Stability Analysis of Wind Turbines Combined with Rechargeable Batteries Based on Markov Jump Linear Systems

Dai, Xiaokun; Song, Yang; Schüller, Mira; Schramm, Dieter

doi:10.1007/978-981-10-6364-0_13

Cited by 1 publication

(2 citation statements)

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“…Research has shown that the stochastic behaviour of the mean WS can be represented as a Markovian process [13,14]. This behaviour has been exploited in recent WT control strategies as the ones in [15][16][17]. Motivated by this research, we assume that that {ξ v k } is a discrete homogenous Markov chain taking values in the finite set {1, .…”

Section: Markovian Jump Discrete State-space Modelingmentioning

confidence: 99%

“…Remark 4. Note that certain degree of conservatism is introduced when using switched observer gain matrices in the form of (21) for the regions lying in the WS zone II, where the state-space matrices A(θ k ) and B(θ k ) are not constant (see (16)). Even though switched polytopic gain matrices could be used instead, the small variations experienced by these matrices (see Fig.2) justify the choice in (21) that reduces the observer complexity.…”

Section: Fe Architecturementioning

confidence: 99%

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Markovian jump system approach for the estimation and adaptive diagnosis of decreased power generation in wind farms

Sales-Setién¹,

Peñarrocha-Alós²

2019

IET Control Theory & Applications

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In this work, we propose a Markovian jump model of the power generation system of a wind turbine and we present a closed-loop model-based observer to estimate the faults related to energy losses. The observer is designed through an H∞-based optimization problem that optimally fixes the trade-off between the observer fault sensitivity and robustness. The fault estimates are then used in data-based decision mechanisms for achieving fault detection and isolation. The performance of the strategy is then ameliorated in a wind farm level scheme that uses a bank of the aforementioned observers and decision mechanisms. Finally, the proposed approach is tested using a well-known benchmark in the context of wind farm fault diagnosis.

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