Optimal load frequency control through combined state and control gain estimation for noisy measurements
Autor: | A. Unnikrishnan, Elizabeth Rita Samuel, Anju G. Pillai |
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Jazyk: | angličtina |
Rok vydání: | 2020 |
Předmět: |
State variable
lcsh:Distribution or transmission of electric power Load frequency control Computer science Linear quadratic regulator 020209 energy 020208 electrical & electronic engineering Automatic frequency control Energy Engineering and Power Technology 02 engineering and technology Linear-quadratic regulator State feedback control Extended Kalman filter lcsh:TK3001-3521 lcsh:Production of electric energy or power. Powerplants. Central stations Noise Electric power system Control theory lcsh:TK1001-1841 0202 electrical engineering electronic engineering information engineering State (computer science) Electrical and Electronic Engineering Single area power system Safety Risk Reliability and Quality |
Zdroj: | Protection and Control of Modern Power Systems, Vol 5, Iss 1, Pp 1-12 (2020) |
ISSN: | 2367-0983 2367-2617 |
DOI: | 10.1186/s41601-020-00169-5 |
Popis: | Combined estimation of state and feed-back gain for optimal load frequency control is proposed. Load frequency control (LFC) addresses the problem of controlling system frequency in response to disturbance, and is one of main research areas in power system operation. A well acknowledged solution to this problem is feedback stabilization, where the Linear Quadratic Regulator (LQR) based controller computes the feedback gain K from the known system parameters and implements the control, assuming the availability of all the state variables. However, this approach restricts control to cases where the state variables are readily available and the system parameters are steady. Alternatively, by estimating the states continuously from available measurements of some of the states, it can accommodate dynamic changes in the system parameters. The paper proposes the technique of augmenting the state variables with controller gains. This introduces a non-linearity to the augmented system and thereby the estimation is performed using an Extended Kalman Filter. This results in producing controller gains that are capable of controlling the system in response to changes in load demand, system parameter variation and measurement noise. |
Databáze: | OpenAIRE |
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