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Journal articles on the topic 'Robust state estimation'

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Published: 4 June 2021

Last updated: 8 June 2024

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1

Khan, Zahid, Katrina Lane Krebs, Sarfaraz Ahmad, and Misbah Munawar. "POWER SYSTEM STATE ESTIMATION USING A ROBUST ESTIMATOR." NED University Journal of Research XVI, no. 4 (August 30, 2019): 53–65. http://dx.doi.org/10.35453/nedjr-ascn-2018-0038.

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State estimation (SE) is a primary data processing algorithm which is utilised by the control centres of advanced power systems. The most generally utilised state estimator is based on the weighted least squares (WLS) approach which is ineffective in addressing gross errors of input data of state estimator. This paper presents an innovative robust estimator for SE environments to overcome the non-robustness of the WLS estimator. The suggested approach not only includes the similar functioning of the customary loss function of WLS but also reflects loss function built on the modified WLS (MWLS) estimator. The performance of the proposed estimator was assessed based on its ability to decrease the impacts of gross errors on the estimation results. The properties of the suggested state estimator were investigated and robustness of the estimator was studied considering the influence function. The effectiveness of the proposed estimator was demonstrated with the help of examples which also indicated non-robustness of MWLS estimator in SE algorithm.

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2

De Palma, Daniela, and Giovanni Indiveri. "Output outlier robust state estimation." International Journal of Adaptive Control and Signal Processing 31, no. 4 (February 9, 2016): 581–607. http://dx.doi.org/10.1002/acs.2673.

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3

Zhang, Zhenglei, Jirong Wang, Junwei Gao, and Huabo Liu. "Robust State Estimation for T–S Fuzzy Markov Jump Systems." Mathematics 11, no. 2 (January 16, 2023): 487. http://dx.doi.org/10.3390/math11020487.

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The problem of robust state estimation for a class of uncertain nonlinear systems with Markov jump is investigated. The uncertain nonlinear system under consideration is represented by the Takagi–Sugeno (T–S) fuzzy model because it is difficult to describe. Firstly, different from the traditional T–S fuzzy modeling method, the deviation of the linear system approaching a nonlinear system is considered, which is represented as a model error in system modeling. Secondly, through a robust state estimation method based on the sensitivity penalty, we develop a robust state estimator for linear subsystems, and the fuzzy robust state estimator is obtained by fuzzy rules. Thirdly, the stability and boundedness of the fuzzy robust state estimator are proved under the assumption conditions to ensure the reliability of the obtained estimator. Finally, some numerical examples are given to verify the effectiveness of the fuzzy robust state estimator.

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4

Wang, Min, and Huabo Liu. "Event-Triggered Robust State Estimation for Nonlinear Networked Systems with Measurement Delays against DoS Attacks." Sensors 23, no. 14 (July 20, 2023): 6553. http://dx.doi.org/10.3390/s23146553.

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In this paper, we focus on the event-triggered robust state estimation problems for nonlinear networked systems with constant measurement delays against denial-of-service (DoS) attacks. The computation of the extended Kalman filter (EKF) generates errors of linearization approximations, which can result in increased state estimation errors, and subsequently amplifies the linearization errors. DoS attacks interfere with the transmission of measurements sent to the remote robust state estimator by overloading the communication networks, while the communication rate of the communication channel is constrained. Therefore, an event-triggered robust state estimation algorithm based on sensitivity penalization with an explicit packet arrival parameter is derived to defend against DoS attacks and linearization errors. Meanwhile, the presence of measurement delays precludes the direct use of conventional state estimation algorithms, prompting us to devise an innovative state augmentation method. The results of the numerical simulations show that the proposed robust state estimator can appreciably improve the accuracy of state estimation.

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5

Chen, Yung Yue, Shyang Jye Chang, Sheng Chih Shen, and Yung Hsiang Chen. "Robust Estimation Design of a Class of Systems with Noise Coupling Input Saturation." Materials Science Forum 594 (August 2008): 494–99. http://dx.doi.org/10.4028/www.scientific.net/msf.594.494.

