An interval arithmetic-based state estimation for unbalanced active distribution networks

Xu, Junjun; Wu, Zheng-Mao; Dou, Xiaobo; Hu, Qinran

doi:10.1109/pesgm.2017.8274035

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…We also simulate the integrated electricity-gas system to calculate the established state estimation model in MATLAB/Simulink (2021 version). MATLAB is an effective tool for simulating hybrid energy systems [27], which can, in combination with Matpower [28,29], be used to test and calculate the power flow of the power system. The widespread use of MATLAB in various energy system situations sufficiently demonstrates its effectiveness.…”

Section: Case Studiesmentioning

confidence: 99%

Interval State Estimation of Electricity-Gas Systems Considering Measurement Correlations

Huang,

Feng

2024

Energies

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The popularization of electricity-gas systems leads to increasing demand for state management of systems. However, the existence of neglected measurement correlations brings uncertainties to the electricity-gas systems state estimation. In this paper, an interval state estimation method that considers measurement correlations existing in the electricity-gas systems is presented. We derive the linear measurement model for the electricity-gas systems through Taylor series expansion and estimate the measurement variance-covariance matrix with measurement correlations. The system parameter matrix and the measurement variance-covariance matrix containing measurement correlations are combined into an interval, and the interval state matrix considering measurement correlations is constructed. Then, the linear equations for the state estimation interval considering measurement correlations are established based on the measurement containing correlations and interval state matrix; as a result, the electricity-gas system state estimation model containing measurement correlations is established. In addition, a method for determining the range of state estimation intervals is proposed. Numerical tests on an integrated electricity-gas system comprising a 10-node natural gas network and IEEE 30-bus system indicate that the proposed approach has more advantages over the UT+KO approach in computation accuracy and computation efficiency.

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Section: Case Studiesmentioning

confidence: 99%

Interval State Estimation of Electricity-Gas Systems Considering Measurement Correlations

Huang,

Feng

2024

Energies

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“…Let f (x) = 0 be a system of n non-linear equations of variables. The KM algorithm is defined by K(y, X) = y − Y f (y) + (I − YF′(X))(X − y) (27) where y is chosen from the regions of X and Y is an arbitrary nonsingular matrix, F′(X) is the monotonic interval evaluation of f ′(x). In particular, y and Y are chosen to be y = m(X) and Y = [m(F′(X))] −1 .…”

Section: Km Algorithmmentioning

confidence: 99%

“…In the field of interval state estimation, there are two interval methods as representative: iterative Krawczyk-operator algorithm [25,26] and linear optimisation model [27]. Iterative Krawczykoperator algorithm performs interval linear state estimation based on phasor measurement unit (PMU) and supervisory control and data acquisition hybrid measurements.…”

Section: Introductionmentioning

confidence: 99%

Revised constraint‐propagation method for distribution interval state estimation

Ngo

Lou

2020

IET Generation, Transmission & Distribution

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Uncertainty Modeling of Distributed Energy Resources: Techniques and Challenges

Zhang

Wang

2019

Curr Sustainable Renewable Energy Rep

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An interval arithmetic-based state estimation for unbalanced active distribution networks

Cited by 7 publications

References 15 publications

Interval State Estimation of Electricity-Gas Systems Considering Measurement Correlations

Interval State Estimation of Electricity-Gas Systems Considering Measurement Correlations

Revised constraint‐propagation method for distribution interval state estimation

Uncertainty Modeling of Distributed Energy Resources: Techniques and Challenges

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