A mixed-integer linear programming approach for robust state estimation

Chen, Yanbo; Jin, Ma

doi:10.1007/s40565-014-0078-7

Cited by 10 publications

(5 citation statements)

References 32 publications

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“…Finally, it is assumed that voltage, power flow and power injection measurements have a Gaussian distribution and a standard deviation of 0.004, 0.008 and 0.01, respectively, according to the state estimation studies presented in [19,22].…”

Section: Preliminary Considerationsmentioning

confidence: 99%

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State Estimation in Electric Power Systems Using an Approach Based on a Weighted Least Squares Non-Linear Programming Modeling

et al. 2021

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In this work, the state estimation problem of electric power systems is represented through a mathematical programming approach. Initially, a non-linear mathematical model based on the classical method of weighted least squares is proposed to solve the state estimation problem for comparative purposes. Due to the inherent limitations that this classical model presents when dealing with errors in the set of measurements, a new mathematical model is proposed that can be used within an iterative procedure to reduce the impact of measurement errors on the estimated results. Several tests on a didactic 5-bus power system and IEEE benchmark power test systems showed the effectiveness of the proposed approach which achieved better results than the proposed classical state estimation model. The non-linear programming models proposed in this paper are implemented in the mathematical modeling language AMPL. Additionally, to validate the results of the proposed methodologies, the power system operation points are compared with the results obtained using the Matpower simulation package. The results allowed concluding that the proposed mathematical models can be successfully applied to perform state estimation studies in power systems.

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Section: Preliminary Considerationsmentioning

confidence: 99%

“…Accordingly, it is important to represent the state estimation problem through mathematical optimization models. Although this possibility has not been widely explored as shown in [12], recent studies show that various mathematical programming methods have been proposed to solve the state estimation problem with promising results [10,19].…”

Section: Introductionmentioning

confidence: 99%

State Estimation in Electric Power Systems Using an Approach Based on a Weighted Least Squares Non-Linear Programming Modeling

et al. 2021

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“…As such, for distributed solutions, the bad data detection and identification can be performed the same as integrated solutions. If the measurement redundancy is enough, robust state estimators [8,34,35] may be further implemented.…”

Section: Pseudo-measurements For Extended Boundary Busesmentioning

confidence: 99%

Multi-area distributed three-phase state estimation for unbalanced active distribution networks

Chen

Wei

Sun

et al. 2016

J. Mod. Power Syst. Clean Energy

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This paper proposes a new multi-area framework for unbalanced active distribution network (ADN) state estimation. Firstly, an innovative three-phase distributed generator (DG) model is presented to take the asymmetric characteristics of DG three-phase outputs into consideration. Then a feasible method to set pseudo-measurements for unmonitored DGs is introduced. The states of DGs, together with the states of alternating current (AC) buses in ADNs, were estimated by using the weighted least squares (WLS) method. After that, the ADN was divided into several independent subareas. Based on the augmented Lagrangian method, this work proposes a fully distributed three-phase state estimator for the multi-area ADN. Finally, from the simulation results on the modified IEEE 123-bus system, the effectiveness and applicability of the proposed methodology have been investigated and discussed.

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“…LTS considers the sum of squared errors for (m-K) smallest residuals only. Mixed integer linear programming (MILP) of a robust estimator is formulated in [12], which uses CPLEX as the solving tool and it is found to be time efficient for larger systems. Another robust estimator is exhibited in [13], which is designed from the concept of normal measurement rate (NMR) and the theory of uncertainty of measurements.…”

Section: Introductionmentioning

confidence: 99%

Robust power system state estimation by appropriate selection of tolerance for the least measurement rejected algorithm

Shahriar¹,

Habiballah²

2019

Turk J Elec Eng & Comp Sci

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Modern power systems are highly complicated and nonlinear in nature. Accurate estimation of the power system states (voltage-magnitude and phase-angle) is required for the secure operation of the power system. The presence of bad-data measurements in meters has made this estimation process challenging. An efficient estimator should detect and eliminate the effect of bad data during the estimation process. Least measurement rejected (LMR) is a robust estimator that has been found successful in dealing with various categories of bad data. The performance of LMR depends upon the proper selection of a tolerance for each measurement. This paper presents a novel approach for tolerance value selection to improve the capability of handling different single and multiple bad-data scenarios successfully. The performance of this updated LMR (ULMR) is compared with weighted least squares, weighted least absolute value, and two versions of LMR from the literature. IEEE 30-bus and 118-bus systems are used to demonstrate the robustness of the proposed estimator under different bad-measurement (single and multiple) scenarios.

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A mixed-integer linear programming approach for robust state estimation

Cited by 10 publications

References 32 publications

State Estimation in Electric Power Systems Using an Approach Based on a Weighted Least Squares Non-Linear Programming Modeling

State Estimation in Electric Power Systems Using an Approach Based on a Weighted Least Squares Non-Linear Programming Modeling

Multi-area distributed three-phase state estimation for unbalanced active distribution networks

Robust power system state estimation by appropriate selection of tolerance for the least measurement rejected algorithm

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