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Artykuły w czasopismach na temat „WLS STATE ESTIMATION”

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Data publikacji: 11 września 2023

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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 (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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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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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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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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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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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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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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11

Mohmadishak Sheikh, Chetan Sheth. "System State Estimation Using Weighted Least Square Method." Proceeding International Conference on Science and Engineering 11, no. 1 (2023): 1294–99. http://dx.doi.org/10.52783/cienceng.v11i1.276.

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State estimation is an essential part of every energy control management system. Accurate estimation of state or operating state is essential for security control and monitoring of power systems. Power system state estimation is a procedure to estimate true state from the inexact state of a power system. The conventional state estimator provides estimates of the power system states, i.e., bus voltages and angles which is obtained. State estimation is a computational technique for electrical power system. It empowers the calculation of the power flows of the electrical power system which are no

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12

Chen, Jiaxiong, and Yuan Liao. "Investigation of WLS state estimation convergence under topology errors and load increment." International Journal of Automation and Logistics 1, no. 1 (2013): 47. http://dx.doi.org/10.1504/ijal.2013.057452.

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13

MAJDOUB, Meriem. "Performance Evaluation of Two Simplified Algorithms of WLS Power System State Estimation." PRZEGLĄD ELEKTROTECHNICZNY 1, no. 12 (2018): 20–25. http://dx.doi.org/10.15199/48.2018.12.05.

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14

Kim, Jonghoek, and Sungyun Choi. "Robust and efficient WLS-based dynamic state estimation considering transformer core saturation." Journal of the Franklin Institute 357, no. 17 (2020): 12938–59. http://dx.doi.org/10.1016/j.jfranklin.2020.08.012.

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15

Chetan Sheth, Mohmadishak Sheikh,. "Power System State Estimation using Weighted Least Square Method." Proceeding International Conference on Science and Engineering 11, no. 1 (2023): 1721–27. http://dx.doi.org/10.52783/cienceng.v11i1.327.

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State estimation is an essential part of every energy control management system. Accurate estimation of state or operating state is essential for security control and monitoring of power systems. Power system state estimation is a procedure to estimate true state from the inexact state of a power system. The conventional state estimator provides estimates of the power system states, i.e., bus voltages and angles which is obtained. State estimation is a computational technique for electrical power system. It empowers the calculation of the power flows of the electrical power system which are no

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16

Duan, Jiandong, Peng Wang, Wentao Ma, Xinyu Qiu, Xuan Tian, and Shuai Fang. "State of Charge Estimation of Lithium Battery Based on Improved Correntropy Extended Kalman Filter." Energies 13, no. 16 (2020): 4197. http://dx.doi.org/10.3390/en13164197.

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State of charge (SOC) estimation plays a crucial role in battery management systems. Among all the existing SOC estimation approaches, the model-driven extended Kalman filter (EKF) has been widely utilized to estimate SOC due to its simple implementation and nonlinear property. However, the traditional EKF derived from the mean square error (MSE) loss is sensitive to non-Gaussian noise which especially exists in practice, thus the SOC estimation based on the traditional EKF may result in undesirable performance. Hence, a novel robust EKF method with correntropy loss is employed to perform SOC

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17

Kim, Doyun, Justin Migo Dolot, and Hwachang Song. "Distribution System State Estimation Using Model-Optimized Neural Networks." Applied Sciences 12, no. 4 (2022): 2073. http://dx.doi.org/10.3390/app12042073.

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Maintaining reliability during power system operation relies heavily on the operator’s knowledge of the system and its current state. With the increasing complexity of power systems, full system monitoring is needed. Due to the costs to install and maintain measurement devices, a cost-effective optimal placement is normally employed, and as such, state estimation is used to complete the picture. However, in order to provide accurate state estimates in the current power system climate, the models must be fully expanded to include probabilistic uncertainties and non-linear assets. Recognizing it

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18

Farhat, I. A. "An Improved Power System State Estimation Using A Dynamically Adapted JAYA Algorithm." مجلة الجامعة الأسمرية: العلوم التطبيقية 7, no. 4 (2022): 136–44. http://dx.doi.org/10.59743/aujas.v7i4.1527.

