Multiobjective H2/H∞ Control Design with Regional Pole Constraints

Hardiansyah, Hardiansyah; Junaidi, Junaidi

doi:10.12928/telkomnika.v10i1.765

Cited by 7 publications

(2 citation statements)

References 16 publications

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“…Therefore, state-space equations for the tested power system considering wind turbine are obtained as in is given in (18) and (19). 45,47 x…”

Section: Figurementioning

confidence: 99%

“…where control signal U(s) = W s (s)K(s) and γ is the boundary of H ∞ norm, it is a small positive value which is indicated the control efficiency. Linear matrix inequality (LMI) algorithm is used to solve this optimization problem by minimizing the cost function 47 and to design H ∞ controller K(s). 31,48 In this paper, the DSC-PLL has been simulated based on its structure and its parameters of Huang and Rajashekara.…”

Section: Robust Control Design For Virtual Inertia Controlmentioning

confidence: 99%

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Adaptive filtering with robust controller for improvement of inertial response of wind turbine

Aali

Besmi

Kazemi

2019

Int Trans Electr Energ Syst

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Summary It is necessary to study the impact of large‐scale wind turbines with virtual inertia control on power system stability considering the effects of frequency measurement. The phase‐locked loop (PLL) measures the system frequency. The operation of the PLL is deteriorated when the voltage contains sags, unbalance, high harmonics, and sudden drop frequency. Cascaded delayed signal cancelation (DSC) PLL technique can be used for frequency measurement under nonideal voltage due to its good harmonics filtering capability. But there are some oscillations and errors in the frequency estimation by the DSC‐PLL that led not to suitable frequency control in a doubly fed induction generator (DFIG). In this paper, an adaptive complex least mean square (ACLMS) algorithm is used for frequency estimation. The ACLMS algorithm is an adaptive filter, which can track frequency deviation, accurately and rapidly. In addition, this paper addresses robust frequency control in the DFIG. In a power system with wind turbines, load change, power fluctuation disturbance, and a dynamical perturbation influence the system frequency. In this condition, the conventional‐inertia controller is faced with challenges. H∞‐robust controller develops the frequency control loop. The estimated frequency using the ACLMS algorithm feeds the robust controller to improve the inertial response of the DFIG. This paper compares between H∞ and optimal conventional‐based inertia controller. The simulation validates that the ACLMS estimation with the H∞ inertia controller simultaneously provides frequency support to a low‐inertia tested power system, effectively. It improves frequency stability and resiliency under different disturbances and uncertainty.

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“…Therefore, state-space equations for the tested power system considering wind turbine are obtained as in is given in (18) and (19). 45,47 x…”

Section: Figurementioning

confidence: 99%