Improved probabilistic method for power system dynamic stability studies

Wang, K.W.; Chung, C.Y.; Tse, C.T.; Tsang, K.M.

doi:10.1049/ip-gtd:20000025

Cited by 55 publications

(29 citation statements)

References 4 publications

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“…With loads represented by random variables, the eigenvalues are random variables. A particular eigenvalue can represented as a nonlinear function of nodal voltages [20],…”

Section: Probabilistic Characteristics Of Eigenvaluementioning

confidence: 99%

“…According to (19) and (20), the means and covariances of eigenvalues of system state matrix can be obtained, and the computed eigenvalues are approximated by normal distribution according to linearized relationship [23]. Probability of system stability is determined from the most critical eigenvlaues k k , according to [21] …”

Section: Probabilistic Stability Analysesmentioning

confidence: 99%

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Determination and enhancement of probabilistic stability margin with load uncertainties

Zhang

Tse

Wang

et al. 2013

International Journal of Electrical Power & Energy Systems

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“…With loads represented by random variables, the eigenvalues are random variables. A particular eigenvalue can represented as a nonlinear function of nodal voltages [20],…”

Section: Probabilistic Characteristics Of Eigenvaluementioning

confidence: 99%

Section: Probabilistic Stability Analysesmentioning

confidence: 99%

Determination and enhancement of probabilistic stability margin with load uncertainties

Zhang

Tse

Wang

et al. 2013

International Journal of Electrical Power & Energy Systems

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“…Up to now, the PSSSA methods of power system can be classified into analytical methods and numerical methods. The analytical methods include point estimate method [3,4], cumulant method [5], and probabilistic collocation method (PCM) [6,7]. These methods usually need to approximately simplify the complex nonlinear functional relationship between the eigenvalues and input random variables, and the calculation error is inevitable.…”

Section: Introductionmentioning

confidence: 99%

Latin Hypercube Sampling Based Probabilistic Small Signal Stability Analysis Considering Load Correlation

Zuo¹,

Li²,

Cai³

et al. 2014

Journal of Electrical Engineering and Technology

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-A novel probabilistic small signal stability analysis (PSSSA) method considering load correlation is proposed in this paper. The superiority Latin hypercube sampling (LHS) technique combined with Monte Carlo simulation (MCS) is utilized to investigate the probabilistic small signal stability of power system in presence of load correlation. LHS helps to reduce the sampling size, meanwhile guarantees the accuracy and robustness of the solutions. The correlation coefficient matrix is adopted to represent the correlations between loads. Simulation results of the two-area, four-machine system prove that the proposed method is an efficient and robust sampling method. Simulation results of the 16-machine, 68-bus test system indicate that load correlation has a significant impact on the probabilistic analysis result of the critical oscillation mode under a certain degree of load uncertainty.

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“…flow using the method of combined cumulants and Gram -Charlier expansion handles the uncertainty of load variation, which can be a quick screening tool to determine the major investment on improving transmission system inadequacy [13]. The probabilistic eigenvalue algorithm was successfully developed to analyse system smalldisturbance stability [14,15] to design and optimise parameters of power system stabilisers [16 -19].…”

Section: Introductionmentioning

confidence: 99%

Voltage stability analysis considering the uncertainties of dynamic load parameters

Zhang

Tse

Wang

et al. 2009

IET Generation, Transmission &Amp; Distribution

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Many uncertainties exist in power systems and they will affect the stability analysis results. Voltage stability considering uncertainty in load parameters will be discussed. With the assumption that parameter variation is normal distribution, the probabilistic characteristics of eigenvalues under the uncertainties of dynamic load parameters can be obtained. Distribution of the critical eigenvalue will determine the stability probability of a power system. The stability margin can be inferred from the probabilistic critical load level, which is the maximal load level where system is 'probabilistically' stable. Case studies on three test systems illustrate that the stability margin will be reduced with load uncertainty. The proposed probabilistic results are validated using deterministic method of Monte Carlo on multi 10 000 sample studies.

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Improved probabilistic method for power system dynamic stability studies

Cited by 55 publications

References 4 publications

Determination and enhancement of probabilistic stability margin with load uncertainties

Determination and enhancement of probabilistic stability margin with load uncertainties

Latin Hypercube Sampling Based Probabilistic Small Signal Stability Analysis Considering Load Correlation

Voltage stability analysis considering the uncertainties of dynamic load parameters

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