Academic literature on the topic 'WLS STATE ESTIMATION'

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Journal articles on the topic "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) 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

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 weighted least squares (GSP-WLS) state estimator is developed by utilizing the state smoothness. In addition, a sensor placement strategy that aims to optimize the estimation performance of the GSP-WLS estimator is proposed. Simulation results on the IEEE 118-bus system show that the GSP methods reduce the estimation error magnitude by up to two orders of magnitude compared to existing methods, using only 70 sampled buses, and increase of up to 30% in the probability of bad data detection for the same probability of false alarms in unobservable systems The results conclude that the proposed methods enable an accurate state estimation, even when the system is unobservable, and significantly reduce the required measurement sensors.
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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 site of PMU, and can be maximized by choosing the installation site appropriately.
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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 specifically weighted least square (WLS)-based loss function for training. To demonstrate the performance of the proposed state estimator, a comparison study was done with other SE methods included in this paper. The results showed that the proposed state estimator had high accuracy in estimating the states of DC microgrids. Other than the enhanced accuracy, the deep-learning-based state estimator also provided faster computation speeds than the conventional state estimator.
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