Prediction of small hydropower plant power production in Himreen Lake dam (HLD) using artificial neural network
In developing countries, the power production is properly less than the request of power or load, and sustaining a system stability of power production is a trouble quietly. Sometimes, there is a necessary development to the correct quantity of load demand to retain a system of power production stea...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier Ltd
2018
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/19521/ http://umpir.ump.edu.my/id/eprint/19521/ http://umpir.ump.edu.my/id/eprint/19521/ http://umpir.ump.edu.my/id/eprint/19521/1/Prediction%20of%20small%20hydropower%20plant%20power-fkee-2018.pdf |
| Summary: | In developing countries, the power production is properly less than the request of power or load, and sustaining a system stability of power production is a trouble quietly. Sometimes, there is a necessary development to the correct quantity of load demand to retain a system of power production steadily. Thus, Small Hydropower Plant (SHP) includes a Kaplan turbine was verified to explore its applicability. This paper concentrates on applying on Artificial Neural Networks (ANNs) by approaching of Feed-Forward, Back-Propagation to make performance predictions of the hydropower plant at the Himreen lake dam-Diyala in terms of net turbine head, flow rate of water and power production that data gathered during a research over a 10 year period. The model studies the uncertainties of inputs and output operation and there’s a designing to network structure and then trained by means of the entire of 3570 experimental and observed data. Furthermore, ANN offers an analyzing and diagnosing instrument effectively to model performance of the nonlinear plant. The study suggests that the ANN may predict the performance of the plant with a correlation coefficient (R) between the variables of predicted and observed output that would be higher than 0.96. |
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