AI SearchApplications of kalman filtering techniques in the operation of hydropower systems
Call Number: AIT Thesis no.ET-87-4 Material type:
TextSeries: Asian Institute of Technology. Thesis ; no. ET-87-4Publication details: Bangkok : Asian Institute of Technology, 1987Description: 75, 18 pSubject(s): Online resources: Dissertation note: Thesis (M.Sc.) - Asian Institute of Technology, 1987 Summary: This paper studies the application of the Kalman filtering technique to the operation of hydropower systems in the following three aspects: daily river flow forecasting; weekly and monthly power generation forecasting 1 and hourly load forecasting. The procedures for method for estimating measurement variance R the study followed these the three aspects of application and of the system noise covariance Q of the Kalman filter were proposed, procedures and method. the and and In the case of CILI hydropower system, the study found that the application of Kalman filtering technique improves the performance of the fixed parameter model for daily flow forecasting in some cases; It is very useful for power generation forecasting for a runoff hydropower station with a known value of river inflow in a period; And it significantly improves the forecasting accuracy of hourly load forecasting compared with the fixed parameter models. In the second aspect of the study, the models of weekly and monthly river inflow and power generation for CA-AN runoff hydropower station were constructed, and the application of the Kalman filtering technique to these models improved the accuracy of power generation forecasting.
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A thesis submitter in partial fulfillment of the requirements for the degree of Master of Science, School of Environment, Resources and Development
Thesis (M.Sc.) - Asian Institute of Technology, 1987
This paper studies the application of the Kalman filtering technique to the operation of hydropower systems in the following three aspects: daily river flow forecasting; weekly and monthly power generation forecasting 1 and hourly load forecasting. The procedures for method for estimating measurement variance R the study followed these the three aspects of application and of the system noise covariance Q of the Kalman filter were proposed, procedures and method. the and and In the case of CILI hydropower system, the study found that the application of Kalman filtering technique improves the performance of the fixed parameter model for daily flow forecasting in some cases; It is very useful for power generation forecasting for a runoff hydropower station with a known value of river inflow in a period; And it significantly improves the forecasting accuracy of hourly load forecasting compared with the fixed parameter models. In the second aspect of the study, the models of weekly and monthly river inflow and power generation for CA-AN runoff hydropower station were constructed, and the application of the Kalman filtering technique to these models improved the accuracy of power generation forecasting.
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