Adaptive robust PSS to enhance stabilization of interconnected power systems with high renewable energy penetration

Supriyadi, Ade; Takano, Hirotaka; Murata, Junichi; Goda, Tadahiro

doi:10.1016/j.renene.2013.09.044

Cited by 15 publications

(2 citation statements)

References 18 publications

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“…Diagram of the proposed adaptive robust control design is shown in Figure 3. In the proposed adaptive control, a system identification technique and the robust controller design method are combined into an indirect adaptive controller design [21], [22]. The identified model is used to monitor discrepancy between the actual power system output and the expected output (model output).…”

Section: Proposed Adaptive Robust Controllermentioning

confidence: 99%

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Adaptive robust control design to enhance smart grid power system stabilization using wind characteristics in Indonesia

Herbandono

Nandar

2021

IJPEDS

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<span lang="EN-US">This paper is interested to study power system stability in smart grid power system using wind characteristic in south of Yogyakarta, Indonesia. To overcome the intermittent of wind characteristics, this paper presents adaptive robust control design to enhance power system stabilization. The online identification system is used in this research, which updated whenever the estimated model mismatch exceeds predetermined bounds. Then genetic algorithm (GA) is applied to re-tune parameters controller based on the estimated model. The structure of controller is proportional integral (PI) controller due to the most applicable in industry, simple structure, low cost and high reliability. Robustness of controller is guaranteed by taking system uncertainties into consideration. The performance of the proposed controller has been carried out in a hybrid wind-diesel power system in comparison with previous work controller. Simulation results confirm that damping effect of the proposed controllers are much better that of the conventional controllers against various operating.</span>

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Section: Proposed Adaptive Robust Controllermentioning

confidence: 99%

“…The goal of system identification is to characterize the dynamic input-output relation of the underlying process [22]- [24]. In this study, output error (OE) identification method is used to construct the estimated model of each generator because the OE model is easily computed, familiarly used and has high accuracy.…”

Section: Proposed Adaptive Robust Controllermentioning

confidence: 99%