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State estimation is a central issue for power systems monitoring and control. Applying state estimation schemes ensures the accuracy of system real-time monitoring process. Due to the high nonlinearities and non-smoothness of the dynamic behavior of power systems, state estimation is getting more importance to lessen the error margins. In order to find the best estimate of the various variables of a power system, optimization-based and statistical techniques are applied. Classically, common metering devices are used to measure power system variables. Nevertheless, these devices are associated

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19

Jiang, Sicheng, Shiwei Li, Hongbin Wu, Yuting Hua, Bin Xu, and Ming Ding. "Distributed state estimation method based on WLS-AKF hybrid algorithm for active distribution networks." International Journal of Electrical Power & Energy Systems 145 (February 2023): 108732. http://dx.doi.org/10.1016/j.ijepes.2022.108732.

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Haseeb, Abdul, Umar Waleed, Muhammad Mansoor Ashraf, Faisal Siddiq, Muhammad Rafiq, and Muhammad Shafique. "Hybrid Weighted Least Square Multi-Verse Optimizer (WLS–MVO) Framework for Real-Time Estimation of Harmonics in Non-Linear Loads." Energies 16, no. 2 (2023): 609. http://dx.doi.org/10.3390/en16020609.

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The electric power quality has become a serious concern for electric utilities and end users owing to its undesirable effects on system capabilities and performance. Harmonic levels on power systems have been pronounced to a greater extent with the continuous growth in the application of solid-state and reactive power compensatory devices. Harmonics are the key constituents that are mainly responsible for power quality deterioration. Power system harmonics need to be correctly estimated and filtered to increase power quality. This research work focuses on accurate estimation of power system ha

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21

Rashed, Muhammad, Iqbal Gondal, Joarder Kamruzzaman, and Syed Islam. "State Estimation within IED Based Smart Grid Using Kalman Estimates." Electronics 10, no. 15 (2021): 1783. http://dx.doi.org/10.3390/electronics10151783.

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State Estimation is a traditional and reliable technique within power distribution and control systems. It is used for building a topology of the power grid network based on state measurements and current operational state of different nodes & buses. The protection of sensors and measurement units such as Intelligent Electronic Devices (IED) in Central Energy Management System (CEMS) against False Data Injection Attacks (FDIAs) is a big concern to grid operators. These are special kind of cyber-attacks that are directed towards the state & measurement data in such a way that mislead th

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22

Ghaedi, Alireza, and Mohammad Esmail Hamedani Golshan. "Modified WLS three-phase state estimation formulation for fault analysis considering measurement and parameter errors." Electric Power Systems Research 190 (January 2021): 106854. http://dx.doi.org/10.1016/j.epsr.2020.106854.

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Farhat, I. A. "A MODIFIED DYNAMIC BACTERIAL FORAGING ALGORITHM FOR AN ENHANCED POWER SYSTEM STATE ESTIMATION." مجلة الجامعة الأسمرية: العلوم التطبيقية 6, no. 5 (2021): 466–78. http://dx.doi.org/10.59743/aujas.v6i5.1501.

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Power systems are getting more complex with the ongoing growing of the ever changing energy demand. This dynamic situation of the electric power networks makes the control and monitoring of the system a crucial issue. In order to have an accurate real time monitoring and representative models, state estimation practices are essential. This requirement becomes more significant for nonlinear systems such as the electric power networks. The objective of the state estimation problem is to apply a variety of statistical and optimization methods in order to determine the best estimate of the power s

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24

Pu, Tian Jiao, Wei Han, Jing Yuan Dong, Qun Li, and Ji Keng Lin. "A Robust State Estimation Method Based on Exponential Weight Functions." Applied Mechanics and Materials 385-386 (August 2013): 1366–71. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.1366.

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State estimation of power system is the basis of all the high level application and analysis for dispatch centers. Against the bad convergence of the WLAV (weighted least absolute value)-based state estimation method, a new robust state estimation method based on exponential weight function (E-LAV) is presented in the paper. This method uses an exponential weight function to replace discontinues weight function of WLAV to improve the poor convergence. The results of the sample system of 4-node system and the IEEE 118-node system show that the E-LAV-based state estimation method not only owns t

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25

Qu, Zhengwei, Jianxuan Zhang, Yunjing Wang, Popov Maxim Georgievitch, and Kai Guo. "False Data Injection Attack Detection and Improved WLS Power System State Estimation Based on Node Trust." Journal of Electrical Engineering & Technology 17, no. 2 (2021): 803–17. http://dx.doi.org/10.1007/s42835-021-00923-1.

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26

Long, Cheng, Hua Zhang, Lilan Dong, and Ruipeng Guo. "Distribution Joint State Estimation with Multiple Snapshots Based on SCADA and AMI Measurements." Journal of Physics: Conference Series 2351, no. 1 (2022): 012011. http://dx.doi.org/10.1088/1742-6596/2351/1/012011.