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An investigation about the robust estimation of a class of systems with noise coupling input saturation is presented in this study. In general, the existed estimation algorithm is based on the exactly known of the input saturation, but in fact, this is not always true in some practical cases because of the coupling of input saturations and input noises. For treating these kinds of coupling problems in the state estimations of systems, in this study, one fuzzy-based robust estimation algorithm is proposed. The proposed robust estimator includes two parts: firstly, a regression form fuzzy system that is adopted to approximate the unknown input saturation, and then, a robust estimator that combines the above fuzzy system by robust filter design concept for eliminating the effects of noises and modeling uncertainties is proposed. This combination of fuzzy approach and robust filtering technologies successfully offers one a more simple and practical method for treating the estimation problem of a class of systems that input saturations and input noises couple together.

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6

Graham, Matthew C., Jonathan P. How, and Donald E. Gustafson. "Robust State Estimation with Sparse Outliers." Journal of Guidance, Control, and Dynamics 38, no. 7 (July 2015): 1229–40. http://dx.doi.org/10.2514/1.g000350.

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7

Garimella, S. S., and K. Srinivasan. "Robust State Estimation for Linear Systems." Journal of Dynamic Systems, Measurement, and Control 115, no. 1 (March 1, 1993): 193–96. http://dx.doi.org/10.1115/1.2897397.

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Real-time state estimation of a linear dynamic system using an observer, in the presence of modeling errors in the system model used by the observer and uncertainty in the initial system states, is considered here. A guideline for designing observers for multioutput systems is established, based on an expression for an upper bound on the norm of the state estimation error derived in this paper. An example is presented to illustrate the usefulness of this guideline.

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8

Irving, M. R. "Robust Algorithm for Generalized State Estimation." IEEE Transactions on Power Systems 24, no. 4 (November 2009): 1886–87. http://dx.doi.org/10.1109/tpwrs.2009.2030116.

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9

Kekatos, Vassilis, and Georgios B. Giannakis. "Distributed Robust Power System State Estimation." IEEE Transactions on Power Systems 28, no. 2 (May 2013): 1617–26. http://dx.doi.org/10.1109/tpwrs.2012.2219629.

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10

Alvarez, Jesús. "Nonlinear state estimation with robust convergence." Journal of Process Control 10, no. 1 (February 2000): 59–71. http://dx.doi.org/10.1016/s0959-1524(99)00018-9.

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11

Singh, Nitin K., and Abhisek K. Behera. "Robust state estimation via twisting observer." Automatica 160 (February 2024): 111433. http://dx.doi.org/10.1016/j.automatica.2023.111433.

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12

Aljabrine, Abdulwahab A., Abdallah A. Smadi, Yacine Chakhchoukh, Brian K. Johnson, and Hangtian Lei. "Resiliency Improvement of an AC/DC Power Grid with Embedded LCC-HVDC Using Robust Power System State Estimation." Energies 14, no. 23 (November 23, 2021): 7847. http://dx.doi.org/10.3390/en14237847.

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The growth of renewable energy generation in the power grid brings attention to high-voltage direct current (HVDC) transmission as a valuable solution for stabilizing the system. Robust hybrid power system state estimation could enhance the resilience of the control of these systems. This paper proposes a two-stage, highly robust least-trimmed squares (LTS)-based estimator. The first step combines the supervisory control and data acquisition (SCADA) measurements using the robust LTS-based estimator. The second step merges the obtained state results with the available phasor measurement units (PMUs) measurements using a robust Huber M-estimator. The proposed robust LTS-based estimator shows good performance in the presence of Gaussian measurement noise. The proposed estimator is shown to resist and correct the effect of false data injection (FDI) attacks and random errors on the measurement vector and the Jacobian matrix. The state estimation (SE) is executed on a modified version of the CIGRE bipole LCC-HVDC benchmark model integrated into the IEEE 12-bus AC dynamic test system. The obtained simulation results confirm the effectiveness and robustness of the proposed two-stage LTS-based SE.

