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Artykuły w czasopismach na temat "Phasor estimation"
Zhao, Dongfang, Fuping Wang, Shisong Li, Wei Zhao, Lei Chen, Songling Huang, Shen Wang i Haitao Li. "An Optimization of Least-Square Harmonic Phasor Estimators in Presence of Multi-Interference and Harmonic Frequency Variance". Energies 16, nr 8 (12.04.2023): 3397. http://dx.doi.org/10.3390/en16083397.
Pełny tekst źródłaChukkaluru, Sai Lakshmi, i Shaik Affijulla. "Review of Discrete Fourier Transform During Dynamic Phasor Estimation and the Design of Synchrophasor Units". ECTI Transactions on Electrical Engineering, Electronics, and Communications 21, nr 1 (28.02.2023): 248548. http://dx.doi.org/10.37936/ecti-eec.2023211.248548.
Pełny tekst źródłaGuo, Yufu, Hang Xu i Aobing Chi. "Broadband Dynamic Phasor Measurement Method for Harmonic Detection". Electronics 11, nr 11 (24.05.2022): 1667. http://dx.doi.org/10.3390/electronics11111667.
Pełny tekst źródłaGiotopoulos, Vasilis, i Georgios Korres. "Implementation of Phasor Measurement Unit Based on Phase-Locked Loop Techniques: A Comprehensive Review". Energies 16, nr 14 (18.07.2023): 5465. http://dx.doi.org/10.3390/en16145465.
Pełny tekst źródłaParthasarathy, Hari Krishna Achuthan, Madhusudan Saranathan, Adhitya Ravi, M. C. Lavanya i V. Rajini. "Comparative Analysis of Phasor Estimation Techniques for PMU Applications". Journal of Physics: Conference Series 2325, nr 1 (1.08.2022): 012010. http://dx.doi.org/10.1088/1742-6596/2325/1/012010.
Pełny tekst źródłaLiu, Min. "Distribution System State Estimation with Phasor Measurement Units". Applied Mechanics and Materials 668-669 (październik 2014): 687–90. http://dx.doi.org/10.4028/www.scientific.net/amm.668-669.687.
Pełny tekst źródłaXiao, Xianyong, Runze Zhou, Xiaoyang Ma i Rui Xu. "Harmonic Phasor Estimation Method Considering Dense Interharmonic Interference". Entropy 25, nr 2 (27.01.2023): 236. http://dx.doi.org/10.3390/e25020236.
Pełny tekst źródłaMejia-Barron, Arturo, David Granados-Lieberman, Jose Razo-Hernandez, Juan Amezquita-Sanchez i Martin Valtierra-Rodriguez. "Harmonic PMU Algorithm Based on Complex Filters and Instantaneous Single-Sideband Modulation". Electronics 8, nr 2 (29.01.2019): 135. http://dx.doi.org/10.3390/electronics8020135.
Pełny tekst źródładelaOSerna, J. A. "Phasor Estimation From Phasorlets". IEEE Transactions on Instrumentation and Measurement 54, nr 1 (luty 2005): 134–43. http://dx.doi.org/10.1109/tim.2004.838914.
Pełny tekst źródłaChi, Aobing, Chengbi Zeng, Yufu Guo i Hong Miao. "A Bregman-Split-Based Compressive Sensing Method for Dynamic Harmonic Estimation". Entropy 24, nr 7 (17.07.2022): 988. http://dx.doi.org/10.3390/e24070988.
Pełny tekst źródłaRozprawy doktorskie na temat "Phasor estimation"
Jones, Kevin David. "Three-Phase Linear State Estimation with Phasor Measurements". Thesis, Virginia Tech, 2011. http://hdl.handle.net/10919/32119.
Pełny tekst źródłaMaster of Science
Chen, Jian. "Accurate frequency estimation with phasor angles". Thesis, This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-12042009-020203/.
