scholarly journals Thermal station modelling and optimal control based on deep learning

2021 ◽  
Vol 25 (4 Part B) ◽  
pp. 2965-2973
Author(s):  
Min Cao
Keyword(s):  
Deep Learning ◽  
Thermal Power Station ◽  
Control Method ◽  
Learning Algorithm ◽  
Thermal Power ◽  
Heating System ◽  
Utilization Rate ◽  
Power Station ◽  
Deep Learning Algorithm ◽  
Deterministic Strategy

To solve the mismatch between heating quantity and demand of thermal stations, an optimized control method based on depth deterministic strategy gradient was proposed in this paper. In this paper, long short-time memory deep learning algorithm is used to model the thermal power station, and then the depth deterministic strategy gradient control algorithm is used to solve the water supply flow sequence of the primary side of the thermal power station in combination with the operation mechanism of the central heating system. In this paper, a large number of historical working condition data of a thermal station are used to carry out simulation experiment, and the results show that the method is effective, which can realize the on-demand heating of the thermal station a certain extent and improve the utilization rate of heat.

scholarly journals Microgrid Group Control Method Based on Deep Learning under Cloud Edge Collaboration

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Yazhe Mao ◽  
Baina He ◽  
Deshun Wang ◽  
Renzhuo Jiang ◽  
Yuyang Zhou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Power Distribution ◽  
Control Method ◽  
Learning Algorithm ◽  
Economic Benefits ◽  
Group Model ◽  
Deep Learning Algorithm ◽  
Group Control ◽  
Load Characteristics ◽  
Edge Side

Aiming at the economic benefits, load fluctuations, and carbon emissions of the microgrid (MG) group control, a method for controlling the MG group of power distribution Internet of Things (IoT) based on deep learning is proposed. Firstly, based on the cloud edge collaborative power distribution IoT architecture, combined with distributed generation, electric vehicles (EV), and load characteristics, the MG system model in the power distribution IoT is established. Then, a deep learning algorithm is used to train the features of the data model on the edge side. Finally, the group control strategy is adopted in the power distribution cloud platform to reasonably regulate the coordinated output of multiple energy sources, adjust the load state, and realize the economic operation of the power grid. Based on the MATLAB platform, a group model of MG is built and simulated. The results show the effectiveness of the proposed control method. Compared with other methods, the proposed control method has higher income and minimum carbon emission and realizes the economic and environmental protection system operation.

scholarly journals Tractor Assistant Driving Control Method Based on EEG Combined With RNN-TL Deep Learning Algorithm

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 163269-163279
Author(s):  
Wei Lu ◽  
Yuning Wei ◽  
Jinxia Yuan ◽  
Yiming Deng ◽  
Aiguo Song
Keyword(s):  
Deep Learning ◽  
Control Method ◽  
Learning Algorithm ◽  
Deep Learning Algorithm

scholarly journals Fault diagnosis of motor bearing based on deep learning

2019 ◽  
Vol 11 (9) ◽  
pp. 168781401987562 ◽  
Author(s):  
Yifan Jian ◽  
Xianguo Qing ◽  
Liang He ◽  
Yang Zhao ◽  
Xiao Qi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Working Conditions ◽  
Input Data ◽  
Learning Algorithm ◽  
Random Noise ◽  
Thermal Power ◽  
Efficient Operation ◽  
Deep Learning Algorithm ◽  
Motor Bearing

The effective fault diagnosis of the motor bearings not only can ensure the smooth and efficient operation of equipment but also can detect and eliminate the running fault in time to prevent major accidents. Based on deep learning algorithm, this article constructs a stacked auto-encoder network. The input data are compressed and reduced by introducing sparsity constraint, so that the network can accurately extract the fault characteristics of the input data, and the fault recognition ability of the network can be improved by introducing random noise. The simulation result shows that the stacked auto-encoder network can not only overcome the shortcomings of traditional fault diagnosis method that requires to distinguish fault samples manually and needs a large number of prior knowledge but also realize the self-learning of fault signal feature. The accuracy rate of fault identification reaches 98%, 94%, 96%, and 95.5% in four different working conditions. What’s more, the network can exhibit strong robustness under different working conditions. Finally, the new research ideas of fault diagnosis in thermal power plant are put forward by copying the idea of fault diagnosis of motor bearing.

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