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13

Shi, Yu, Yueting Hou, Yue Yu, Zhaoyang Jin, and Mohamed A. Mohamed. "Robust Power System State Estimation Method Based on Generalized M-Estimation of Optimized Parameters Based on Sampling." Sustainability 15, no. 3 (January 31, 2023): 2550. http://dx.doi.org/10.3390/su15032550.

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Robustness is an important performance index of power system state estimation, which is defined as the estimator’s capability to resist the interference. However, improving the robustness of state estimation often reduces the estimation accuracy. To solve this problem, this paper proposes a power system state estimation method for generalized M-estimation of optimized parameters based on sampling. Compared with the traditional robust state estimator, the generalized M-estimator based on projection statistics improves the robustness of state estimation, and the proposed optimized parameter determination method improves the overall accuracy of state estimation by appropriately adjusting its robustness. Considering different degrees of non-Gaussian distributed measurement noises and bad data, the estimation accuracy the proposed method is demonstrated to be up to 23% higher than the traditional generalized M-estimator through MATLAB simulations in IEEE 14, 118 bus test systems, and Polish 2736 bus system.

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14

Li, Jing, Ya Di Luo, Yan Sheng Lang, Cheng Long Dou, Yu Zou, Zi Ming Guo, Dong Sheng Wang, and Xin Peng Li. "Research of Fine and Robust State Estimation." Advanced Materials Research 1008-1009 (August 2014): 202–6. http://dx.doi.org/10.4028/www.scientific.net/amr.1008-1009.202.

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According to the characteristics of the wind farm measuration, this paper presents a Fine and Robust State Estimation method for solving residual contamination problem caused by large-scale wind power integration. This method introduces the reference value of measurement type into the weight function and uses the pre-check information of state estimation measurement to do SCADA measurement pretreatment, and then form the bad data reference factor to assist positioning the bad data of measurement. Finally, the simulation tests of a regional power grid to prove that the proposed method can effectively identify telemetry bad data of wind farms eliminate residual pollution caused by it, which improve the accuracy of the State Estimation.

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15

Wu, Qin Fan, Qing Li, and Nong Cheng. "Visual Odometry and 3D Mapping in Indoor Environments." Applied Mechanics and Materials 336-338 (July 2013): 348–54. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.348.

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This paper presents a robust state estimation and 3D environment modeling approach that enables Micro Aerial Vehicle (MAV) operating in challenging GPS-denied indoor environments. A fast, accurate and robust approach to visual odometry is developed based on Microsoft Kinect. Discriminative features are extracted from RGB images and matched across consecutive frames. A robust least-square estimator is applied to get relative motion estimation. All computation is performed in real-time, which provides high frequency of 6 degree-of-freedom state estimation. A detailed 3D map of an indoor environment is also constructed.

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16

UOSAKI, Katsuji, and Yoshihiro KOKETSU. "Robust State Estimation for Nonlinear Dynamical Systems." Transactions of the Society of Instrument and Control Engineers 26, no. 11 (1990): 1259–66. http://dx.doi.org/10.9746/sicetr1965.26.1259.

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17

Li, Keyi, and Gongjian Zhou. "Robust state estimation with trajectory shape constraints." IET Radar, Sonar & Navigation 14, no. 7 (July 2020): 1046–54. http://dx.doi.org/10.1049/iet-rsn.2019.0556.

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18

Mili, L., M. G. Cheniae, and P. J. Rousseeuw. "Robust state estimation of electric power systems." IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications 41, no. 5 (May 1994): 349–58. http://dx.doi.org/10.1109/81.296336.