Pełny tekst źródłaChen, Jiaxiong. "Power System State Estimation Using Phasor Measurement Units". UKnowledge, 2013. http://uknowledge.uky.edu/ece_etds/35.
Pełny tekst źródłaZhou, Ming. "Advanced System Monitoring with Phasor Measurements". Diss., Virginia Tech, 2008. http://hdl.handle.net/10919/27813.
Pełny tekst źródłaPh. D.
Khan, Muhammad Ayaz. "State Estimation and Voltage Phasor Measurements in Distribution Networks". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.
Znajdź pełny tekst źródłaHurtgen, Michaël. "Wide-area state estimation using synchronized phasor measurement units". Doctoral thesis, Universite Libre de Bruxelles, 2011. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209924.
Pełny tekst źródłaThe classical state estimator currently used is based on SCADA (Supervisory Control and Data Acquisition) measurements. Weaknesses of the SCADA measurement system are the asynchronicity of the measurements, which introduce errors in the state estimation results during dynamic events on the electrical network.\\
Wide-area monitoring systems, consisting of a network of Phasor Measurement Units (PMU) provide synchronized phasor measurements, which give an accurate snapshot of the monitored part of the network at a given time. The objective of this thesis is to integrate PMU measurements in the state estimator. The proposed state estimators use PMU measurements exclusively, or both classical and PMU measurements.\\
State estimation is particularly useful to filter out measurement noise, detect and eliminate bad data. A sensitivity analysis to measurement errors is carried out for a state estimator using only PMU measurements and a classical state estimator. Measurement errors considered are Gaussian noise, systematic errors and asynchronicity errors. Constraints such as zero injection buses are also integrated in the state estimator. Bad data detection and elimination can be done before the state estimation, as in pre-estimation methods, or after, as in post-estimation methods. For pre-estimation methods, consistency tests are used. Another proposed method is validation of classical measurements by PMU measurements. Post-estimation is applied to a measurement set which has asynchronicity errors. Detection of a systematic error on one measurement in the presence of Gaussian noise is also analysed. \\
The state estimation problem can only be solved if the measurements are well distributed over the network and make the network observable. Observability is crucial when trying to solve the state estimation problem. A PMU placement method based on metaheuristics is proposed and compared to an integer programming method. The PMU placement depends on the chosen objective. A given PMU placement can provide full observability or redundancy. The PMU configuration can also take into account the zero injection nodes which further reduce the number of PMUs needed to observe the network. Finally, a method is proposed to determine the order of the PMU placement to gradually extend the observable island. \\
State estimation errors can be caused by erroneous line parameter or bad calibration of the measurement transformers. The problem in both cases is to filter out the measurement noise when estimating the line parameters or calibration coefficients and state variables. The proposed method uses many measurement samples which are all integrated in an augmented state estimator which estimates the voltage phasors and the additional parameters or calibration coefficients.
Doctorat en Sciences de l'ingénieur
info:eu-repo/semantics/nonPublished
Yang, Xuan. "Distributed state estimation with the measurements of Phasor Measurement Units". Thesis, University of Birmingham, 2013. http://etheses.bham.ac.uk//id/eprint/4479/.
Pełny tekst źródłaNuqui, Reynaldo Francisco. "State Estimation and Voltage Security Monitoring Using Synchronized Phasor Measurements". Diss., Virginia Tech, 2001. http://hdl.handle.net/10919/28266.
Pełny tekst źródłaPh. D.
Wehbe, Yasser. "Model Estimation of Electric Power Systems by Phasor Measurement Units Data". Scholar Commons, 2012. http://scholarcommons.usf.edu/etd/4419.
Pełny tekst źródłaTuku, Woldu. "Distributed state estimation using phasor measurement units (PMUs)for a system snapshot". Kansas State University, 2012. http://hdl.handle.net/2097/14129.
