A Prediction Method for Turbine Performance of Centrifugal Pumps Based on Improved Artificial Neural Network

An artificial neural network and performance prediction technology, applied in neural learning methods, biological neural network models, instruments, etc., to achieve the effects of simple method, high prediction accuracy and short calculation cycle

Active Publication Date: 2022-07-26
CHINA JILIANG UNIV
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Problems solved by technology

[0009] Aiming at the deficiencies of the prior art, the present invention provides a method for predicting the performance of a centrifugal pump as a turbine based on an improved artificial neural network, which can accurately and quickly predict the performance of the pump in the turbine when some parameters in the pumping state are known. hydraulic properties in the state

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  • A Prediction Method for Turbine Performance of Centrifugal Pumps Based on Improved Artificial Neural Network
  • A Prediction Method for Turbine Performance of Centrifugal Pumps Based on Improved Artificial Neural Network
  • A Prediction Method for Turbine Performance of Centrifugal Pumps Based on Improved Artificial Neural Network

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[0038] The present invention will be described in detail below according to the accompanying drawings and preferred embodiments, and the purpose and effects of the present invention will become clearer.

[0039] like figure 1 As shown, the centrifugal pump based on the improved artificial neural network of the present invention makes the turbine performance prediction method and comprises the following steps:

[0040] Step 1: Calculate the lift of the optimal working point of the pump as a turbine at each specific speed in sections H BEP,T ,flow Q BEP,T ;

[0041] When the centrifugal pump is in the pumping state, the specific speed N s,PWhen ∈(0,30], since the internal flow field of the ultra-low specific speed pump is quite different from that of the common centrifugal pump, the conversion relationship between the specific speed in the pumping state and the specific speed in the turbine state is used to determine the head conversion factor. To obtain the performance ...

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Abstract

The invention discloses a method for predicting the performance of a centrifugal pump as a turbine based on an improved artificial neural network. The method firstly calculates the head of the optimal working point of the pump as a turbine at each specific speed in sections. H BEP,T ,flow Q BEP,T , calculate the flow rate of each operating point under the turbine state Q i and Q BEP,T The ratio a and Q i corresponding H i and H BEP,T the square root b of the ratio of Carry out L1 and L2 regularization at the same time; use the training set to train the artificial neural network; finally input the geometric parameters, a and b of the centrifugal pump to be predicted in the turbine state into the artificial neural network after training, and output Head and efficiency for each flow condition. The method of the invention has wide application range, high prediction accuracy and short calculation period.

Description

technical field [0001] The invention belongs to the field of performance prediction of centrifugal pumps used as turbines, and particularly relates to a performance prediction method of centrifugal pumps used as turbines based on an improved artificial neural network. Background technique [0002] Electricity is an indispensable energy in daily production and life. From the point of view of power generation methods, at present, thermal power generation is still the main power generation method in my country. However, as my country and the world pay more and more attention to environmental issues, the contribution of thermal power plants to electricity production will be gradually reduced, and new energy sources and environmentally friendly energy methods will be vigorously developed to promote sustainable development. It is in this context that micro-hydropower is getting more and more attention. For remote areas, the installation of hydro turbines is inconvenient and the m...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F30/27G06N3/08G06F111/06G06F113/08
CPCG06F30/27G06N3/08G06F2111/06G06F2113/08Y04S10/50
Inventor 周佩剑余文进牟介刚
Owner CHINA JILIANG UNIV
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