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19

Watson, Ryan M., Jason N. Gross, Clark N. Taylor, and Robert C. Leishman. "Robust Incremental State Estimation Through Covariance Adaptation." IEEE Robotics and Automation Letters 5, no. 2 (April 2020): 3737–44. http://dx.doi.org/10.1109/lra.2020.2979655.

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20

Stepinski, T. "Practical Realization of Robust State Estimation Schemes." IFAC Proceedings Volumes 18, no. 5 (July 1985): 1179–83. http://dx.doi.org/10.1016/s1474-6670(17)60722-5.

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21

Castillo, E., A. J. Conejo, R. E. Pruneda, C. Solares, and J. M. Menendez. "$m-k$ Robust Observability in State Estimation." IEEE Transactions on Power Systems 23, no. 2 (May 2008): 296–305. http://dx.doi.org/10.1109/tpwrs.2008.920707.

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22

Irving, M. R. "Robust State Estimation Using Mixed Integer Programming." IEEE Transactions on Power Systems 23, no. 3 (August 2008): 1519–20. http://dx.doi.org/10.1109/tpwrs.2008.926721.

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23

Chuang, Chia-Hua, and Chun-Liang Lin. "On Robust State Estimation of Gene Networks." Biomedical Engineering and Computational Biology 2 (January 2010): 117959721000200. http://dx.doi.org/10.1177/117959721000200001.

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Gene networks in biological systems are not only nonlinear but also stochastic due to noise corruption. How to accurately estimate the internal states of the noisy gene networks is an attractive issue to researchers. However, the internal states of biological systems are mostly inaccessible by direct measurement. This paper intends to develop a robust extended Kalman filter for state and parameter estimation of a class of gene network systems with uncertain process noises. Quantitative analysis of the estimation performance is conducted and some representative examples are provided for demonstration.

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24

Hua, Junhao, and Chunguang Li. "Distributed Robust Bayesian Filtering for State Estimation." IEEE Transactions on Signal and Information Processing over Networks 5, no. 3 (September 2019): 428–41. http://dx.doi.org/10.1109/tsipn.2018.2889579.

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25

Yousfi, B., T. Raïssi, M. Amairi, D. Gucik-Derigny, and M. Aoun. "Robust state estimation for singularly perturbed systems." International Journal of Control 90, no. 3 (July 27, 2016): 566–79. http://dx.doi.org/10.1080/00207179.2016.1186842.

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26

Crevits, Ruben, and Christophe Croux. "Robust estimation of linear state space models." Communications in Statistics - Simulation and Computation 48, no. 6 (February 21, 2018): 1694–705. http://dx.doi.org/10.1080/03610918.2017.1422752.

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27

Alvarez, Jesús, and Teresa López. "Robust dynamic state estimation of nonlinear plants." AIChE Journal 45, no. 1 (January 1999): 107–23. http://dx.doi.org/10.1002/aic.690450110.

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28

Gao, Chao, Guorong Zhao, Jianhua Lu, and Shuang Pan. "Decentralized state estimation for networked spatial-navigation systems with mixed time-delays and quantized complementary measurements: The moving horizon case." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 232, no. 11 (June 8, 2017): 2160–77. http://dx.doi.org/10.1177/0954410017712277.

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In this paper, the navigational state estimation problem is investigated for a class of networked spatial-navigation systems with quantization effects, mixed time-delays, and network-based observations (i.e. complementary measurements and regional estimations). A decentralized moving horizon estimation approach, featuring complementary reorganization and recursive procedure, is proposed to tackle this problem. First, through the proposed reorganized scheme, a random delayed system with complementary observations is reconstructed into an equivalent delay-free one without dimensional augment. Second, with this equivalent system, a robust moving horizon estimation scheme is presented as a uniform estimator for the navigational states. Third, for the demand of real-time estimate, the recursive form of decentralized moving horizon estimation approach is developed. Furthermore, a collective estimation is obtained through the weighted fusion of two parts, i.e. complementary measurements based estimation, and regional estimations directly from the neighbors. The convergence properties of the proposed estimator are also studied. The obtained stability condition implicitly establishes a relation between the upper bound of the estimation error and two parameters, i.e. quantization density and delay occur probability. Finally, an application example to networked unmanned aerial vehicles is presented and comparative simulations demonstrate the main features of the proposed method.