Pełny tekst źródłaDepartment of Electrical and Computer Engineering
Noel N. Schulz
As the size of electric power systems are increasing, the techniques to protect, monitor and control them are becoming more sophisticated. Government, utilities and various organizations are striving to have a more reliable power grid. Various research projects are working to minimize risks on the grid. One of the goals of this research is to discuss a robust and accurate state estimation (SE) of the power grid. Utilities are encouraging teams to change the conventional way of state estimation to real time state estimation. Currently most of the utilities use traditional centralized SE algorithms for transmission systems. Although the traditional methods have been enhanced with advancement in technologies, including PMUs, most of these advances have remained localized with individual utility state estimation. There is an opportunity to establish a coordinated SE approach integration using PMU data across a system, including multiple utilities and this is using Distributed State Estimation (DSE). This coordination will minimize cascading effects on the power system. DSE could be one of the best options to minimize the required communication time and to provide accurate data to the operators. This project will introduce DSE techniques with the help of PMU data for a system snapshot. The proposed DSE algorithm will split the traditional central state estimation into multiple local state estimations and show how to reduce calculation time compared with centralized state estimation. Additionally these techniques can be implemented in micro-grid or islanded system.
Książki na temat "Phasor estimation"
Singh, Hema, R. Chandini i Rakesh Mohan Jha. RCS Estimation of Linear and Planar Dipole Phased Arrays: Approximate Model. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-287-754-3.
Pełny tekst źródłaTime-frequency analysis and synthesis of linear signal spaces: Time-frequency filters, signal detection and estimation, and range-Doppler estimation. Boston: Kluwer Academic Publishers, 1998.
Znajdź pełny tekst źródłaManning, Robert Michael. Real-time in situ signal-to-noise ratio estimation for the assessment of operational communications links. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2002.
Znajdź pełny tekst źródłaManning, Robert Michael. Real-time in situ signal-to-noise ratio estimation for the assessment of operational communications links. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2002.
Znajdź pełny tekst źródłaManning, Robert Michael. Real-time in situ signal-to-noise ratio estimation for the assessment of operational communications links. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2002.
Znajdź pełny tekst źródłaManning, Robert Michael. Real-time in situ signal-to-noise ratio estimation for the assessment of operational communications links. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2002.
Znajdź pełny tekst źródłaLundin, Fredrik. Case studies in omniparametric simulation. Go̊teborg, Sweden: Dept. of Mathematical Sciences, Chalmers University of Technology and Göteborg University, 2006.
Znajdź pełny tekst źródłaLaari, Arto. Gas-liquid mass transfer in bubbly flow: Estimation of mass transfer, bubble size and reactor performance in various applications. Lappeenranta: Lappeenranta University of Technology, 2005.
Znajdź pełny tekst źródłaR, Kumar. A novel multistage estimation of the signal parameters of a possibly data-modulated sinusoid under very high dynamics. Pasadena, Calif: National Aeronautics and Space Administration, Jet Propulsion Laboratory, California Institute of Technology, 1989.
Znajdź pełny tekst źródłaSatdarova, Faina. DIFFRACTION ANALYSIS OF DEFORMED METALS: Theory, Methods, Programs. xxu: Academus Publishing, 2019. http://dx.doi.org/10.31519/monography_1598.
Pełny tekst źródłaCzęści książek na temat "Phasor estimation"
Phadke, A. G., i J. S. Thorp. "Phasor Estimation of Nominal Frequency Inputs". W Power Electronics and Power Systems, 29–48. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-76537-2_2.
Pełny tekst źródłaPhadke, Arun G., i James S. Thorp. "Phasor Estimation of Nominal Frequency Inputs". W Power Electronics and Power Systems, 29–45. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50584-8_2.
Pełny tekst źródłaPhadke, A. G., i J. S. Thorp. "Phasor Estimation at Off-Nominal Frequency Inputs". W Power Electronics and Power Systems, 49–79. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-76537-2_3.
