Fault Diagnosis of PMSG Wind Power Generation System Based on LMD and MSE

Duan, Hao; Lu, Ming; Sun, Yongteng; Wang, Jinyu; Wang, Cheng; Chen, Zuguo

doi:10.1155/2020/5306473

Cited by 8 publications

(4 citation statements)

References 42 publications

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“…Figure 9 shows the power curve of the first 1000 hours after Global_intensity in the IHEPC dataset is aggregated with a granularity of 1 hour. [31], which is used to calculate the average of the squared errors between the true and predicted values. It is an absolute indicator commonly used in logistic regression tasks.…”

Section: Methodsmentioning

confidence: 99%

Multigranularity Building Energy Consumption Prediction Method Based on Convolutional Recurrent Neural Network

Wang

Gao³

et al. 2022

Wireless Communications and Mobile Computing

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For the building energy consumption models with complex scale sensitivity, it is difficult to achieve ideal prediction effect with single-granularity prediction model. Therefore, this paper proposed a multigranularity MgHa-LSTM model based on convolutional recurrent neural network, including a multigranularity feature extraction module and a long-term dependency capture module. Multigranularity feature extraction included granularity segmentation, feedback mechanism, and parallel convolutional modules, which can capture short-term scale sensitivity dependencies. Long-term dependency capture consists of a hybrid attention mechanism and long-short term memory layers, which are able to capture long-term dependencies. For building energy consumption patterns with different scale sensitivity, MgHa-LSTM, MLP, CNN, LSTM, and MsC-LSTM models were constructed on the IHEPC building energy consumption dataset used in this paper for comparative experiments. The experimental results showed that on the IHEPC dataset, the MSE of the building energy consumption prediction model is 0.2821 based on the MgHa-LSTM model proposed in this paper, which is equivalent to 93.72% of the MsC-LSTM model with the smallest MSE among other deep learning prediction models. Compared with other deep learning prediction models, the prediction results of the MgHa-LSTM building energy consumption prediction model are more accurate.

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Section: Methodsmentioning

confidence: 99%

Multigranularity Building Energy Consumption Prediction Method Based on Convolutional Recurrent Neural Network

Wang

Gao³

et al. 2022

Wireless Communications and Mobile Computing

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“…Fault detection in WTs is a topic of current interest [21,22]. It is one of the applications where deep learning is being explored in the wind energy field, together with forecasting and model identification.…”

Section: Related Workmentioning

confidence: 99%

Deep learning and fuzzy logic to implement a hybrid wind turbine pitch control

Sierra-García

Santos

2021

Neural Comput & Applic

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This work focuses on the control of the pitch angle of wind turbines. This is not an easy task due to the nonlinearity, the complex dynamics, and the coupling between the variables of these renewable energy systems. This control is even harder for floating offshore wind turbines, as they are subjected to extreme weather conditions and the disturbances of the waves. To solve it, we propose a hybrid system that combines fuzzy logic and deep learning. Deep learning techniques are used to estimate the current wind and to forecast the future wind. Estimation and forecasting are combined to obtain the effective wind which feeds the fuzzy controller. Simulation results show how including the effective wind improves the performance of the intelligent controller for different disturbances. For low and medium wind speeds, an improvement of 21% is obtained respect to the PID controller, and 7% respect to the standard fuzzy controller. In addition, an intensive analysis has been carried out on the influence of the deep learning configuration parameters in the training of the hybrid control system. It is shown how increasing the number of hidden units improves the training. However, increasing the number of cells while keeping the total number of hidden units decelerates the training.

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“…The literature 15 proposed a fault diagnosis method with LMD‐multiscale sample entropy and support vector machine, which achieved better results, but the feature extraction method using multiscale sample entropy has a greater sensitivity in the face of different faulty signal samples, and thus has poor generalization ability. Literature 16 addresses the problem of similar fault features and low diagnostic efficiency caused by IGBT open‐circuit faults in mmc inverters. A weighted‐amplitude permutation entropy and DS evidence fusion theory fault diagnosis method is proposed, and it is verified that weighted‐amplitude permutation entropy possesses better feature extraction capability.…”

Section: Introductionmentioning

confidence: 99%

Open circuit fault diagnosis of wind power converter based on VMD energy entropy and time domain feature analysis

Bai,

Li,

et al. 2023

Energy Science & Engineering

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Aiming at the shortcomings of feature extraction and fault identification in fault diagnosis of wind power converters, a novel method for open circuit fault diagnosis of wind power converters based on variational mode decomposition (VMD) energy entropy (EE) and time domain feature analysis (TDFA) is proposed. Primarily, the three‐phase output current at the grid side of the wind power converter is collected as the original signal, and the VMD is used to decompose the original signal into a series of intrinsic mode functions (IMF). To reduce noise interference as much as possible, the Pearson correlation coefficient between each mode component and the original signal under different fault states is analyzed, and the IMF component containing the major failure features is selected to calculate the energy entropy of each component; afterward, according to the Pearson correlation coefficient results, the modal components of the first layer are selected for time domain feature analysis; finally, the feature matrix that combines energy entropy and time domain feature analysis is inputted into the long short‐term memory neural network for training and fault identification. The simulation and experimental results show that the open circuit fault diagnosis method proposed in this paper has high accuracy and robustness.

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Fault Diagnosis of PMSG Wind Power Generation System Based on LMD and MSE

Cited by 8 publications

References 42 publications

Multigranularity Building Energy Consumption Prediction Method Based on Convolutional Recurrent Neural Network

Multigranularity Building Energy Consumption Prediction Method Based on Convolutional Recurrent Neural Network

Deep learning and fuzzy logic to implement a hybrid wind turbine pitch control

Open circuit fault diagnosis of wind power converter based on VMD energy entropy and time domain feature analysis

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