Discrete Data AGC of Hydrothermal Systems Under Varying Turbine Time Constants Along With the Power System Loading Conditions

Discrete Data AGC of Hydrothermal Systems Under Varying Turbine Time Constants Along With the Power System Loading Conditions

Abstract:

The paper deals with the discrete data automatic generation control (AGC) of two area hydrothermal power system operating at different loading conditions. The power system load varies considerably throughout the day and accordingly, thermal and hydropower systems are scheduled to operate at different loading conditions. It is found that steam as well as hydro turbine time constants vary as the power system loading varies. In earlier AGC studies, these parameters are considered to be constant, irrespective of power system loadings. This paper studies the dynamic performance of hydrothermal power system considering the variation of these turbine time constants along with the nominal loading of power system. The studies have been conducted for linear as well as nonlinear models of speed governor and hydroturbine models recommended by the IEEE committee. It is also discovered that conventional empirical formula-based hydraulic governor settings recommended by the IEEE working group are completely unacceptable for hydrothermal power system. A maiden attempt is made to optimize the hydraulic governor settings simultaneously along with the controller gains using metaheuristic algorithm. The comparison of system dynamic responses reveal that optimization-based hydrogovernor settings give the better dynamic performance in case of linear as well as nonlinear models of hydro system, and therefore are strongly recommended over the conventional settings. The paper also deals with the optimum selection of sampling periods for discrete data AGC operation of hydrothermal systems.
Discrete Data AGC of Hydrothermal Systems Under Varying Turbine Time Constants Along With the Power System Loading Conditions
Published in: IEEE Transactions on Industry Applications Volume: 53Issue: 5, Sept.-Oct. 2017 )
Page(s): 4998 – 5013
Date of Publication: 31 May 2017
 ISSN Information:

INSPEC Accession Number: 17192536

Publisher: IEEE

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