Pełny tekst źródłaPhadke, Arun G., i James S. Thorp. "Phasor Estimation at Off-Nominal Frequency Inputs". W Power Electronics and Power Systems, 47–72. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50584-8_3.
Pełny tekst źródłaJordaan, Jaco, Anton van Wyk i Ben van Wyk. "Nonparametric Time-Varying Phasor Estimation Using Neural Networks". W Neural Information Processing, 693–702. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-69162-4_72.
Pełny tekst źródłaShankar, Shiv, Vishal Rathore, K. B. Yadav i Alok Priyadarshi. "State Estimation of Power Network Using Phasor Measurement". W Lecture Notes in Electrical Engineering, 63–74. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0193-5_6.
Pełny tekst źródłaThiab, Omar Sami, Łukasz Nogal i Ryszard Kowalik. "Dynamic Power Systems Phasor Estimation Using Kalman Filter Algorithms". W Communications in Computer and Information Science, 110–22. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38752-5_9.
Pełny tekst źródłaAarthi, V., Ram Krishan i Himanshu Grover. "State Estimation in Active Power Distribution Systems Using Phasor Measurement Units". W Lecture Notes in Electrical Engineering, 123–39. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0969-8_14.
Pełny tekst źródłaAlam, Mehebub, Shubhrajyoti Kundu, Siddhartha Sankar Thakur i Sumit Banerjee. "Transmission Line Outage Estimation Through Bus Current Comparison Utilizing Current Phasor of PMU". W Lecture Notes in Electrical Engineering, 377–89. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7994-3_35.
Pełny tekst źródłaSaha, Soumili, Prateek Bose i Sarita Nanda. "Phasor Estimation of Power Signals in a Smart Grid Environment Using Sigmoid Adaptive Filter". W Lecture Notes in Electrical Engineering, 487–93. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0749-3_36.
Pełny tekst źródłaStreszczenia konferencji na temat "Phasor estimation"
Bhonsle, J. S., i A. S. Junghare. "Phasor assisted state estimation". W 2015 International Conference on Computer, Communication and Control (IC4). IEEE, 2015. http://dx.doi.org/10.1109/ic4.2015.7375587.
Pełny tekst źródłaPaternina, M. R. A., A. Zamora, M. Ernesto Vazquez i Juan M. Ramirez. "Phasor estimation under transient conditions". W 2015 IEEE Eindhoven PowerTech. IEEE, 2015. http://dx.doi.org/10.1109/ptc.2015.7232242.
Pełny tekst źródłaDaoud, George, Hany Selim i Mohamed M. AbdelRaheem. "Micro Phasor Measurement Unit Phasor Estimation by off-Nominal Frequency". W 2018 IEEE International Conference on Smart Energy Grid Engineering (SEGE). IEEE, 2018. http://dx.doi.org/10.1109/sege.2018.8499506.
Pełny tekst źródłaJones, Kevin D., James S. Thorp i R. Matthew Gardner. "Three-phase linear state estimation using Phasor Measurements". W 2013 IEEE Power & Energy Society General Meeting. IEEE, 2013. http://dx.doi.org/10.1109/pesmg.2013.6672516.
Pełny tekst źródłaAbur, Ali, i Floyd Galvan. "Synchro-Phasor Assisted State Estimation (SPASE)". W 2012 IEEE PES Innovative Smart Grid Technologies (ISGT). IEEE, 2012. http://dx.doi.org/10.1109/isgt.2012.6175559.
Pełny tekst źródłaPatel, Tapas Kumar, Sarat Chandra Swain, Prafulla Chandra Panda i Subodh Kumar Mohanty. "Accurate phasor estimation during power swing". W 2017 Innovations in Power and Advanced Computing Technologies (i-PACT). IEEE, 2017. http://dx.doi.org/10.1109/ipact.2017.8244911.
