Guaranteed state estimation of power system via interval constraints propagation

Wang, Bin; He, Guangyu; Liu, Kaicheng; Lv, Hairong; Yin, Wenjun; Mei, Shengwei

doi:10.1049/iet-gtd.2012.0333

Cited by 18 publications

(5 citation statements)

References 39 publications

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“…It is difficult to establish uniform analytical expressions and standard analytical methods (Wang et al, 2013). However, based on the theory of unknown-but-bounded error (UBBE), the original problem can be transformed into two optimization problems containing non-linear interval constraints, where the upper and lower bounds on the variables to be solved are obtained separately (Bargiela et al, 2003).…”

Section: Operating State Uncertainty Evaluationmentioning

confidence: 99%

Improved unscented Kalman filter based interval dynamic state estimation of active distribution network considering uncertainty of photovoltaic and load

Lin²,

Wu³

et al. 2023

Front. Energy Res.

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State estimation of active distribution network (ADN) plays an important role in distribution energy management system. The increase penetration of distributed generations, especially the distributed photovoltaic (PV), in ADN leads to high uncertainty of ADN’s operation and the state of the ADN varies with the variation of the PV output power. For the uncertainty of PV power output, an interval dynamic state estimation (IDSE) method, which estimates the interval of ADN state variables is proposed in this paper. Firstly, considering the slow computation speed of the Unscented Kalman Filter (UKF), the square root UKF is used to predict the real-time operating level of the state variables. Secondly, since the power output of PV has features of the variation randomly, the neural network-based prediction intervals is employed to predict the power output interval of PV. Finally, the normal fluctuation range of ADN state is modelled as a bilevel non-linear programming problem to perform IDSE, which in turn monitors the operating state of ADN. The proposed method is evaluated on the IEEE 33-node and IEEE 123-node systems, respectively. The test results demonstrate that the dynamic status of the ADN can be tracked accurately using the proposed method.

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Section: Operating State Uncertainty Evaluationmentioning

confidence: 99%

Improved unscented Kalman filter based interval dynamic state estimation of active distribution network considering uncertainty of photovoltaic and load

Lin²,

Wu³

et al. 2023

Front. Energy Res.

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show abstract

“…The natural inclusion function, which is the product by directly applying IA to evaluate interval functions, will often result in relatively large overestimation because of the inherent expansion effect of IA. For example, in the problem of

f false(x false) = x^{2} - 2 x + 1

with

false[x false] = false[0.9, 1.1 false]

, the result of

false[f false] false[x false]

using natural inclusion function [33] is

false[- 0.39, 0.41 false]

, whereas the precise result of

false[f false] false[x false]

false[0, 0.01 false]

obtained by calculating the range of

f false(false[- 1, 1 false] false)

. To make the interval width narrower, the high‐order Taylor series expansion of functions are commonly used.…”

Section: Ipf Analysis Based On Taylor Inclusion Functionmentioning

confidence: 99%

Interval method for uncertain power flow analysis based on Taylor inclusion function

Liao

Liu

Zhang

et al. 2017

IET Generation, Transmission & Distribution

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“…Some studies have focused on interval analysis algorithm to replace deterministic grid state estimation [18][19][20][21]. The idea of these methods is to include the true value of the system in an interval as much as possible, so as to provide reference to the system operators.…”

Section: Introductionmentioning

confidence: 99%

“…However, as interval operation does not satisfy the closure or distributive laws, and the interval operations become increasingly larger. Thus, a few researchers have developed contraction operators for state estimation [21] and power flow analysis [22][23][24], among which the interval constraint-propagation (ICP) method is highly efficient. ICP algorithms solve 'constraint satisfaction problems' (CSPs); these are mathematical problems solved by finding states satisfying certain constraints.…”

Section: Introductionmentioning

confidence: 99%

Revised constraint‐propagation method for distribution interval state estimation

Ngo

Lou

2020

IET Generation, Transmission & Distribution

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Guaranteed state estimation of power system via interval constraints propagation

Cited by 18 publications

References 39 publications

Improved unscented Kalman filter based interval dynamic state estimation of active distribution network considering uncertainty of photovoltaic and load

Improved unscented Kalman filter based interval dynamic state estimation of active distribution network considering uncertainty of photovoltaic and load

Interval method for uncertain power flow analysis based on Taylor inclusion function

Revised constraint‐propagation method for distribution interval state estimation

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