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To adapt to the distribution network unobservable problem from the scarcity of real-time measurements, the distribution system state estimation (DSSE) method based on hybrid measurements of the supervisory control and data acquisition (SCADA) and the advanced metering infrastructure (AMI) is proposed. Firstly, the ratio of energy data from AMI is used to construct pseudo-measurements, which satisfies the observability of DSSE. Secondly, multiple SCADA acquisition snapshots joint state estimation model is constructed by the SCADA measurements and the energy data of AMI with multiple snapshots.

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27

Yem Souhe, Felix Ghislain, Alexandre Teplaira Boum, Pierre Ele, Camille Franklin Mbey, and Vinny Junior Foba Kakeu. "A Novel Smart Method for State Estimation in a Smart Grid Using Smart Meter Data." Applied Computational Intelligence and Soft Computing 2022 (May 10, 2022): 1–14. http://dx.doi.org/10.1155/2022/7978263.

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Smart grids have brought new possibilities in power grid operations for control and monitoring. For this purpose, state estimation is considered as one of the effective techniques in the monitoring and analysis of smart grids. State estimation uses a processing algorithm based on data from smart meters. The major challenge for state estimation is to take into account this large volume of measurement data. In this article, a novel smart distribution network state estimation algorithm has been proposed. The proposed method is a combined high-gain state estimation algorithm named adaptive extende

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Ayiad, Motaz, Emily Maggioli, Helder Leite, and Hugo Martins. "Communication Requirements for a Hybrid VSC Based HVDC/AC Transmission Networks State Estimation." Energies 14, no. 4 (2021): 1087. http://dx.doi.org/10.3390/en14041087.

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The communication infrastructure of the modern Supervisory, Control and Data Acquisition (SCADA) system continues to enlarge, as hybrid High Voltage Direct Current (HVDC)/Alternating Current (AC) networks emerge. A centralized SCADA faces challenges to meet the time requirements of the two different power networks topologies, such as employing the SCADA toolboxes for both grids. This paper presents the modern communication infrastructure and the time requirements of a centralized SCADA for hybrid HVDC/AC network. In addition, a case study of a complete cycle for a unified Weighted Least Square

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Manousakis, Nikolaos M., and George N. Korres. "Application of State Estimation in Distribution Systems with Embedded Microgrids." Energies 14, no. 23 (2021): 7933. http://dx.doi.org/10.3390/en14237933.

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In this paper, a weighted least square (WLS) state estimation algorithm with equality constraints is proposed for smart distribution networks embedded with microgrids. Since only a limited number of real-time measurements are available at the primary or secondary substations and distributed generation sites, load estimates at unmeasured buses remote from the substations are needed to execute state estimation. The load information can be obtained by forecasted and historical data or smart real-time meters. The proposed algorithms can be applied in either grid-connected or islanded operation mod

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Jin, Tao, Fuliang Chu, Cong Ling, and Daniel Nzongo. "A Robust WLS Power System State Estimation Method Integrating a Wide-Area Measurement System and SCADA Technology." Energies 8, no. 4 (2015): 2769–87. http://dx.doi.org/10.3390/en8042769.

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Ayiad, Motaz, Helder Leite, and Hugo Martins. "State Estimation for Hybrid VSC Based HVDC/AC Transmission Networks." Energies 13, no. 18 (2020): 4932. http://dx.doi.org/10.3390/en13184932.

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As the integration of High Voltage Direct Current (HVDC) systems on modern power networks continues to expand, challenges have appeared in different fields of the network architecture. In the Supervisory, Control and Data Acquisition (SCADA) field, software and toolboxes are expected to be modified to meet the new network characteristics. Therefore, this paper presents a unified Weighted Least Squares (WLS) state estimation algorithm suitable for hybrid HVDC/AC transmission systems, based on Voltage Source Converter (VSC). The mathematical formulas of the unified approach are derived for model

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Macii, David, Daniele Fontanelli, and Grazia Barchi. "A Distribution System State Estimator Based on an Extended Kalman Filter Enhanced with a Prior Evaluation of Power Injections at Unmonitored Buses." Energies 13, no. 22 (2020): 6054. http://dx.doi.org/10.3390/en13226054.

