Fault diagnosis of high power grid wind turbine based on particle swarm optimization BP neural network during COVID-19 epidemic period

Chen, Xi

doi:10.3233/jifs-189301

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…Where, a is the weight (0 < a < 1) and y t is the primary exponential smoothing of the t th period [14][15].…”

Section: Principles Of Time Series Modelsmentioning

confidence: 99%

Analysis and Prediction of Epidemic Prevention and Control by Police Stations Based on Time Series

Mingyue Qiu,

Xueying Zhang

2023

Journal of Computers

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<p>It has been over two years since the outburst of the COVID-19 pandemic. Currently, China has entered into a normalization stage and police stations are still in the endeavor of improving their epidemic prevention and control measures. However, grassroots police stations are still backward in epidemic prevention and control, and lack of response measures for each period of the epidemic. This paper uses time series models to predict the epidemic trend and analyze the measures undertaken by the police stations. In the process of data pretreatment, this paper focuses on the data processing of the epidemic control period. Then the epidemic trend is predicted based on five different time series models and two different time intervals. The results indicate that the tertiary exponential smoothing prediction model with day as the interval is the best and accurate prediction method. According to the prediction model, it can be determined the current stage of the epidemic development by time points, so as to give targeted reference for the police stations. The basic idea in using various time series models is to predict the accumulated number of confirmed cases based on the existing data not only to help, guide and refine the existing epidemic measures but also offer suggestions for epidemic prevention and control by police stations in response to each period of the epidemic. Based on the findings exhaustive recommendations are proposed for real-time and targeted epidemic prevention and control by police administration.</p> <p> </p>

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“…Where, a is the weight (0 < a < 1) and y t is the primary exponential smoothing of the t th period [14][15].…”

Section: Principles Of Time Series Modelsmentioning

confidence: 99%

Analysis and Prediction of Epidemic Prevention and Control by Police Stations Based on Time Series

Mingyue Qiu,

Xueying Zhang

2023

Journal of Computers

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“…In (Zhang et al, 2020), a particle swarm optimization algorithm (PSO) was used to optimize SVM for fault diagnosis of wind turbine gearbox bearings, and results have shown that the precision and accuracy of diagnosis were improved. In (Chen, 2020), backpropagation neural network (BPNN) and long shortterm memory network (LSTMN) were combined with PSO and great fault diagnosis results were obtained in wind turbine rolling bearing fault diagnosis. In (Odofin et al, 2018), a genetic algorithm (GA) was adopted to optimize the machine learning algorithm to improve the reliability of the wind turbine energy system.…”

Section: Introductionmentioning

confidence: 99%

Wind Turbine Pitch System Fault Detection Using ssODM-DSTA

Tang¹,

Hu²,

Wu³

et al. 2021

Front. Energy Res.

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A fault detection method of wind turbine pitch system using semi-supervised optimal margin distribution machine (ssODM) optimized by dynamic state transition algorithm (DSTA) [ssODM-DSTA] was proposed to solve the problem of obtaining the optimal hyperparameters of the fault detection model for the pitch system. This method was adopted to input the three hyperparameters of the ssODM into the dynamic state transition algorithm in the form of a three-dimensional vector to obtain the global optimal hyperparameters of the model, thus improving the performance of the fault detection model. Using a random forest to rank the priority of features of the pitch system fault data, the features with large weight proportions were retained. Then, the Pearson correlation method is used to analyze the degree of correlation among features, filter redundant features, and reduce the scale of features. The dataset was divided into a training dataset and a test dataset to train and test the proposed fault detection model, respectively. The real-time wind turbine pitch system fault data were collected from domestic wind farms to carry out fault detection experiments. The results have shown that the proposed method had a positive fault rate (FPR) and fault negative rate (FNR), compared with other optimization algorithms.

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Flow field distribution and structural strength performance evaluation of fixed offshore wind turbine based on digital twin technology

Cao,

Tang,

et al. 2023

Ocean Engineering

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Fault diagnosis of high power grid wind turbine based on particle swarm optimization BP neural network during COVID-19 epidemic period

Cited by 5 publications

References 17 publications

Analysis and Prediction of Epidemic Prevention and Control by Police Stations Based on Time Series

Analysis and Prediction of Epidemic Prevention and Control by Police Stations Based on Time Series

Wind Turbine Pitch System Fault Detection Using ssODM-DSTA

Flow field distribution and structural strength performance evaluation of fixed offshore wind turbine based on digital twin technology

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