Artykuły w czasopismach: „WLS STATE ESTIMATION”

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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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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 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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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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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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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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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 not observed or not directly measured. State estimation is a computer program that detects, isolate and eliminate the incorrect or bad measurement data and estimates the accurate state. The magnitudes of bus voltage and phase angle are the states variables for an electrical power system. This paper outlines Weighted Least Square (WLS) estimation techniques and simulated estimation for standard IEEE systems.

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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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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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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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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 not observed or not directly measured. State estimation is a computer program that detects, isolate and eliminate the incorrect or bad measurement data and estimates the accurate state. The magnitudes of bus voltage and phase angle are the states variables for an electrical power system. This paper outlines Weighted Least Square (WLS) estimation techniques and simulated estimation for standard IEEE systems.

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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 estimation to improve the accuracy under non-Gaussian environments firstly. Secondly, a novel robust EKF, called C-WLS-EKF, is developed by combining the advantages of correntropy and weighted least squares (WLS) to improve the digital stability of the correntropy EKF (C-EKF). In addition, the convergence of the proposed algorithm is verified by the Cramér–Rao low bound. Finally, a C-WLS-EKF method based on an equivalent circuit model is designed to perform SOC estimation. The experiment results clarify that the SOC estimation error in terms of the MSE via the proposed C-WLS-EKF method can efficiently be reduced from 1.361% to 0.512% under non-Gaussian noise conditions.

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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 its analogous relationship with state estimation, machine learning and its ability to summarily model unseen and complex relationships between input data is used. Thus, a power system state estimator was developed using modified long short-term (LSTM) neural networks to provide quicker and more accurate state estimates over the conventional weighted least squares-based state estimator (WLS-SE). The networks are then subject to standard polynomial scheduled weight pruning to further optimize the size and memory consumption of the neural networks. The state estimators were tested on a hybrid AC/DC distribution system composed of the IEEE 34-bus AC test system and a 9-bus DC microgrid. The conventional WLS-SE has achieved a root mean square error (RMSE) of 0.0151 p.u. for voltage magnitude estimates, while the LSTM’s were able to achieve RMSE’s between 0.0019 p.u. and 0.0087 p.u., with the latter having 75% weight sparsity, estimates about ten times faster, and half of its full memory requirement occupied.

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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 with errors, especially with the ongoing expansion of the power networks. These errors are linked to many issues related to operation and communication processes besides the errors caused by the metering device itself. The commonly applied technique to solve the power system state estimation problem is the Weighted Least-Squares (WLS) method. In this paper a dynamically adapted algorithm is introduced employing a WLS-based dynamic JAYA optimization technique. The algorithm was validated and applied on the well-known IEEE 14-bus network. The results proved the efficiency and advantages of the proposed algorithm when compared to other methods used for the state estimation problem.

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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 harmonics with the proposed hybrid weighted least-square multi-verse optimizer (WLS–MVO) based framework. Multi-verse optimizer replicates the phenomenon of the formation of new universes as described by multi-verse theory to solve complex real-world optimization problems. The proposed WLS–MVO framework is tested and validated by estimating the harmonics present in multiple test signals with different noise levels. Amplitudes and phases of harmonics present in the polluted signal were estimated, and the framework computational time was compared with the previously developed technique’s results which are reported in the literature. There was 80% reduction in computational time and 82% improvement in terms of accuracy in estimating harmonics using WLS–MVO as compared to previously developed techniques. The performance of the developed framework is further validated by estimating the harmonics present in the real-time voltage and current waveforms obtained from axial flux permanent magnet generator (AFPMSG), uninterruptible power supply (UPS), and light-emitting diode (LED). The purposed technique technique outperforms the already-developed techniques, in terms of accuracy and computational time.

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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 the CEMS into making incorrect decisions and create generation load imbalance. These are known to bypass the traditional bad data detection systems within central estimators. This paper presents the use of an additional novel state estimator based on Kalman filter along with traditional Distributed State Estimation (DSE) which is based on Weighted Least Square (WLS). Kalman filter is a feedback control mechanism that constantly updates itself based on state prediction and state correction technique and shows improvement in the estimates. The additional estimator output is compared with the results of DSE in order to identify anomalies and injection of false data. We evaluated our methodology by simulating proposed technique using MATPOWER over IEEE-14, IEEE-30, IEEE-118, IEEE-300 bus. The results clearly demonstrate the superiority of the proposed method over traditional state estimation.

