2014
DOI: 10.1109/tcst.2013.2279939
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Multivariable Self-Tuning Feedback Linearization Controller for Power Oscillation Damping

Abstract: Abstract-The objective of this paper is to design a measurement based self-tuning controller which does not rely on accurate models and deals with nonlinearities in system response. A special form of neural network (NN) model called as feedback linearizable neural network (FLNN) compatible with feedback linearization technique is proposed for representation of nonlinear power systems behaviour. Levenberg−Marquardt (LM) is applied in batch mode to improve the model estimation. A time varying feedback linearizat… Show more

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Cited by 18 publications
(10 citation statements)
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References 36 publications
(46 reference statements)
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“…where d is estimated deadzone widths. Given the fuzzy logic compensator with rulebase (16), the throughput of the compensator plus deadzone is given by…”
Section: Compensation Of Deadzone Nonlinearitymentioning
confidence: 99%
See 1 more Smart Citation
“…where d is estimated deadzone widths. Given the fuzzy logic compensator with rulebase (16), the throughput of the compensator plus deadzone is given by…”
Section: Compensation Of Deadzone Nonlinearitymentioning
confidence: 99%
“…In this paper, we present the deadzone compensation method for feedback linearizable nonlinear systems. In Section 2, a brief overview on the feedback linearization theory is given [16] [17]. The fuzzy deadzone compensation technique is described in Section 3.…”
Section: Introductionmentioning
confidence: 99%
“…Feedback linearization is a technique that reduces the nonlinearity of nonlinear systems, in which an equivalent model including nonlinearity can be switched to a linear model [11]. In order to design a controller that reduces the resonance frequency component, in the differential equation of equation (1), when the differential equation of the grid current is expressed as a state equation like equation (4), it is to be equation (5).…”
Section: Feedback Linearizationmentioning
confidence: 99%
“…In addition, the improvement introduced by the corresponding quadratic prediction algorithm enables the reduction of undesired peaks in transient processes. In [22], a generic nonlinear multi-input single-output self-tuning controller is employed to damp the oscillations under various post-disturbance operating conditions, and the proposed method has been proved to be superior to the conventional model-based controller. Additionally, considering parameter uncertainties in systems, reachability analysis is used in [23] for controller tuning, which can ensure stable operation against parameter uncertainties in power systems.…”
Section: Introductionmentioning
confidence: 99%