Modeling of Reservoir Inflow for Hydropower Dams Using Artificial Neural Network

Salami, Adebayo Wahab; Mohammed, A.A.; Adeyemo, JA; Olanlokun, OK

doi:10.4314/njt.v34i1.4

Cited by 9 publications

(8 citation statements)

References 14 publications

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“…Understanding reservoir flow pattern is therefore fundamental to achieving outstanding success during the process of monitoring of water budget in a reservoir [31,32]. We are required to know the lowest dependable flow on one hand and the highest flood level possible in the river on the other hand.…”

Section: Resultsmentioning

confidence: 99%

Improvement of hydroelectric power generation using pumped storage system

et al. 2018

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Hydroelectric power is a renewable source of energy. By principle, hydroelectric power generation relies on the law of conservation of energy where kinetic energy that resulted from the movement of the mass of water from the river is translated into electricity, the quantum of which depends on systemic variables viz: plant efficiency, volumetric water flow through the turbine and the head of the water from the water surface to the turbine. Understanding the dynamics of these variables, and the correlation between them are core to proper planning and management of a hydroelectric power station. In this Study, simple mathematical methods that include linear programming and statistical analysis based on simulation techniques were used to evaluate vital parameters based on the data obtained from the Hydrologic units of the Shiroro Power Stations in Nigeria. The overall aim of the study is to idealize power generation at Shiroro dam in and out of raining season so as to ensure optimum generation of electricity all year round in order to achieve energy sufficiency in Nigeria. The result of the study is encouraging as it supports the viability of the pumped storage system for generating hydroelectric power all year round. The coupling of the hydroelectric power with pumped storage system if properly harnessed could be the needed panacea for the erratic power supply in Nigeria.

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Section: Resultsmentioning

confidence: 99%

Improvement of hydroelectric power generation using pumped storage system

et al. 2018

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“…A Grey Forecasting model based on BP Neural Network for Crude Oil Production and Consumption in China was carried out by [8]. Artificial Neural Networks are useful for predicting oilfield output because they require few a priori assumptions about the model [9]. The optimization of oilfield production using neural network was carried out by [10].…”

Section: In This In Thismentioning

confidence: 99%

A Mathematical Model for Predicting Output in an Oilfield in the Niger Delta Area of Nigeria

Oladeinde¹,

Ohwo²,

Oladeinde³

2015

Nig. J. Tech.

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in the six variable model was subjected to further analysis. The in the six variable model was subjected to further analysis. The in the six variable model was subjected to further analysis. The in the six variable model was subjected to further analysis. The regression coefficient screening proces regression coefficient screening proces regression coefficient screening proces regression coefficient screening process resulted in a two variable model for predicting oil field output. The s resulted in a two variable model for predicting oil field output. The s resulted in a two variable model for predicting oil field output. The s resulted in a two variable model for predicting oil field output. The resultant model was found to have greater utility in predicting oil field output as it produced less residual. The resultant model was found to have greater utility in predicting oil field output as it produced less residual. The resultant model was found to have greater utility in predicting oil field output as it produced less residual. The resultant model was found to have greater utility in predicting oil field output as it produced less residual. The present model can be of invaluable use to the oil field present model can be of invaluable use to the oil field present model can be of invaluable use to the oil field present model can be of invaluable use to the oil field manager manager manager manager. . . .

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“…19% of the installed capacity is provided by the more reliable and cheap hydropower plants. The three major hydropower plants in Nigeria are the Kainji hydroelectric power station (KHEPS), the Jebba hydroelectric power station (JHEPS) and the Shiroro hydroelectric power station [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

A progressive domain expansion method for solving optimal control problem

et al. 2020

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Electricity generation at the hydropower stations in Nigeria has been below the expected value. While the hydro stations have a capacity to generate up to 2,380 MW, the daily average energy generated in 2017 was estimated at around 846 MW. A factor responsible for this is the lack of a proper control system to manage the transfer of resources between the cascaded Kainji-Jebba Hydropower stations operating in tandem. This paper addressed the optimal regulation of the operating head of the Jebba hydropower reservoir in the presence of system constraints, flow requirement and environmental factors that are weather-related. The resulting two-point boundary value problem was solved using the progressive expansion of domain technique as against the shooting or multiple shooting techniques. The results provide the optimal inflow required to keep the operating head of the Jebba reservoir at a nominal level, hence ensuring that the maximum number of turbo-alternator units are operated.

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Modeling of Reservoir Inflow for Hydropower Dams Using Artificial Neural Network

Cited by 9 publications

References 14 publications

Improvement of hydroelectric power generation using pumped storage system

Improvement of hydroelectric power generation using pumped storage system

A Mathematical Model for Predicting Output in an Oilfield in the Niger Delta Area of Nigeria

A progressive domain expansion method for solving optimal control problem

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