Intelligent Diagnosis Method for Hydroelectric Unit State Based on Improved Adaboost Classifier

In response to the shortcomings of existing methods for identifying the status of hydroelectric units, this article aims to innovatively propose an efficient intelligent diagnosis method for hydroelectric unit status, which can accurately identify different fault types of hydroelectric unit rotors. To achieve this goal, this article integrates multiple advanced signal processing technologies and machine learning algorithms. Firstly, preprocessing techniques such as wavelet threshold, CEEMDAN, and maximum information coefficient were used to denoise and decompose the vibration signal of hydropower units. Through these preprocessing steps, key feature information in the signal was effectively extracted; Calculating the approximate entropy value of each decomposed signal IMF helps to quantify the complexity of the signal and provide features for subsequent fault diagnosis and classification; Secondly, a diagnostic dataset containing approximate entropy sequences of signals and the operating status of hydroelectric units is constructed. In order to solve the problem of sample imbalance, ADASYN oversampling technology is used to expand minority class samples, making the number of various types of samples equal and effectively balancing the number of fault samples; Then, the Golden Sine strategy and dual population initialization mechanism were introduced to improve the Honey Badger algorithm, and a weak classifier model was constructed using the HKELM algorithm; Finally, based on the aforementioned weak classifier as the base model, an intelligent diagnosis model for the state of hydroelectric units was constructed using the Adaboost ensemble learning method. This model can comprehensively consider the discriminative ability of multiple weak classifiers, and through this model, inspectors can obtain real-time technical references about the status of hydropower units, helping inspectors take timely measures to ensure the normal operation of the units.