Optimal load frequency control throughcombined state and control gainestimation for noisy measurements

Anju G. Pillai; Elizabeth Rita Samuel; A. Unnikrishnan

doi:10.1186/s41601-020-00169-5

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Optimal load frequency control throughcombined state and control gainestimation for noisy measurements

Protection and Control of Modern Power Systems | 更新时间：2026-06-25

- Optimal load frequency control throughcombined state and control gainestimation for noisy measurements
- Protection and Control of Modern Power Systems Vol. 5, Issue 3, (2020)
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- DOI：10.1186/s41601-020-00169-5
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- Published：2020
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Anju G. Pillai, Elizabeth Rita Samuel, A. Unnikrishnan. Optimal load frequency control throughcombined state and control gainestimation for noisy measurements[J]. Protection and Control of Modern Power Systems, 2020, 5(3).
DOI：

Anju G. Pillai, Elizabeth Rita Samuel, A. Unnikrishnan. Optimal load frequency control throughcombined state and control gainestimation for noisy measurements[J]. Protection and Control of Modern Power Systems, 2020, 5(3). DOI： 10.1186/s41601-020-00169-5.

摘要

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.

Abstract

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

system parameter variation and measurement noise.

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