefore, it is difficult to describe the mapping relationshipbetween the fault cause and fault symptom using the traditional approach.Methods: To improve the accuracy of fault diagnosis for a hydroelectric generating unit, we proposeda hybrid intelligent diagnosis technology in which the BP neural network is trained by cuckoosearch 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 thehighest diagnostic accuracy.Conclusion: The QCSBP model can effectively identify the fault state of a hydroelectric generatingunit, and is a fault diagnosis method with application prospect.
机译：背景：水力发电单元的故障主要以振动形式表示，原因非常复杂。 因此，难以使用传统方法描述故障原因和故障症状的映射关系。方法：提高水电发电机的故障诊断的准确性，我们培训了BP神经网络的Hybrist智能诊断技术 通过Cuckoosearch算法具有量子机制（QCSBP）。结果：通过实验研究，我们证明了使用量子机制（QCS）的Cuckoo搜索优于五种可比方法，所提出的QCSBP模型具有最高的诊断精度。结论： QCSBP模型可以有效地识别水力发电机的故障状态，是应用前景的故障诊断方法。

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