Degradation Tendency Prediction for Pumped Storage Unit Based on Integrated Degradation Index Construction and Hybrid CNN-LSTM Model

Zhou, Jianzhong; Shan, Yahui; Liu, Jie; Xu, Yanhe; Zheng, Yuan

doi:10.3390/s20154277

Cited by 16 publications

(12 citation statements)

References 43 publications

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“…Among the status data, swing and vibration reflect the operating status of the PDU significantly [9]. The swing of the upper guide bearing was chosen to reflect the status of the PSU in this paper.…”

Section: Working Parameter Selection Based On Micmentioning

confidence: 99%

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Degradation Trend Prediction of Pumped Storage Unit Based on MIC-LGBM and VMD-GRU Combined Model

et al. 2022

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The harsh operating environment aggravates the degradation of pumped storage units (PSUs). Degradation trend prediction (DTP) provides important support for the condition-based maintenance of PSUs. However, the complexity of the performance degradation index (PDI) sequence poses a severe challenge of the reliability of DTP. Additionally, the accuracy of healthy model is often ignored, resulting in an unconvincing PDI. To solve these problems, a combined DTP model that integrates the maximal information coefficient (MIC), light gradient boosting machine (LGBM), variational mode decomposition (VMD) and gated recurrent unit (GRU) is proposed. Firstly, MIC-LGBM is utilized to generate a high-precision healthy model. MIC is applied to select the working parameters with the most relevance, then the LGBM is utilized to construct the healthy model. Afterwards, a performance degradation index (PDI) is generated based on the LGBM healthy model and monitoring data. Finally, the VMD-GRU prediction model is designed to achieve precise DTP under the complex PDI sequence. The proposed model is verified by applying it to a PSU located in Zhejiang province, China. The results reveal that the proposed model achieves the highest precision healthy model and the best prediction performance compared with other comparative models. The absolute average (|AVG|) and standard deviation (STD) of fitting errors are reduced to 0.0275 and 0.9245, and the RMSE, MAE, and R2 are 0.00395, 0.0032, and 0.9226 respectively, on average for two operating conditions.

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Section: Working Parameter Selection Based On Micmentioning

confidence: 99%

“…Its accuracy directly affects the reliability of PDI. In relevant literature, the artificial neural network (ANN) [8], Gaussian process regression (GPR) [9], radial basis function interpolation surface [10,11], Shepard interpolation surface [12], etc. are frequently used.…”

Section: Introductionmentioning

confidence: 99%

Degradation Trend Prediction of Pumped Storage Unit Based on MIC-LGBM and VMD-GRU Combined Model

et al. 2022

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“…Therefore, the operating condition parameters of FTU change frequently [13]. Since the monitoring vibration signals are highly correlated to operation condition parameters, the traditional performance evaluation method with a fixed threshold is difficult to reflect the actual state of FTUs accurately [14]. Machine learning has been widely used in equipment fault diagnosis and performance evaluation due to its good pattern recognition capability [15].…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Prognostic Method of Francis Turbine Units Using Low-Quality Data under Variable Operating Conditions

Duan

Liu

Zhou

et al. 2022

Sensors

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The prognostic is the key to the state-based maintenance of Francis turbine units (FTUs), which consists of performance state evaluation and degradation trend prediction. In practical engineering environments, there are three significant difficulties: low data quality, complex variable operation conditions, and prediction model parameter optimization. In order to effectively solve the above three problems, an ensemble prognostic method of FTUs using low-quality data under variable operation conditions is proposed in this study. Firstly, to consider the operation condition parameters, the running data set of the FTU is constructed by the water head, active power, and vibration amplitude of the top cover. Then, to improve the robustness of the proposed model against anomaly data, the density-based spatial clustering of applications with noise (DBSCAN) is introduced to clean outliers and singularities in the raw running data set. Next, considering the randomness of the monitoring data, the healthy state model based on the Gaussian mixture model is constructed, and the negative log-likelihood probability is calculated as the performance degradation indicator (PDI). Furthermore, to predict the trend of PDIs with confidence interval and automatically optimize the prediction model on both accuracy and certainty, the multiobjective prediction model is proposed based on the non-dominated sorting genetic algorithm and Gaussian process regression. Finally, monitoring data from an actual large FTU was used for effectiveness verification. The stability and smoothness of the PDI curve are improved by 3.2 times and 1.9 times, respectively, by DBSCAN compared with 3-sigma. The root-mean-squared error, the prediction interval normalized average, the prediction interval coverage probability, the mean absolute percentage error, and the R2 score of the proposed method achieved 0.223, 0.289, 1.000, 0.641%, and 0.974, respectively. The comparison experiments demonstrate that the proposed method is more robust to low-quality data and has better accuracy, certainty, and reliability for the prognostic of the FTU under complex operating conditions.

