Health State Identification Method of Nuclear Power Main Circulating Pump Based on EEMD and OQGA-SVM

Liu, Zhilong; Li, Minggang; Zhu, Zhiwei; Xiao, Lifu; Nie, Chaoqun; Tang, Zhangchun

doi:10.3390/electronics12020410

Cited by 13 publications

(4 citation statements)

References 36 publications

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“…In addition, although SVM has good processing ability for small samples and nonlinear data, its parameters penalty coefficient and kernel function can affect the accuracy of fault diagnosis [23]. They cannot be selected adaptively according to actual samples, leading to low accuracy of classification and slow convergence [24]. Grid search and crossvalidation (CV) were used to determine the optimal values of penalty coefficient and kernel function [20].…”

Section: A Survey Of Previous Related Workmentioning

confidence: 99%

A New Hybrid Fault Diagnosis Method for Wind Energy Converters

Liang

Zhang

2023

Electronics

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Fault diagnostic techniques can reduce the requirements for the experience of maintenance crews, accelerate maintenance speed, reduce maintenance cost, and increase electric energy production profitability. In this paper, a new hybrid fault diagnosis method based on multivariate empirical mode decomposition (MEMD), fuzzy entropy (FE), and an artificial fish swarm algorithm (AFSA)-support vector machine (SVM) is proposed to identify the faults of a wind energy converter. Firstly, the measured three-phase output voltage signals are processed by MEMD to obtain three sets of intrinsic mode functions (IMFs). The multi-scale analysis tool MEMD is used to extract the common modes matching the timescale. It studies the multi-scale relationship between three-phase voltages, realizes their synchronous analysis, and ensures that the number and frequency of the modes match and align. Then, FE is calculated to describe the IMFs’ complexity, and the IMFs-FE information is taken as fault feature to increase the robustness to working conditions and noise. Finally, the AFSA algorithm is used to optimize SVM parameters, solving the difficulty in selecting the penalty factor and radial basis function kernel. The effectiveness of the proposed method is verified in a simulated wind energy system, and the results show that the diagnostic accuracy for 22 fault modes is 98.7% under different wind speeds, and the average accuracy of 30 running can be maintained above 84% for different noise levels. The maximum, minimum, average, and standard deviation are provided to prove the robust and stable performance. Compared with the other methods, the proposed hybrid method shows excellent performance in terms of high accuracy, strong robustness, and computational efficiency.

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Section: A Survey Of Previous Related Workmentioning

confidence: 99%

A New Hybrid Fault Diagnosis Method for Wind Energy Converters

Liang

Zhang

2023

Electronics

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“…The EEMD method introduces Gaussian white noise into the original signal, and the automatic distribution of the signal in the appropriate time scale is achieved after multiple average calculations. This is effective in solving modal aliasing (Liu et al, 2023b). Wind power is susceptible to significant random fluctuations as well as the impact caused by wind direction, wind speed, and other factors.…”

Section: Ensemble Empirical Mode Decomposition (Eemd)mentioning

confidence: 99%

A novel few-sample wind power prediction model based on generative adversarial network and quadratic mode decomposition

Yuan

2023

Front. Energy Res.

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With the emergence of various new power systems, accurate wind power prediction plays a critical role in their safety and stability. However, due to the historical wind power data with few samples, it is difficult to ensure the accuracy of power system prediction for new wind farms. At the same time, wind power data show significant uncertainty and fluctuation. To address this issue, it is proposed in this research to build a novel few-sample wind power prediction model based on the least-square generative adversarial network (LSGAN) and quadratic mode decomposition (QMD). Firstly, a small amount of original wind power data are generated to improve the data by least-square generative adversarial network, which solves the error in prediction with limited sample data. Secondly, a quadratic mode decomposition method based on ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD) is developed to address the instability of wind power data and extract hidden temporal characteristics. Specifically, ensemble empirical mode decomposition decomposes the data once to obtain a set of intrinsic mode functions (IMFs), and then variational mode decomposition is used to decompose the fuzzy irregular IMF1 function twice. Finally, a bidirectional long short-term memory network (BiLSTM) based on particle swarm optimization (PSO) is applied to predict wind power data. The LSGAN-QMD-PSO-BiLSTM model proposed in this research is verified on a wind farm located in Spain, which indicates that the proposed model achieves the lowest root mean square error (RMSE) and mean absolute error (MAE) errors of 100.6577 and 66.5175 kW, along with the highest R2 of 0.9639.

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Section: Introductionmentioning

confidence: 99%

“…Currently, there are a few mature technical solutions to address the challenges posed by the unmanned operation and maintenance of the main circulation pump in the cooling system of the VSG-HVDC system [5][6][7][8][9][10][11][12][13]. Specifically, a relevant study focused on estimating the health state of the pump [5]. Another research effort involved the development of mathematical models to analyze the electromagnetic and electromechanical processes of the pump during startup and operation modes [7].…”

Section: Introductionmentioning

confidence: 99%

A Novel Ensemble Fault Diagnosis Model for Main Circulation Pumps of Converter Valves in VSC-HVDC Transmission Systems

Zhou

Qin

Yang³

et al. 2023

Sensors

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The intelligent fault diagnosis of main circulation pumps is crucial for ensuring their safe and stable operation. However, limited research has been conducted on this topic, and applying existing fault diagnosis methods designed for other equipment may not yield optimal results when directly used for main circulation pump fault diagnosis. To address this issue, we propose a novel ensemble fault diagnosis model for the main circulation pumps of converter valves in voltage source converter-based high voltage direct current transmission (VSG-HVDC) systems. The proposed model employs a set of base learners already able to achieve satisfying fault diagnosis performance and a weighting model based on deep reinforcement learning that synthesizes the outputs of these base learners and assigns different weights to obtain the final fault diagnosis results. The experimental results demonstrate that the proposed model outperforms alternative approaches, achieving an accuracy of 95.00% and an F1 score of 90.48%. Compared to the widely used long and short-term memory artificial neural network (LSTM), the proposed model exhibits improvements of 4.06% in accuracy and 7.85% in F1 score. Furthermore, it surpasses the latest existing ensemble model based on the improved sparrow algorithm, with enhancements of 1.56% in accuracy and 2.91% in F1 score. This work presents a data-driven tool with high accuracy for the fault diagnosis of main circulation pumps, which plays a critical role in maintaining the operational stability of VSG-HVDC systems and satisfying the unmanned requirements of offshore flexible platform cooling systems.

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Health State Identification Method of Nuclear Power Main Circulating Pump Based on EEMD and OQGA-SVM

Cited by 13 publications

References 36 publications

A New Hybrid Fault Diagnosis Method for Wind Energy Converters

A New Hybrid Fault Diagnosis Method for Wind Energy Converters

A novel few-sample wind power prediction model based on generative adversarial network and quadratic mode decomposition

A Novel Ensemble Fault Diagnosis Model for Main Circulation Pumps of Converter Valves in VSC-HVDC Transmission Systems

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