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In the context of smart grids, Distribution Systems State Estimation (DSSE) is notoriously problematic because of the scarcity of available measurement points and the lack of real-time information on loads. The scarcity of measurement data influences on the effectiveness and applicability of dynamic estimators like the Kalman filters. However, if an Extended Kalman Filter (EKF) resulting from the linearization of the power flow equations is complemented by an ancillary prior least-squares estimation of the weekly active and reactive power injection variations at all buses, significant performa

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Radhoush, Sepideh, Trevor Vannoy, Kaveen Liyanage, Bradley M. Whitaker, and Hashem Nehrir. "Distribution System State Estimation and False Data Injection Attack Detection with a Multi-Output Deep Neural Network." Energies 16, no. 5 (2023): 2288. http://dx.doi.org/10.3390/en16052288.

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Distribution system state estimation (DSSE) has been introduced to monitor distribution grids; however, due to the incorporation of distributed generations (DGs), traditional DSSE methods are not able to reveal the operational conditions of active distribution networks (ADNs). DSSE calculation depends heavily on real measurements from measurement devices in distribution networks. However, the accuracy of real measurements and DSSE results can be significantly affected by false data injection attacks (FDIAs). Conventional FDIA detection techniques are often unable to identify FDIAs into measure

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Bretas, A. S., N. G. Bretas, S. H. Braunstein, A. Rossoni, and R. D. Trevizan. "Multiple gross errors detection, identification and correction in three-phase distribution systems WLS state estimation: A per-phase measurement error approach." Electric Power Systems Research 151 (October 2017): 174–85. http://dx.doi.org/10.1016/j.epsr.2017.05.021.

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Office, Energies. "Retraction: A Robust WLS Power System State Estimation Method Integrating a Wide-Area Measurement System and SCADA Technology. Energies 2015, 8, 2769–2787." Energies 8, no. 10 (2015): 10995. http://dx.doi.org/10.3390/en81010995.

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Zhang, Yi Long, and Xue Guang Zhang. "The Output Filter Identification of Three-Phase PWM Converter Using Weighted Least Square Method." Applied Mechanics and Materials 734 (February 2015): 877–86. http://dx.doi.org/10.4028/www.scientific.net/amm.734.877.

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This paper proposed the Weighted Least Square method (WLS method) to identify the output filter of three-phase PWM converter, which incorporates the signal processing as well as mathematical techniques into conventional Least Square method. It sets different weights to different measurements according to the phase where it locates, based on the discovery of the correlation between accuracy and phase of current. The algorithm is tested in both simulation and experimental environment, and the results validate that proposed method gives accurate estimation in steady state, and can response within

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Radhoush, Sepideh, Trevor Vannoy, Kaveen Liyanage, Bradley M. Whitaker, and Hashem Nehrir. "Distribution System State Estimation Using Hybrid Traditional and Advanced Measurements for Grid Modernization." Applied Sciences 13, no. 12 (2023): 6938. http://dx.doi.org/10.3390/app13126938.

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Distribution System State Estimation (DSSE) techniques have been introduced to monitor and control Active Distribution Networks (ADNs). DSSE calculations are commonly performed using both conventional measurements and pseudo-measurements. Conventional measurements are typically asynchronous and have low update rates, thus leading to inaccurate DSSE results for dynamically changing ADNs. Because of this, smart measurement devices, which are synchronous at high frame rates, have recently been introduced to enhance the monitoring and control of ADNs in modern power networks. However, replacing al

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Sveshnikov, Sergey, Victor Bocharnikov, Anatoly Pavlikovsky, and Andrey Prima. "Estimating the potential willingness of the state to use military force based on the Sugeno fuzzy integral." Yugoslav Journal of Operations Research, no. 00 (2022): 2. http://dx.doi.org/10.2298/yjor210515002s.

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Estimation of the potential willingness of the state to use military force is an integral part of the analysis of international relations and the preparation of key decisions in security sphere. Our problem was to develop a method for numerically estimating the potential willingness of any state to use military force. This method should take into account a large number of quantitative and qualitative criteria, the uncertainty of their relationships, as well as the uncertainty of the initial data, some of which can only be obtained with the help of experts. Our analysis has shown that the known

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Jamal, Alaa, and Raphael Linker. "Genetic Operator-Based Particle Filter Combined with Markov Chain Monte Carlo for Data Assimilation in a Crop Growth Model." Agriculture 10, no. 12 (2020): 606. http://dx.doi.org/10.3390/agriculture10120606.