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29

Kong, Xiangyu, Ying Chen, Tao Xu, Chengshan Wang, Chengsi Yong, Peng Li, and Li Yu. "A Hybrid State Estimator Based on SCADA and PMU Measurements for Medium Voltage Distribution System." Applied Sciences 8, no. 9 (September 1, 2018): 1527. http://dx.doi.org/10.3390/app8091527.

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With the increasing importance of renewable energy and flexible loads, the operation of the distribution system is becoming more stochastic and complex, and it is necessary to monitor the power system in real-time. Considering the gradual applications of intelligent electronic devices in the distribution systems, a hybrid state estimator based on supervisory control and data acquisition (SCADA) and phasor measurement unit (PMU) measurements is proposed in this paper, which consists of the improved robust estimation and linear state estimation. At the time of SCADA data acquisition, the improved robust estimation combining the SCADA measurements with PMU measurements is performed. To eliminate the effect of bad data, the internal student residual method is introduced, and the robust thresholds are adjusted adaptively. Then the linear state estimation is performed at the time of PMU data acquisition based on the results of the previous estimation time and the PMU measurements, which can quickly correct the robust estimation results and track the changes of the distribution system. Finally, the effectiveness and performance of the proposed method are verified in a modified IEEE 33-bus distribution system and a real distribution system in China.

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30

Zhao, Junbo, Gexiang Zhang, Zhao Yang Dong, and Massimo La Scala. "Robust Forecasting Aided Power System State Estimation Considering State Correlations." IEEE Transactions on Smart Grid 9, no. 4 (July 2018): 2658–66. http://dx.doi.org/10.1109/tsg.2016.2615473.

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31

Guerrier, Stephane, Roberto Molinari, and Maria-Pia Victoria-Feser. "Estimation of Time Series Models via Robust Wavelet Variance." Austrian Journal of Statistics 43, no. 4 (June 13, 2014): 267–77. http://dx.doi.org/10.17713/ajs.v43i4.45.

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A robust approach to the estimation of time series models is proposed. Taking froma new estimation method called the Generalized Method of Wavelet Moments (GMWM)which is an indirect method based on the Wavelet Variance (WV), we replace the classicalestimator of the WV with a recently proposed robust M-estimator to obtain a robustversion of the GMWM. The simulation results show that the proposed approach can beconsidered as a valid robust approach to the estimation of time series and state-spacemodels.

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32

Wang, Chun, Minghao Geng, Qingshan Xu, and Haixiang Zang. "A Fast State Estimator for Integrated Electrical and Heating Networks." Energies 13, no. 17 (August 31, 2020): 4488. http://dx.doi.org/10.3390/en13174488.

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Integrated electrical and heating networks (IEHNs) effectively improve energy utilization efficiency, reduce environmental pollution and realize sustainable development of energy. To realize the accurate, comprehensive and fast perception of the integrated electrical and heating networks, it is necessary to build a state estimation model. However, the robust state estimator of IEHNs based on the temperature drop equation, flow balance equation and power balance equation still have the problems of convergence and low computational efficiency. In this paper, a fast state estimation method based on weighted least absolute value is proposed, which makes partition calculation of ring-shaped heating network and radiant heating network under certain assumptions. Simulation results show that the method improves the efficiency of the robust state estimator on the premise of high accuracy.

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33

Mahmoud, Magdi S. "Robust stability and ℋ∞-estimation for uncertain discrete systems with state-delay." Mathematical Problems in Engineering 7, no. 5 (2001): 393–412. http://dx.doi.org/10.1155/s1024123x01001703.