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“…Some authors [11] applied this method to the radial bearing fault detection of Kaplan turbine, to make maintenance plan before the failure of radial bearing, improve the availability of bearing and reduce the maintenance cost. However, the existing diagnosis technology has difficulty meeting the requirements of engineering applications due to the fewer faults with the label of the unit and the abnormal information contained in a signal that is not enough to fully evaluate the unit status [13,14]. The hydropower unit system has the characteristics of large-scale uncertainty, high nonlinearity, and strong correlation, so it is difficult to establish an accurate mechanism model.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the swing data, the authors [19] integrated the vibration signal preprocessing, feature selection and prediction methods into a multi-objective optimization framework, and proposed an aggregated empirical wavelet transform-Gram-Schmidt orthogonal-multi-salp-objectiveswarm-algorithm-kernel extreme learning machine (AEWT-GSO-MOSSA-KELM) model for vibration tension prediction of hydropower, and the predicted MAE of this model is 0.65 and the calculation time is 95.716 s, which are better than EEMD based, PCA based, SVR based, SSA based and MOPSO based models in the same operating environment. Zhou et al [14] used vibration information and swing information to build a health state model of hydropower units. Based on the comprehensive consideration of the bearing vibration, active power, and working head information of the units, a condition parameter degradation assessment, and prediction model was proposed to evaluate and forecast hydropower units [13,20,21].…”

Section: Introductionmentioning

confidence: 99%

The Prediction Method on the Early Failure of Hydropower Units Based on Gaussian Process Regression Driven by Monitoring Data

et al. 2020

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The hydropower units have a complex structure, complicated and changing working conditions, complexity and a diversity of faults. Effectively evaluating the healthy operation status and accurately predicting the failure for the hydropower units using the real-time monitoring data is still a difficult problem. To this end, this paper proposes a prediction method for the early failure of hydropower units based on Gaussian process regression (GPR). Firstly, by studying the correlation between different monitoring data, nine state parameters closely related to the operation of hydropower units are mined from the massive data. Secondly, a health evaluation model is established based on GPR using the historical multi-dimensional monitoring information and fault-free monitoring data at the initial stage of unit operation. Finally, a condition monitoring directive based on the Mahalanobis distance (MD) is designed. The effectiveness of the proposed method is verified by several typical examples of monitoring data of a hydropower station in Guangxi, China. The results show that, in three cases, the abnormal conditions of the unit are found 2 days, 4 days and 43 days earlier than those of regular maintenances respectively. Therefore, the method can effectively track the change process of the operation state of hydropower units, and detect the abnormal operation state of hydropower units in advance.

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Degradation Tendency Prediction for Pumped Storage Unit Based on Integrated Degradation Index Construction and Hybrid CNN-LSTM Model

Cited by 16 publications

References 43 publications

Degradation Trend Prediction of Pumped Storage Unit Based on MIC-LGBM and VMD-GRU Combined Model

Degradation Trend Prediction of Pumped Storage Unit Based on MIC-LGBM and VMD-GRU Combined Model

An Ensemble Prognostic Method of Francis Turbine Units Using Low-Quality Data under Variable Operating Conditions

The Prediction Method on the Early Failure of Hydropower Units Based on Gaussian Process Regression Driven by Monitoring Data

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