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Gotowe bibliografie tematyczne / WLS STATE ESTIMATION

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Gotowa bibliografia na temat „WLS STATE ESTIMATION”

Autor: Grafiati

Data publikacji: 11 września 2023

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Artykuły w czasopismach na temat "WLS STATE ESTIMATION"

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 (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)

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2

Gomez-Quiles, Catalina, Antonio de la Villa Jaen, and Antonio Gomez-Exposito. "A Factorized Approach to WLS State Estimation." IEEE Transactions on Power Systems 26, no. 3 (2011): 1724–32. http://dx.doi.org/10.1109/tpwrs.2010.2096830.

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3

Chakrabarti, S., and E. Kyriakides. "PMU Measurement Uncertainty Considerations in WLS State Estimation." IEEE Transactions on Power Systems 24, no. 2 (2009): 1062–71. http://dx.doi.org/10.1109/tpwrs.2009.2016295.

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4

Yuan, Chen, Yuqi Zhou, Guangyi Liu, Renchang Dai, Yi Lu, and Zhiwei Wang. "Graph Computing-Based WLS Fast Decoupled State Estimation." IEEE Transactions on Smart Grid 11, no. 3 (2020): 2440–51. http://dx.doi.org/10.1109/tsg.2019.2955695.

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5

Kalpanadevi, M., and R. Neela. "BBO Algorithm for Line Flow Based WLS State Estimation." Materials Today: Proceedings 5, no. 1 (2018): 318–28. http://dx.doi.org/10.1016/j.matpr.2017.11.088.

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6

Zhong, S., and A. Abur. "Auto Tuning of Measurement Weights in WLS State Estimation." IEEE Transactions on Power Systems 19, no. 4 (2004): 2006–13. http://dx.doi.org/10.1109/tpwrs.2004.836182.

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7

Dabush, Lital, Ariel Kroizer, and Tirza Routtenberg. "State Estimation in Partially Observable Power Systems via Graph Signal Processing Tools." Sensors 23, no. 3 (2023): 1387. http://dx.doi.org/10.3390/s23031387.

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This paper considers the problem of estimating the states in an unobservable power system, where the number of measurements is not sufficiently large for conventional state estimation. Existing methods are either based on pseudo-data that is inaccurate or depends on a large amount of data that is unavailable in current systems. This study proposes novel graph signal processing (GSP) methods to overcome the lack of information. To this end, first, the graph smoothness property of the states (i.e., voltages) is validated through empirical and theoretical analysis. Then, the regularized GSP weigh

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8

Kang, Jeong-Won, and Dae-Hyun Choi. "Distributed multi-area WLS state estimation integrating measurements weight update." IET Generation, Transmission & Distribution 11, no. 10 (2017): 2552–61. http://dx.doi.org/10.1049/iet-gtd.2016.1493.

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9

Liu, Min. "Distribution System State Estimation with Phasor Measurement Units." Applied Mechanics and Materials 668-669 (October 2014): 687–90. http://dx.doi.org/10.4028/www.scientific.net/amm.668-669.687.

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With phasor measurement units (PMU) become available in the distribution system; the estimation accuracy of the distribution system state estimation (DSSE) is expected to be improved. Based on the weighted least square (WLS) approach, this paper proposed a new state estimator which takes into account the PMU measurements including voltage magnitude and phasor angle, and load current magnitude and phasor angle. Simulation results indicate that the estimation accuracy is obvious improve by adding PMU measurements to the DSSE. Furthermore, the estimation accuracy changes with the installation sit

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10

Adi, Faya Safirra, Yee Jin Lee, and Hwachang Song. "State Estimation for DC Microgrids using Modified Long Short-Term Memory Networks." Applied Sciences 10, no. 9 (2020): 3028. http://dx.doi.org/10.3390/app10093028.

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The development of state estimators for local electrical energy supply systems is inevitable as the role of the system’s become more important, especially with the recent increased interest in direct current (DC) microgrids. Proper control and monitoring requires a state estimator that can adapt to the new technologies for DC microgrids. This paper mainly deals with the DC microgrid state estimation (SE) using a modified long short-term memory (LSTM) network, which until recently has been applied only in forecasting studies. The modified LSTM network for the proposed state estimator adopted a

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