Hydroelectric Unit Fault Diagnosis Based on Modified Fractional Hierarchical Fluctuation Dispersion Entropy and AdaBoost-SCN

The hydropower unit is the core of the hydropower station, and maintaining the safety and stability of the hydropower unit is the first essential priority of the operation of the hydropower station. However, the complex environment increases the probability of the failure of hydropower units. Therefore, aiming at the complex diversity of hydropower unit faults and the imbalance of fault data, this paper proposes a fault identification method based on modified fractional-order hierarchical fluctuation dispersion entropy (MFHFDE) and AdaBoost-stochastic configuration networks (AdaBoost-SCN). First, the modified hierarchical entropy and fractional-order theory are incorporated into the multiscale fluctuation dispersion entropy (MFDE) to enhance the responsiveness of MFDE to various fault signals and address its limitation of overlooking the high-frequency components of signals. Subsequently, the Euclidean distance is used to select the fractional order. Then, a novel method for evaluating the complexity of time-series signals, called MFHFDE, is presented. In addition, the AdaBoost algorithm is used to integrate stochastic configuration networks (SCN) to establish the AdaBoost-SCN strong classifier, which overcomes the problem of the weak generalization ability of SCN under the condition of an unbalanced number of signal samples. Finally, the features extracted via MFHFDE are fed into the classifier to accomplish pattern recognition. The results show that this method is more robust and effective compared with other methods in the anti-noise experiment and the feature extraction experiment. In the six kinds of imbalanced experimental data, the recognition rate reaches more than 98%.

Xing Xiong & Zhexi Xu & Rende Lu & Yisheng Li & Bingyan Li & Fengjiao Wu & Bin Wang, 2025. "Hydroelectric Unit Fault Diagnosis Based on Modified Fractional Hierarchical Fluctuation Dispersion Entropy and AdaBoost-SCN," Energies, MDPI, vol. 18(14), pages 1-14, July.

Handle: RePEc:gam:jeners:v:18:y:2025:i:14:p:3798-:d:1703870

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