Performance evaluation of Kainji hydro-electric power plant using artificial neural networks and multiple linear regression

Ozigis, I. I.; Adeyemi, Rasaq Ayodele; Ondachi, Paul A.; Oodo, Stephen

doi:10.1007/s42108-021-00135-3

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…Models like the multi-linear regression (MLR) and Artificial neural networks (ANN) could come in handy to resolve such issues. Ozigis [13], reported that Karadas et al mentioned that both ANN and MLR have been found to be effective in analyzing time series data of power plants and can also perform well when exposed to random data. Thus, they could be used as handy tools in predicting tendencies for high bearing temperatures in gas turbine.…”

Section: Introductionmentioning

confidence: 99%

Gas Turbine Bearing Temperature Monitoring via Regression Modelling

Mohammed

Ozigis²,

Lawal

2023

AJERD

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This paper focuses on using Regression technique (MLR) towards finding solution to incidence of high compressor bearing temperature on one of the units at Geregu power plant in Ajaokuta, Nigeria. Monitoring of parameters related to the bearing temperature was carried out to find out causes for the high bearing temperature fault and came up with successful diagnosis by interrelating the gasturbine current lube oil test results of parameters like the kinematic viscosities, % concentration of additives and flash point with reference and standard VG46 lube oil data published in literature. Using statistical tools like the Pearson correlation and co-variant metrics for the five-years, the viscosities at 100oC and 40oC were selected as the input of the MLR model based on their Pearson coefficients of (-98.08%) and (-99.68%) respectively relative to the compressor bearing temperature and the covariance strength of the two parameters independently. The MLR model for the bearing temperature prediction gave a root mean square error of 0.121 and coefficient of determination(R2) of 99.71%. The model predicts that by the 2nd quarter of 2025, the bearing temperature would have reached the alarm point(900C) from the current value of 850C and that by the 1st quarter of 2027, the bearing temperature would have reached the trip point (1200C). Conclusion reached is that a well formulated data driven model can reliably forecast bearing temperature and together with sensors aid in gasturbine condition monitoring. Likewise, it is concluded that shearing due to the consistent high temperature operation of the gasturbine lube oil is responsible for the depletion of the Zinc(-23.9%) and Magnesium(-26%) additives leading to the decay in the viscosity and consequent bearing temperature increment. Recommendation made is to either replenish oil with antiwear additives or completely replace the oil to minimize the bearing wear rate and thus the bearing temperature.

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Section: Introductionmentioning

confidence: 99%

Gas Turbine Bearing Temperature Monitoring via Regression Modelling

Mohammed

Ozigis²,

Lawal

2023

AJERD

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Monthly Stream Flows Estimation in the Karasu River of Euphrates Basin With Artificial Neural Networks Approach

Katipoğlu

2022

Mühendislik Bilimleri Ve Tasarım Dergisi

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This study aims to estimate streamflow values with artificial neural networks (ANN) using various meteorological parameters. In developing the ANN model, various combinations of precipitation, air temperatures, and potential evapotranspiration values were used as inputs, and streamflow values were obtained. Meteorological data is divided into 70% train, 15% test, and 15% validation. In the model's design, various training algorithms, network architecture, input combinations, and the number of iterations were tried, and the most suitable model was tested. Correlation coefficient (R), coefficient of determination (R2), absolute error (AE), and absolute relative error (ARE) coefficients were compared, and the most suitable model was selected. According to the analysis results, the optimal model was obtained using 2000 iterations, the architecture of the 4-4-1 model, and the Quasi-Newton algorithm. It was determined that the ANNs successfully modeled the rainfall-runoff relationship and produced reliable estimates. In addition, it was revealed that the inclusion of potential evapotranspiration values obtained by the Thornthwaite method into the model increases the model's success.

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Performance evaluation of Kainji hydro-electric power plant using artificial neural networks and multiple linear regression

Cited by 2 publications

References 14 publications

Gas Turbine Bearing Temperature Monitoring via Regression Modelling

Gas Turbine Bearing Temperature Monitoring via Regression Modelling

Monthly Stream Flows Estimation in the Karasu River of Euphrates Basin With Artificial Neural Networks Approach

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