Pełny tekst źródłaSodhi, Ranjana, S. C. Srivastava i S. N. Singh. "Teager energy based dynamic phasor estimation". W 2012 Annual IEEE India Conference (INDICON). IEEE, 2012. http://dx.doi.org/10.1109/indcon.2012.6420792.
Pełny tekst źródłaKovacic, Marko, Marko Jurcevic, Roman Malaric i Antonijo Kunac. "A Review of Phasor Estimation Algorithms". W 2020 3rd International Colloquium on Intelligent Grid Metrology (SMAGRIMET). IEEE, 2020. http://dx.doi.org/10.23919/smagrimet48809.2020.9264012.
Pełny tekst źródłaRao, J. Ganeswara, i Ashok Kumar Pradhan. "Accurate phasor estimation during power swing". W 2016 IEEE Power and Energy Society General Meeting (PESGM). IEEE, 2016. http://dx.doi.org/10.1109/pesgm.2016.7741201.
Pełny tekst źródłaVats, Mansi, i Sangeeta Kamboj. "Application of phasor & frequency estimation techniques in phasor measurement unit". W 2016 7th India International Conference on Power Electronics (IICPE). IEEE, 2016. http://dx.doi.org/10.1109/iicpe.2016.8079490.
Pełny tekst źródłaRaporty organizacyjne na temat "Phasor estimation"
Choquette, Gary, i Nolan Choquette. PR-000-16600-R01 Correlative Estimation of Hydrocarbon Dewpoint. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), wrzesień 2019. http://dx.doi.org/10.55274/r0011618.
Pełny tekst źródłaPeters, Keith, i Steven Kay. Unbiased Estimation of the Phase of a Sinusoid. Fort Belvoir, VA: Defense Technical Information Center, styczeń 2002. http://dx.doi.org/10.21236/ada525814.
Pełny tekst źródłaCumming, I. G., F. Wong i R. K. Hawkins. RADARSAT Doppler Centroid Estimation Using Phase-Based Estimators. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1999. http://dx.doi.org/10.4095/219627.
Pełny tekst źródłaBlampied, Nigel, Tariq Shehab, Elhami Nasr i Laxmi Sindhu Samudrala. Preconstruction Support Cost Hours Estimating on Caltrans Pavement Rehabilitation Projects. Mineta Transportation Institute, maj 2023. http://dx.doi.org/10.31979/mti.2023.2148.
Pełny tekst źródłaTeti, Joseph G., i Jr. Basic Concepts of Spectral Estimation Using a Uniform Linear Phased Array. Fort Belvoir, VA: Defense Technical Information Center, wrzesień 1988. http://dx.doi.org/10.21236/ada325809.
Pełny tekst źródłaNeuert, Mark, i Smitha Koduru. PR-244-173856-R01 In-line Inspection Crack Tool Reliability and Performance Evaluation. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), czerwiec 2019. http://dx.doi.org/10.55274/r0011599.
Pełny tekst źródłaBajwa, Abdullah, Tim Kroeger i Timothy Jacobs. PR-457-17201-R04 Residual Gas Fraction Estimation Based on Measured Engine Parameters - Phase IV. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), wrzesień 2021. http://dx.doi.org/10.55274/r0012176.
Pełny tekst źródłaMaes, Marc. PR-328-133600-R02 Probabilistic Corrosion Growth Models and ILI-Based Estimation Procedures - Phase II. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), kwiecień 2015. http://dx.doi.org/10.55274/r0010842.
Pełny tekst źródłaBajwa, Abdullah, i Timothy Jacobs. PR-457-17201-R01 Residual Gas Fraction Estimation Based on Measured In-Cylinder Pressure. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), wrzesień 2018. http://dx.doi.org/10.55274/r0011519.
Pełny tekst źródłaBorden, Brett, i R. J. Dinger. Radar Inverse Scattering Using Statistical Estimation of the Echo Phase- Front Derivatives. Fort Belvoir, VA: Defense Technical Information Center, listopad 1986. http://dx.doi.org/10.21236/ada176598.
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