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In this paper, we investigate the problems of robust stability and ℋ∞-estimation for a class of linear discrete-time systems with time-varying norm-bounded parameter uncertainty and unknown state-delay. We provide complete results for robust stability with prescribed performance measure and establish a version of the discrete Bounded Real Lemma. Then, we design a linear estimator such that the estimation error dynamics is robustly stable with a guaranteed ℋ∞-performance irrespective of the parameteric uncertainties and unknown state delays. A numerical example is worked out to illustrate the developed theory.

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34

Nugroho, Sebastian A., Ahmad F. Taha, and Junjian Qi. "Robust Dynamic State Estimation of Synchronous Machines With Asymptotic State Estimation Error Performance Guarantees." IEEE Transactions on Power Systems 35, no. 3 (May 2020): 1923–35. http://dx.doi.org/10.1109/tpwrs.2019.2949977.

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35

Antončič, Papič, and Blažič. "Robust and Fast State Estimation for Poorly-Observable Low Voltage Distribution Networks Based on the Kalman Filter Algorithm." Energies 12, no. 23 (November 22, 2019): 4457. http://dx.doi.org/10.3390/en12234457.

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This paper presents a novel approach for the state estimation of poorly-observable low voltage distribution networks, characterized by intermittent and erroneous measurements. The developed state estimation algorithm is based on the Extended Kalman filter, where we have modified the execution of the filtering process. Namely, we have fixed the Kalman gain and Jacobian matrices to constant matrices; their values change only after a larger disturbance in the network. This allows for a fast and robust estimation of the network state. The performance of the proposed state-estimation algorithm is validated by means of simulations of an actual low-voltage network with actual field measurement data. Two different cases are presented. The results of the developed state estimator are compared to a classical estimator based on the weighted least squares method. The comparison shows that the developed state estimator outperforms the classical one in terms of calculation speed and, in case of spurious measurements errors, also in terms of accuracy.

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36

Kadir, Dler H., Alan G. Rahim, Dashty I. Jamil, and Dlshad M. Saleh. "Proposed Capability Indices Based on Robust Estimation Compared with Classical Capability Indices." Cihan University-Erbil Scientific Journal 7, no. 2 (November 10, 2023): 49–53. http://dx.doi.org/10.24086/cuesj.v7n2y2023.pp49-53.

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A process capability study is a scientific and systematic procedure that uses control charts to detect and eliminate the unnatural causes of variation until a state of statistical control is reached. On the other hand, in order to meet the quality requirements of the final product, quality should be achieved at every stage of production. Another way of achieving good quality during production is to use statistical techniques at every stage of production. The purpose of this research is to apply it to process capacity indices in replace of the standard deviation estimator. The information, which is taken from the Coca-Cola/Erbil production process, illustrates the qualities of the beverage (750 ml). A Coca-Cola product's 100 observations are divided into 25 models. Employed both the standard deviation estimator-based and the robust Downton estimation-based process capability indices. It was determined that in this inquiry, the robust Downton estimation had better qualities than the standard division estimator because the robust Downton estimation process capacity index values were greater.

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37

Liu, Jie, Wenqian Dong, Qingqing Zhou, and Dong Li. "Fauce." Proceedings of the VLDB Endowment 14, no. 11 (July 2021): 1950–63. http://dx.doi.org/10.14778/3476249.3476254.

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Cardinality estimation is a fundamental and critical problem in databases. Recently, many estimators based on deep learning have been proposed to solve this problem and they have achieved promising results. However, these estimators struggle to provide accurate results for complex queries, due to not capturing real inter-column and inter-table correlations. Furthermore, none of these estimators contain the uncertainty information about their estimations. In this paper, we present a join cardinality estimator called Fauce. Fauce learns the correlations across all columns and all tables in the database. It also contains the uncertainty information of each estimation. Among all studied learned estimators, our results are promising: (1) Fauce is a light-weight estimator, it has 10× faster inference speed than the state of the art estimator; (2) Fauce is robust to the complex queries, it provides 1.3×--6.7× smaller estimation errors for complex queries compared with the state of the art estimator; (3) To the best of our knowledge, Fauce is the first estimator that incorporates uncertainty information for cardinality estimation into a deep learning model.