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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 system variables. The variables of the power system are conventionally measured using various common metering devices in spite of the complexity and gradual expansion of the networks. However, these measuring meters are associated with errors and inaccurate output readings due to several operational, communicational and device-linked causes. Consequently, determining an improved and optimized estimation of the system state is significant and essentially needed, and hence this topic is getting more attraction among the researchers. The most typically applied approach to deal with the state estimation problem is the Weighted Least-squares (WLS) method. In this paper a hybrid algorithm is introduced utilizing a WLS-based dynamic bacterial foraging algorithm (DBFA). The proposed algorithm was applied and validated using the well-known IEEE 14-bus system. The results demonstrated the effectiveness and superiority of the algorithm when compared to some of other techniques used to tackle the state estimation issue

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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 the similar convergence as WLS-based method, and also owns a strong robustness. Through modifying the weight matrix of the least squares, the proposed new method has good prospects for application in practical engineering.

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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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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. In a rectangular coordinate system, the joint state estimation model and the weighted least squares (WLS) method are described in quadratic polynomial form, leading to a quadratic constraint quadratic estimation (QCQE) model. The QCQE model can decouple the modeling and algorithm implementation of state estimation and enhance the efficiency of algorithm implementation significantly. Otherwise, more state estimation mature algorithms can be adopted for the joint state estimation model in quadratic polynomial form. Simulations on the IEEE 33-bus system verify the accuracy and effectiveness of the proposed method.

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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 extended Kalman filter (AEKF) using extended Kalman filter (EKF) and unscented Kalman filter (UKF) in order to achieve better intelligent utility grid state estimation accuracy. The performance index and the error are indicators used to evaluate the accuracy of the estimation models in this article. An IEEE 37-node test network is used to implement the state estimation models. The state variables considered in this article are the voltage module at the measurement nodes. The results obtained show that the proposed hybrid algorithm has better performance compared to single state estimation methods such as the extended Kalman filter, the unscented Kalman filter, and the weighted least squares (WLS) method.

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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 Squares (WLS) state estimation is tested on a hybrid HVDC/AC transmission network, based on Voltage Source Converter (VSC). The cycle estimates the elapsed times from the sensors up to the SCADA side, including the data acquisition and the WLS processing times. The case study is carried out on the Cigre B4 DC test case network with 43 virtual Remote Terminal Unit (RTU)s installed and 10 data concentrators, all connected through a fiber-based communication network. It is concluded that the time requirements can be fulfilled for a hybrid HVDC/AC network.

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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 mode and can efficiently identify breaker status errors at the main substations and feeders, where sufficient measurement redundancy exists. The impact of the accuracy of real and pseudo-measurements on the estimated bus voltages is tested with a 55-bus distribution network including distributed generation.

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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 modelling the AC, DC and converter coupling components. The method couples the AC and DC sides of the converter through power and voltage constraints and measurement functions. Two hybrid power system test cases have been studied to validate this work, a 4-AC/4-DC/4-AC network and Cigre B4 DC test case network. Furthermore, comparison between the fully decentralized state estimation and the unified method is provided, which indicated an accuracy improvement and error reduction.

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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 performance improvements can be achieved. Extensive simulation results obtained assuming to deploy an increasing number of next-generation smart meters and Phasor Measurement Units (PMUs) show that not only the proposed approach is generally more accurate and precise than the classic Weighted Least Squares (WLS) estimator (chosen as a benchmark algorithm), but it is also less sensitive to both the number and the metrological features of the PMUs. Thus, low-uncertainty state estimates can be obtained even though fewer and cheaper measurement devices are used.

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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 measurement data. In this study, a novel deep neural network approach is proposed to simultaneously perform DSSE calculation (i.e., regression) and FDIA detection (i.e., binary classification) using real measurements. In the proposed work, the classification nodes in the DNN allow us to identify which measurements on which phasor measurement unit (PMU), if any, were affected. In the proposed approach, we aim to show that the proposed method can perform DSSE calculation and identify FDIAs from the available measurements simultaneously with high accuracy. We compare our proposed method to the traditional approach of detecting FDIAs and performing SE calculations separately; moreover, DSSE results are compared with the weighted least square (WLS) algorithm, which is a common model-based method. The proposed method achieves better DSSE performance than the WLS method and the separate DSSE/FDIA method in presence of erroneous measurements; our method also executes faster than the other methods. The effectiveness of the proposed method is validated using two FDIA schemes in two case studies: one using a modified IEEE 33-bus distribution system without DGs, and the other using a modified IEEE 69-bus system with DGs. The results illustrated that the accuracy and F1-score of the proposed method are better than when performing binary classification only. The proposed method successfully detected the FDIAs on each PMU measurement. Moreover, the results of DSSE calculation from the proposed method has a better performance compared to the regression-only method, and the WLS methods in the presence of bad data.