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Particle filter has received increasing attention in data assimilation for estimating model states and parameters in cases of non-linear and non-Gaussian dynamic processes. Various modifications of the original particle filter have been suggested in the literature, including integrating particle filter with Markov Chain Monte Carlo (PF-MCMC) and, later, using genetic algorithm evolutionary operators as part of the state updating process. In this work, a modified genetic-based PF-MCMC approach for estimating the states and parameters simultaneously and without assuming Gaussian distribution for

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Liu, Yingjie, and Dawei Cui. "Vehicle State Estimation Based on Adaptive Fading Unscented Kalman Filter." Mathematical Problems in Engineering 2022 (April 26, 2022): 1–11. http://dx.doi.org/10.1155/2022/7355110.

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Aiming at solving problem of vehicle state estimation, an adaptive fading unscented Kalman filter(AFUKF) algorithm was proposed. Based on this purpose, a 7-DOF nonlinear vehicle model with the Pacejka nonlinear tire model was established firstly. Then, the vehicle state estimator based on Kalman filter was designed to solve the problem of vehicle state estimation. The simulation verification shows the effectiveness and reliability of the designed estimator for vehicle state estimation. Compared with other traditional methods, the calculation accuracy is higher for the AFUKF algorithm to solve

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Liu, Yingjie, and Dawei Cui. "Vehicle State Estimation Based on Adaptive Fading Unscented Kalman Filter." Mathematical Problems in Engineering 2022 (April 26, 2022): 1–11. http://dx.doi.org/10.1155/2022/7355110.

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Aiming at solving problem of vehicle state estimation, an adaptive fading unscented Kalman filter(AFUKF) algorithm was proposed. Based on this purpose, a 7-DOF nonlinear vehicle model with the Pacejka nonlinear tire model was established firstly. Then, the vehicle state estimator based on Kalman filter was designed to solve the problem of vehicle state estimation. The simulation verification shows the effectiveness and reliability of the designed estimator for vehicle state estimation. Compared with other traditional methods, the calculation accuracy is higher for the AFUKF algorithm to solve

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KRAMER, KATHLEEN A., and STEPHEN C. STUBBERUD. "ANALYSIS AND IMPLEMENTATION OF A NEURAL EXTENDED KALMAN FILTER FOR TARGET TRACKING." International Journal of Neural Systems 16, no. 01 (2006): 1–13. http://dx.doi.org/10.1142/s0129065706000457.

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Having a better motion model in the state estimator is one way to improve target tracking performance. Since the motion model of the target is not known a priori, either robust modeling techniques or adaptive modeling techniques are required. The neural extended Kalman filter is a technique that learns unmodeled dynamics while performing state estimation in the feedback loop of a control system. This coupled system performs the standard estimation of the states of the plant while estimating a function to approximate the difference between the given state-coupling function model and the behavio

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Stanley, Thomas R. "Estimating Stage-Specific Daily Survival Probabilities of Nests When Nest age is Unknown." Auk 121, no. 1 (2004): 134–47. http://dx.doi.org/10.1093/auk/121.1.134.

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Abstract Estimation of daily survival probabilities of nests is common in studies of avian populations. Since the introduction of Mayfield's (1961, 1975) estimator, numerous models have been developed to relax Mayfield's assumptions and account for biologically important sources of variation. Stanley (2000) presented a model for estimating stage-specific (e.g. incubation stage, nestling stage) daily survival probabilities of nests that conditions on “nest type” and requires that nests be aged when they are found. Because aging nests typically requires handling the eggs, there may be situations

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Kareem, Urdak, and Fadhaa Hashim. "The Use Of Genetic Algorithm In Estimating The Parameter Of Finite Mixture Of Linear Regression." Journal of Al-Rafidain University College For Sciences ( Print ISSN: 1681-6870 ,Online ISSN: 2790-2293 ), no. 1 (June 29, 2022): 237–52. http://dx.doi.org/10.55562/jrucs.v51i1.536.

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The estimation of the parameters of linear regression is based on the usual Least Square method, as this method is based on the estimation of several basic assumptions. Therefore, the accuracy of estimating the parameters of the model depends on the validity of these hypotheses. The most successful technique was the robust estimation method which is minimizing maximum likelihood estimator (MM-estimator) that proved its efficiency in this purpose. However, the use of the model becomes unrealistic and one of these assumptions is the uniformity of the variance and the normal distribution of the e

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Qin, Yongming, Makoto Kumon, and Tomonari Furukawa. "Estimation of a Human-Maneuvered Target Incorporating Human Intention." Sensors 21, no. 16 (2021): 5316. http://dx.doi.org/10.3390/s21165316.

