Robust ranking of loads by using sensitivity factors and limited number of points from a hyperspace of uncertain parameters

Maslennikov, V.A.; Milanović, Jovica V.; Ustinov, S. M.

doi:10.1109/tpwrs.2002.800892

Cited by 26 publications

(16 citation statements)

References 7 publications

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“…Therefore, even with small MC simulation numbers for and (of less than 1000), this approach will not be suitable due to the computational expense. Some approaches that use efficient sampling, such as Latin hypercube [24] or quasi-random [25], [26], can be used to reduce and . However the dimensionality curse is still a major limitation as increases due to the need for multiple integration loops.…”

Section: Distribution-based Techniquesmentioning

confidence: 99%

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Assessing the Applicability of Uncertainty Importance Measures for Power System Studies

Preece

Milanović

2016

IEEE Trans. Power Syst.

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This paper critically evaluates a number of uncertainty importance measures for use in power system stability studies. Sensitivity analysis of uncertain system parameters is vital as new technologies proliferate and the total level of system uncertainty grows. Accurate assessment of the importance of different uncertainties can guide power system operators towards parameters which will require the greatest levels of mitigation or increased monitoring in order to reduce the uncertainty and its subsequent impact. Local and global sensitivity analysis techniques are described and evaluated within this paper, including nonparametric methods, variance-based approaches, and distribution-based techniques. The techniques are illustrated using a large 295-bus realistic network model of a generic distribution system. Numerical experiments on dynamic models are used in order to assess the impact of uncertainties on the mitigation of system frequency excursions using single-site and distributed energy storage devices. Robin Preece (GS'10-M'13) received the B.Eng. degree in electrical and electronic engineering and Ph.D. degree from the University of Manchester, Manchester, U.K., in 2009 and 2013, respectively. He is currently a Lecturer in power systems engineering with the University of Manchester, Manchester, U.K. Jovica V. Milanović (M'95-SM'98-F'10) received the Dipl.Ing. and M.Sc. degrees from the University of Belgrade, Belgrade, Yugoslavia, the Ph.D. degree from the University of Newcastle, Newcastle, Australia, and the D.Sc. degree from the University of Manchester, Manchester, U.K., all in electrical engineering.Currently, he is a Professor of electrical power engineering,

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Section: Distribution-based Techniquesmentioning

confidence: 99%

“…To overcome this, more efficient sampling [25], [26] is used to evenly sample the search space. The obtained weighted results are then used to produce a probability distribution using a kernel smoothing density estimate.…”

Section: A Case A: Single Storage Unitmentioning

confidence: 99%

Assessing the Applicability of Uncertainty Importance Measures for Power System Studies

Preece

Milanović

2016

IEEE Trans. Power Syst.

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“…(15), ∆X can be expressed in terms of ∆P L and ∆Q L . Substituting the expression for ∆X in (14), (16) and (17), we obtain the formulation of ∆ζ k expressed by ∆P L and ∆Q L . The equivalent optimization model in the desired final form is…”

Section: The Slp Model For Solving Interval Damping Ratiomentioning

confidence: 99%

“…In [13][14], power system damping variation due to the interval uncertainty of load characteristic parameters, is investigated by using a special sampling method which samples limited number of points within interval limits of load parameters. However, the active and reactive steady-state load magnitudes are considered to be invariable in these papers.…”

Section: Introductionmentioning

confidence: 99%

Calculation of interval damping ratio under uncertain load in power system

Xing¹,

Chen²,

Wu³

2012

Bulletin of the Polish Academy of Sciences: Technical Sciences

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Abstract. The problem of small-signal stability considering load uncertainty in power system is investigated. Firstly, this paper shows attempts to create a nonlinear optimization model for solving the upper and lower limits of the oscillation mode's damping ratio under an interval load. Then, the effective successive linear programming (SLP) method is proposed to solve this problem. By using this method, the interval damping ratio and corresponding load states at its interval limits are obtained. Calculation results can be used to evaluate the influence of load variation on a certain mode and give useful information for improvement. Finally, the proposed method is validated on two test systems.

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“…Monte Carlo method (MCM) [9,10] can be used to get acceptable results and statistical properties of system eigenvalues. However, this requires large number of random sampling and cumbersome computational efforts [11][12][13].…”

Section: Introductionmentioning

confidence: 99%