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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 10ms in when grid voltage drops. This method can work under both balanced and unbalanced operating conditions, therefore provides a powerful tool for various control strategies to better understand the operating conditions. Compared with the invasive method, which intentionally inject a series of white noise in the system, proposed WLS method does not bring any turbulence, while compared with conventional Least Square method, it possesses better stability as well as higher accuracy.

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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 all traditional measurement devices with smart measurements is not feasible over a short time. Thus, an essential part of the grid modernization process is to use both traditional and advanced measurements to improve DSSE results. In this paper, a new method is proposed to hybridize traditional and advanced measurements using an online machine learning model. In this work, we assume that an ADN has been monitored using traditional measurements and the Weighted Least Square (WLS) method to obtain DSSE results, and the voltage magnitude and phase angle at each bus are considered as state vectors. After a period of time, a network is modified by the installation of advanced measurement devices, such as Phasor Measurement Units (PMUs), to facilitate ADN monitoring and control with a desired performance. Our work proposes a method for taking advantage of all available measurements to improve DSSE results. First, a machine-learning-based regression model was trained from DSSE results obtained using only the traditional measurements available before the installation of smart measurement devices. After smart measurement devices were added to the network, the model predicted traditional measurements when those measurements were not available to enable synchronization between the traditional and smart sensors, despite their different refresh rates. We show that the regression model had improved performance under the condition that it continued to be updated regularly as more data were collected from the measurement devices. In this way, the training model became robust and improved the DSSE performance, even in the presence of more Distributed Generations (DGs). The results of the proposed method were compared to traditional measurements incorporated into the DSSE calculation using a sample-and-hold technique. We present the DSSE results in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values for all approaches. The effectiveness of the proposed method was validated using two case studies in the presence of DGs: one using a modified IEEE 33-bus distribution system that considered loads and DGs based on a Monte Carlo simulation and the other using a modified IEEE 69-bus system that considered actual data for loads and DGs. The DSSE results illustrate that the proposed method is better than the sample-and-hold method.

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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 methods have a number of serious shortcomings. We proposed to solve this problem based on the representation of partial estimations of states in the form of fuzzy sets, and the importance of criteria in the form of a fuzzy measure. We also proposed to aggregate the partial estimations using the Sugeno fuzzy integral. We developed a hierarchical structure of estimation criteria, determined the importance of the criteria, built an observation channel based on the Harrington curve to obtain input estimations, and also developed an aggregation algorithm. As a result, we calculated estimations for 137 states and examined their potential willingness to use military force. The results disclose new aspects of using fuzzy-integral calculus to construct hierarchical models of multi-criteria estimating, and also demonstrate the possibility of using artificial intelligence methods to obtain numerical estimations in the sphere of international relations.

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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 priors is presented. The method was tested on two simulation examples on the basis of the crop model AquaCrop-OS. In the first example, the method was compared to a PF-MCMC method in which states and parameters are updated sequentially and genetic operators are used only for state adjustments. The influence of ensemble size, measurement noise, and mutation and crossover parameters were also investigated. Accurate and stable estimations of the model states were obtained in all cases. Parameter estimation was more challenging than state estimation and not all parameters converged to their true value, especially when the parameter value had little influence on the measured variables. Overall, the proposed method showed more accurate and consistent parameter estimation than the PF-MCMC with sequential estimation, which showed highly conservative behavior. The superiority of the proposed method was more pronounced when the ensemble included a large number of particles and the measurement noise was low.

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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 the problem of vehicle state estimation. The study can help drivers easily identify key state estimation in safe driving area.

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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 the problem of vehicle state estimation. The study can help drivers easily identify key state estimation in safe driving area.

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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 behavior of the true plant dynamics. At each sample step, this new model is added to the existing model to improve the state estimate. The neural extended Kalman filter has also been investigated as a target tracking estimation routine. Implementation issues for this adaptive modeling technique, including neural network training parameters, were investigated and an analysis was made of the quality of performance that the technique can have for tracking maneuvering targets.