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This paper presents a new approach for estimating the motion state of a target that is maneuvered by an unknown human from observations. To improve the estimation accuracy, the proposed approach associates the recurring motion behaviors with human intentions, and models the association as an intention-pattern model. The human intentions relate to labels of continuous states; the motion patterns characterize the change of continuous states. In the preprocessing, an Interacting Multiple Model (IMM) estimation technique is used to infer the intentions and extract motions, which eventually constru

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Neupert, Steven, and Julia Kowal. "Model-Based State-of-Charge and State-of-Health Estimation Algorithms Utilizing a New Free Lithium-Ion Battery Cell Dataset for Benchmarking Purposes." Batteries 9, no. 7 (2023): 364. http://dx.doi.org/10.3390/batteries9070364.

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State estimation for lithium-ion battery cells has been the topic of many publications concerning the different states of a battery cell. They often focus on a battery cell’s state of charge (SOC) or state of health (SOH). Therefore, this paper introduces, on the one hand, a new lithium-ion battery dataset with dynamic validation data over degradation and, on the other hand, a model-based SOC and SOH estimation based on this dataset as a reference. An unscented Kalman-filter-based approach was used for SOC estimation and extended with a holistic ageing model to handle the SOH estimation. The p

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Chen, Jenn Yih. "Passivity-Based Parameter Estimation and Position Control of Induction Motors via Composite Adaptation." Applied Mechanics and Materials 284-287 (January 2013): 1894–98. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.1894.

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This paper proposes the parameters estimation and position control of an induction motor drive by using the composite adaptation scheme. First, in the rotor reference frame, the input-output linearization theory was employed to decouple the mechanical rotor position and the rotor flux amplitude at the transient state. An open-loop current model rotor flux observer was utilized for estimating the flux, and then the adaptive laws for estimating the rotor resistance, moment of inertia, viscous friction coefficient, and load torque. The passive properties of the flux observer, rotor resistance est

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Doekemeijer, Bart M., Sjoerd Boersma, Lucy Y. Pao, Torben Knudsen, and Jan-Willem van Wingerden. "Online model calibration for a simplified LES model in pursuit of real-time closed-loop wind farm control." Wind Energy Science 3, no. 2 (2018): 749–65. http://dx.doi.org/10.5194/wes-3-749-2018.

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Abstract. Wind farm control often relies on computationally inexpensive surrogate models to predict the dynamics inside a farm. However, the reliability of these models over the spectrum of wind farm operation remains questionable due to the many uncertainties in the atmospheric conditions and tough-to-model dynamics at a range of spatial and temporal scales relevant for control. A closed-loop control framework is proposed in which a simplified model is calibrated and used for optimization in real time. This paper presents a joint state-parameter estimation solution with an ensemble Kalman fil

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Li, Yan, Yan Zhao Ren, Wan Lin Gao, Sha Tao, Jing Dun Jia, and Xin Liang Liu. "Analysis of Influencing Factors on Winter Wheat Yield Estimations Based on a Multisource Remote Sensing Data Fusion." Applied Engineering in Agriculture 37, no. 5 (2021): 991–1003. http://dx.doi.org/10.13031/aea.14398.

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HighlightsThe potential of fusing GF-1 WFV and MODIS data by the ESTARFM algorithm was demonstrated.A better time window selection method for estimating yields was provided.A better vegetation index suitable for yield estimation based on spatiotemporally fused data was identified.The effect of the spatial resolution of remote sensing data on yield estimations was visualized.Abstract. The accurate estimation of crop yields is very important for crop management and food security. Although many methods have been developed based on single remote sensing data sources, advances are still needed to e

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Hassan, Norsalina, and Dzati Athiar Ramli. "Sparse Component Analysis (SCA) Based on Adaptive Time—Frequency Thresholding for Underdetermined Blind Source Separation (UBSS)." Sensors 23, no. 4 (2023): 2060. http://dx.doi.org/10.3390/s23042060.

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Blind source separation (BSS) recovers source signals from observations without knowing the mixing process or source signals. Underdetermined blind source separation (UBSS) occurs when there are fewer mixes than source signals. Sparse component analysis (SCA) is a general UBSS solution that benefits from sparse source signals which consists of (1) mixing matrix estimation and (2) source recovery estimation. The first stage of SCA is crucial, as it will have an impact on the recovery of the source. Single-source points (SSPs) were detected and clustered during the process of mixing matrix estim

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