Application of the structured singular value theory for robust stability and control analysis in multimachine power systems. I. Framework development

Djukanović, M.; Khammash, Mustafa; Vittal, Vijay

doi:10.1109/59.736270

Cited by 73 publications

(44 citation statements)

References 12 publications

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“…In addition, the µ method is generally applied to linear systems while most systems analysed have nonlinear behaviour. A few works in the literature have successfully applied the µ method to analyse stability of conventional power systems [33], [34], [35], [36], [37], [38]. However, the methodology applied through associated software is not discussed and multiple uncertainties are not considered.…”

Section: Introductionmentioning

confidence: 99%

Robust Stability Analysis of a DC/DC Buck Converter Under Multiple Parametric Uncertainties

Sumsurooah

Odavić

Bozhko

et al. 2018

IEEE Trans. Power Electron.

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Abstract-Stability studies are a crucial part of the design of power electronic systems, especially for safety critical applications. Standard methods can guarantee stability under nominal conditions but do not take into account the multiple uncertainties that are inherent in the physical system or in the system model. These uncertainties, if unaccounted for, may lead to highly optimistic or even erroneous stability margins. The structured singular value-based µ method justifiably takes into account all possible uncertainties in the system. However, the application of the µ method to power electronic systems with multiple uncertainties is not widely discussed in the literature. This work presents practical approaches to applying the µ method in the robust stability analysis of such uncertain systems. Further, it reveals the significant impact of various types of parametric uncertainties on the reliability of stability assessments of power electronic systems. This is achieved by examining the robust stability margin of the dc/dc buck converter system, when it is subject to variations in system load, line resistance, operating temperature and uncertainties in the system model. The µ predictions are supported by time domain simulation and experimental results.Index Terms-Robust stability analysis, dc/dc buck power electronic converter, Linear fractional transformation, Structured singular value, µ analysis.

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Section: Introductionmentioning

confidence: 99%

Robust Stability Analysis of a DC/DC Buck Converter Under Multiple Parametric Uncertainties

Sumsurooah

Odavić

Bozhko

et al. 2018

IEEE Trans. Power Electron.

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“…Furthermore, a resonance condition derived from normal form transformation over parameter space was proposed to indicate unsatisfactory system dynamic performance [9,10]. Recently, a systematic procedure for the synthesis of a supplementary damping controller for static var compensators in multimachine power systems was proposed, while the robust performance in terms of the structured singular value was used as the measure of control performance to obtain a wide range of operating conditions [11] for IEEE 50 generator system [12], for a wide I-angel of operating conditions [13,14], and for Manitoba Hydro HVDC system (Nelson River system) [15]. Although these parameterizations also require prior knowledge of certain actuators, their advantage over the parameterization is that they are not uniquely determined for each model.…”

Section: Introductionmentioning

confidence: 99%

Design of a power system stabilizer using decentralized adaptive model following tracking control approach

2009

Int J Numerical Modelling

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SUMMARYThis paper presents the design of a power system stabilizer using decentralized adaptive model following tracking control (DAMFTC) approach to damp oscillations of generators in transient response subjected to uncertainties and generating fault actuators. The power system is represented as a collection of interconnected dynamical subsystems each described by a set of differential/algebraic equations using a clear representation of load voltage magnitude with matched and unmatched time-varying uncertainties. All adaptive learning algorithms in this control system are derived in the sense of Lyapunov stability analysis subject to state errors due to uncertainties and fault section, so that stability and robustness of the closed-loop system are ensured and asymptotic-state tracking can be achieved. An adaptive bound estimation algorithm is investigated to relax the requirement for the bound of uncertainties. The effectiveness of the proposed approach is demonstrated by distributing a detailed simulation of the threemachine nine-bus system with nonlinear interactions, uncertainties, and fault actuators. The simulation includes the effects of network and stator transients.

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“…representative of unmodeled system dynamics [1,2], or structured associated with slow variations in power system parameters, such as loading and other changes in operating conditions [4][5][6] and can result in conservative assessment of system stability.…”

Section: Introductionmentioning

confidence: 99%

A μ-analysis approach to power system stability robustness evaluation

Castellanos¹,

Messina²,

Sarmiento³

2008

Electric Power Systems Research

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Application of the structured singular value theory for robust stability and control analysis in multimachine power systems. I. Framework development

Cited by 73 publications

References 12 publications

Robust Stability Analysis of a DC/DC Buck Converter Under Multiple Parametric Uncertainties

Robust Stability Analysis of a DC/DC Buck Converter Under Multiple Parametric Uncertainties

Design of a power system stabilizer using decentralized adaptive model following tracking control approach

A μ-analysis approach to power system stability robustness evaluation

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