Improving accuracy of state estimator solutions

In power systems, state estimation is one of the core functions of energy management system (EMS). And its function is based on a variety of measurement information to estimate the current state of the power system. To protect the safety of modern power system, it depends on the energy management sy...

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Main Author: Liu, Yao
Other Authors: Gooi Hoay Beng
Format: Final Year Project
Language:English
Published: 2015
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Online Access:http://hdl.handle.net/10356/64720
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-647202023-07-07T17:00:45Z Improving accuracy of state estimator solutions Liu, Yao Gooi Hoay Beng School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering In power systems, state estimation is one of the core functions of energy management system (EMS). And its function is based on a variety of measurement information to estimate the current state of the power system. To protect the safety of modern power system, it depends on the energy management system (EMS), and the plenty of functions in EMS can be divided into two major sections: analysis of real-time changes for online applications and analysis of typical trend for off-line applications in power systems. State estimation can be regarded as the basis for most of the advanced online application software. If the results of state estimation are not accurate, and the subsequent analysis and calculation will not get accurate results as well. Hence, we can understand the importance of a correct state estimator solution. In this project, the main objective is to improve the accuracy of state estimator solutions and eliminate the unexpected errors in the state estimation. Corresponding troubleshooting methods have been conducted, such as collating the MTALAB codes with state estimation algorithm, measuring on the inductance and line impedance, checking the admittance matrix, modifying the parameters of the system and so on. In addition, the relationship between the state estimation algorithm and MATLAB codes was explained and verified. By figuring out how the parameters affect the state estimated results, state estimation has been well understood. Another objective is to solve the compatibility problems. By following the principle of trace-back, the problems have been fixed by the debugging LabVIEW program. A Simple Error Handler was added to find out the source of the problems. In the process of finding the errors, Energy Management System (EMS) flow was investigated, which is in the order of State Estimation, Bad Data Detection and Identification, Automatic Generation Control and Optimal Power Flow. Apart from the two objectives, Microgrid Topology was verified and a correspondence table was built regarding to the orientation of bus numbering system. The recommendation for future work would also be discussed at the end. Bachelor of Engineering 2015-05-29T07:45:34Z 2015-05-29T07:45:34Z 2015 2015 Final Year Project (FYP) http://hdl.handle.net/10356/64720 en Nanyang Technological University 82 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Liu, Yao
Improving accuracy of state estimator solutions
description In power systems, state estimation is one of the core functions of energy management system (EMS). And its function is based on a variety of measurement information to estimate the current state of the power system. To protect the safety of modern power system, it depends on the energy management system (EMS), and the plenty of functions in EMS can be divided into two major sections: analysis of real-time changes for online applications and analysis of typical trend for off-line applications in power systems. State estimation can be regarded as the basis for most of the advanced online application software. If the results of state estimation are not accurate, and the subsequent analysis and calculation will not get accurate results as well. Hence, we can understand the importance of a correct state estimator solution. In this project, the main objective is to improve the accuracy of state estimator solutions and eliminate the unexpected errors in the state estimation. Corresponding troubleshooting methods have been conducted, such as collating the MTALAB codes with state estimation algorithm, measuring on the inductance and line impedance, checking the admittance matrix, modifying the parameters of the system and so on. In addition, the relationship between the state estimation algorithm and MATLAB codes was explained and verified. By figuring out how the parameters affect the state estimated results, state estimation has been well understood. Another objective is to solve the compatibility problems. By following the principle of trace-back, the problems have been fixed by the debugging LabVIEW program. A Simple Error Handler was added to find out the source of the problems. In the process of finding the errors, Energy Management System (EMS) flow was investigated, which is in the order of State Estimation, Bad Data Detection and Identification, Automatic Generation Control and Optimal Power Flow. Apart from the two objectives, Microgrid Topology was verified and a correspondence table was built regarding to the orientation of bus numbering system. The recommendation for future work would also be discussed at the end.
author2 Gooi Hoay Beng
author_facet Gooi Hoay Beng
Liu, Yao
format Final Year Project
author Liu, Yao
author_sort Liu, Yao
title Improving accuracy of state estimator solutions
title_short Improving accuracy of state estimator solutions
title_full Improving accuracy of state estimator solutions
title_fullStr Improving accuracy of state estimator solutions
title_full_unstemmed Improving accuracy of state estimator solutions
title_sort improving accuracy of state estimator solutions
publishDate 2015
url http://hdl.handle.net/10356/64720
_version_ 1772825914323763200