Degradation Trend Prediction of Hydropower Units Based on a Comprehensive Deterioration Index and LSTM

Yunhe Wang, Zhihuai Xiao (), Dong Liu, Jinbao Chen, Dong Liu and Xiao Hu
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Yunhe Wang: School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China
Zhihuai Xiao: School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China
Dong Liu: College of Energy and Power Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
Jinbao Chen: School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China
Dong Liu: China Yangtze Power Co., Ltd., Technical Center, Yichang 443000, China
Xiao Hu: Department of Power Electronics Engineering, Hubei Water Resources Technical College, Wuhan 430200, China

Energies, 2022, vol. 15, issue 17, 1-26

Abstract: Deterioration trend prediction of hydropower units helps to detect abnormal conditions of hydropower units and can prevent early failures. The reliability and accuracy of the prediction results are crucial to ensure the safe operation of the units and promote the stable operation of the power system. In this paper, the long short-term neural network (LSTM) is introduced, a comprehensive deterioration index (CDI) trend prediction model based on the time–frequency domain is proposed, and the prediction accuracy of the situation trend of hydropower units is improved. Firstly, the time–domain health model (THM) is constructed with back-propagation neural network (BPNN) and condition parameters of active power, guide vane opening and blade opening and the time–domain indicators. Subsequently, a frequency-domain health model (FHM) is established based on ensemble empirical mode decomposition (EEMD), approximate entropy (ApEn), and k-means clustering algorithm. Later, the time–domain degradation index (TDI) is developed according to THM, the frequency-domain degradation index (FDI) is constructed according to FHM, and the CDI is calculated as a weighted sum by TDI and FDI. Finally, the prediction model of LSTM is proposed based on the CDI to achieve degradation trend prediction. In order to validate the effectiveness of the CDI and the accuracy of the prediction model, the vibration waveform dataset of a hydropower plant in China is taken as a case study and compared with four different prediction models. The results demonstrate that the proposed model outperforms other comparison models in terms of predicting accuracy and stability.

Keywords: hydropower units; degradation trend prediction; comprehensive deterioration index; long and short-term neural network; ensemble empirical mode decomposition; approximate entropy (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: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (1)

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