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38

Vieyra, Natanael, Jesús Álvarez, and Paul Maya-Ortiz. "Decentralized robust state estimation of multimachine power systems." International Journal of Electrical Power & Energy Systems 135 (February 2022): 107469. http://dx.doi.org/10.1016/j.ijepes.2021.107469.

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39

Wang, Yu, Xiaogang Wang, and Naigang Cui. "Robust decentralised state estimation for formation flying spacecraft." IET Radar, Sonar & Navigation 13, no. 5 (May 2019): 814–23. http://dx.doi.org/10.1049/iet-rsn.2018.5348.

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40

Korres, G. N. "A Robust Method for Equality Constrained State Estimation." IEEE Power Engineering Review 21, no. 12 (December 2001): 69. http://dx.doi.org/10.1109/mper.2001.4311235.

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41

Kanev, Stoyan, and Michel Verhaegen. "RECONFIGURABLE ROBUST FAULT-TOLERANT CONTROL AND STATE ESTIMATION." IFAC Proceedings Volumes 35, no. 1 (2002): 95–100. http://dx.doi.org/10.3182/20020721-6-es-1901.00747.

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42

Smith, M. M., R. S. Powell, M. R. Irving, and M. J. H. Sterling. "Robust algorithm for state estimation in electrical networks." IEE Proceedings C Generation, Transmission and Distribution 138, no. 4 (1991): 283. http://dx.doi.org/10.1049/ip-c.1991.0035.

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43

Santhosh Kumar, C., Ketan Rajawat, Saikat Chakrabarti, and Bikash C. Pal. "Robust distribution system state estimation with hybrid measurements." IET Generation, Transmission & Distribution 14, no. 16 (August 21, 2020): 3250–59. http://dx.doi.org/10.1049/iet-gtd.2020.0260.

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44

Weng, Yang, Rohit Negi, Christos Faloutsos, and Marija D. Ilic. "Robust Data-Driven State Estimation for Smart Grid." IEEE Transactions on Smart Grid 8, no. 4 (July 2017): 1956–67. http://dx.doi.org/10.1109/tsg.2015.2512925.

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45

Zhou, Tong. "Robust Recursive State Estimation With Random Measurement Droppings." IEEE Transactions on Automatic Control 61, no. 1 (January 2016): 156–71. http://dx.doi.org/10.1109/tac.2015.2437524.

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46

Chen, Yanbo, Feng Liu, Shengwei Mei, and Jin Ma. "A Robust WLAV State Estimation Using Optimal Transformations." IEEE Transactions on Power Systems 30, no. 4 (July 2015): 2190–91. http://dx.doi.org/10.1109/tpwrs.2014.2358452.

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47

Zambare, N. "A method of robust multi-rate state estimation." Journal of Process Control 13, no. 4 (June 2003): 337–55. http://dx.doi.org/10.1016/s0959-1524(02)00027-6.

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48

Karlgaard, Christopher D., and Haijun Shen. "Robust state estimation using desensitized Divided Difference Filter." ISA Transactions 52, no. 5 (September 2013): 629–37. http://dx.doi.org/10.1016/j.isatra.2013.04.009.

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Korres, G. N. "A robust method for equality constrained state estimation." IEEE Transactions on Power Systems 17, no. 2 (May 2002): 305–14. http://dx.doi.org/10.1109/tpwrs.2002.1007897.

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Yao, Yiyun, Xuan Liu, and Zuyi Li. "Robust Measurement Placement for Distribution System State Estimation." IEEE Transactions on Sustainable Energy 10, no. 1 (January 2019): 364–74. http://dx.doi.org/10.1109/tste.2017.2775862.

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