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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 where nests can not or should not be aged and the Stanley (2000) model will be inapplicable. Here, I present a model for estimating stage-specific daily survival probabilities that conditions on nest stage for active nests, thereby obviating the need to age nests when they are found. Specifically, I derive the maximumlikelihood function for the model, evaluate the model's performance using Monte Carlo simulations, and provide software for estimating parameters (along with an example). For sample sizes as low as 50 nests, bias was small and confidence interval coverage was close to the nominal rate, especially when a reduced-parameter model was used for estimation.

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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 error. These assumptions are not achievable in the case of studying a specific problem that may include complex data of more than one model. To deal with this type of problem, a mixture of linear regression is used to model such data. In this article, we propose a genetic algorithm-based method combined with (MM-estimator), which is called in this article (RobGA), to improve the accuracy of the estimation in the final stage. We compare the suggested method with robust bi-square (MixBi) in terms of their application to real data representing blood sample. The results showed that RobGA is more efficient in estimating the parameters of the model than the MixBi method with respect to mean square error (MSE) and classification error (CE).

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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 construct the intention-pattern model. Once the intention-pattern model has been constructed, the proposed approach incorporate the intention-pattern model to estimation using any state estimator including Kalman filter. The proposed approach not only estimates the mean using the human intention more accurately but also updates the covariance using the human intention more precisely. The performance of the proposed approach was investigated through the estimation of a human-maneuvered multirotor. The result of the application has first indicated the effectiveness of the proposed approach for constructing the intention-pattern model. The ability of the proposed approach in state estimation over the conventional technique without intention incorporation has then been demonstrated.

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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 paper describes the dataset, the models, the parameterisation, the implementation of the state estimations, and their validation using parts of the dataset, resulting in SOC and SOH estimations over the entire battery life. The results show that the dataset can be used to extract parameters, design models based on it, and validate it with dynamically degraded battery cells. The work provides an approach and dataset for better performance evaluations, applicability, and reliability investigations.

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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 estimator, and composite adaptive position controller were analyzed based on the passivity theorem. According to the properties, the overall position control system was proved to be globally stable without using Lyapunov-type arguments. Experimental results are finally provided to show that the proposed method is robust to variations of the motor mechanical parameters, rotor resistance, and load torque disturbances. Moreover, good position tracking response and characteristics on parameter estimation can be achieved.

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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 filter at its core, which calibrates the surrogate model to the actual atmospheric conditions. The estimator is tested in high-fidelity simulations of a nine-turbine wind farm. Exclusively using measurements of each turbine's generated power, the adaptability to modeling errors and mismatches in atmospheric conditions is shown. Convergence is reached within 400 s of operation, after which the estimation error in flow fields is negligible. At a low computational cost of 1.2 s on an 8-core CPU, this algorithm shows comparable accuracy to the state of the art from the literature while being approximately 2 orders of magnitude faster.

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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 exploit multisource remote sensing data with higher spatial and temporal resolution. More suitable time window selection methods and vegetation indexes, both of which are critical for yield estimations, have not been fully considered. In this article, the Chinese GaoFen-1 Wide Field View (GF-1 WFV) and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) data were fused by the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to generate time-series data with a high spatial resolution. Then, two time window selection methods involving distinguishing or not distinguishing the growth stages during the monitoring period, and three vegetation indexes, the normalized difference vegetation index (NDVI), two-band enhanced vegetation index (EVI2) and wide dynamic range vegetation index (WDRVI), were intercompared. Furthermore, the yield estimations obtained from two different spatial resolutions of fused data and MODIS data were analyzed. The results indicate that taking the growth stage as the time window unit division basis can allow a better estimation of winter wheat yield; and that WDRVI is more suitable for yield estimations than NDVI or EVI2. This study demonstrates that the spatial resolution has a great influence on yield estimations; further, this study identifies a better time window selection method and vegetation index for improving the accuracy of yield estimations based on a multisource remote sensing data fusion. Keywords: Remote sensing, Spatiotemporal data fusion, Winter wheat, Yield estimation.

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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 estimation. Adaptive time–frequency thresholding (ATFT) was introduced to increase the accuracy of the mixing matrix estimations. ATFT only used significant TF coefficients to detect the SSPs. After identifying the SSPs, hierarchical clustering approximates the mixing matrix. The second stage of SCA estimated the source recovery using least squares methods. The mixing matrix and source recovery estimations were evaluated using the error rate and mean squared error (MSE) metrics. The experimental results on four bioacoustics signals using ATFT demonstrated that the proposed technique outperformed the baseline method, Zhen’s method, and three state-of-the-art methods over a wide range of signal-to-noise ratio (SNR) ranges while consuming less time.

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