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

Xing Xiong, Zhexi Xu, Rende Lu, Yisheng Li, Bingyan Li, Fengjiao Wu () and Bin Wang ()
Additional contact information
Xing Xiong: Department of Electrical Engineing, College of Werater Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China
Zhexi Xu: Dongfang Electric Machinery Company Limited, Deyang 618000, China
Rende Lu: Department of Electrical Engineing, College of Werater Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China
Yisheng Li: Department of Electrical Engineing, College of Werater Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China
Bingyan Li: Department of Electrical Engineing, College of Werater Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China
Fengjiao Wu: Department of Electrical Engineing, College of Werater Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China
Bin Wang: Department of Electrical Engineing, College of Werater Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China

Energies, 2025, vol. 18, issue 14, 1-14

Abstract: 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%.

Keywords: modified fractional hierarchical fluctuation dispersion entropy; stochastic configuration networks; AdaBoost; hydroelectric units; fault diagnosis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/14/3798/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/14/3798/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:14:p:3798-:d:1703870

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().