Geodetic Monitoring of Large-Span Underground Facilities During Construction of the Rogun Hydroelectric Power Station

Nazirov, J. A.; Davlatshoev, S. K.; Kozlov, D. V.

doi:10.1007/s10749-018-0965-6

Cited by 5 publications

(2 citation statements)

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“…After setting the basic parameters such as the number of neurons in the hidden layer, target error, learning rate and maximum training times, and normalizing the input data, the KTH component xnk of the NTH input variable enters the input layer of the BP neural network. The product of xnk, the weight wnh between the input layer and the hidden layer of the neural network, and the threshold θ between the input layer and the hidden layer of the neural network are accumulated into the hidden layer of the BP neural network, and the output hn of the hidden layer is obtained after the calculation of the transfer function f(x) of the BP neural network [13][14].…”

Section: Model Buildingmentioning

confidence: 99%

Average Daily Processing Prediction of Artificial Hydropower Station Based on Machine Learning

2020

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More than 20 years ago, China's hydropower undertakings rapid development, hydropower installed capacity has been a breakthrough. With the continuous improvement of hydropower installed capacity, the optimal dispatching of hydropower system is facing great challenges. In this paper, the average daily processing prediction of artificial hydropower station based on machine learning is studied. In this paper, the prediction model based on BPNN neural network is discussed to predict the daily runoff of hydropower station, and the overall system architecture is designed from the aspects of logical structure, physical structure and technical structure. Through THE simulation analysis of the water level of the hydropower station in different time periods, it is verified that the model has good simulation accuracy in the process of water level deduction, which lays a foundation for the optimization of hydropower station operation in the future.

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Section: Model Buildingmentioning

confidence: 99%

Average Daily Processing Prediction of Artificial Hydropower Station Based on Machine Learning

2020

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“…The generator hall of the Rogun hydroelectric power plant ( Fig. 1) possesses an internal width of 21 m, height 70 m, and length 220 m. A compartment containing the transformers with internal width of 19 m, height 37 m, and length 182 m is situated parallel to the generator hall 63 m away measured along the axes [1].…”

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confidence: 99%

Quality Control of Cementation Operations Performed in Country Massif in a Test Section by Seismic Well Logging

Davlatshoev¹

2020

Power Technol Eng

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The process of monitoring the effectiveness of consolidation cementation of the country massif of the Rogun hydroelectric power plant's generator hall by means of seismic well logging along with selection of a test section in the aleurolite zone and the configuration of drilled cementation and test wells are considered. Results of processing and interpretation of the results of processing and interpretation of the results of geophysical studies are presented.

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Ultrasound Study of the Quality of Consolidation Grouting Works for Retaining Rocks in Underground Structures

Davlatshoev¹

2020

Power Technol Eng

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Geodetic Monitoring of Large-Span Underground Facilities During Construction of the Rogun Hydroelectric Power Station

Cited by 5 publications

References 0 publications

Average Daily Processing Prediction of Artificial Hydropower Station Based on Machine Learning

Average Daily Processing Prediction of Artificial Hydropower Station Based on Machine Learning

Quality Control of Cementation Operations Performed in Country Massif in a Test Section by Seismic Well Logging

Ultrasound Study of the Quality of Consolidation Grouting Works for Retaining Rocks in Underground Structures

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