Enhancement of Anomalous Data Mining in Power System Predicting-Aided State Estimation

2004 ◽  
Vol 19 (1) ◽  
pp. 610-619 ◽  
Author(s):  
S.-J. Huang ◽  
J.-M. Lin
Keyword(s):  
Data Mining ◽  
Power System ◽  
State Estimation ◽  
Anomalous Data

Two-level state estimation for large power system. Part 1: Algorithms

1988 ◽  
Vol 135 (4) ◽  
pp. 299 ◽  
Author(s):  
K.L. Lo ◽  
M.M. Salem ◽  
R.D. McColl ◽  
A.M. Moffatt
Keyword(s):  
Power System ◽  
State Estimation ◽  
Large Power ◽  
System Part

A Harmonic Source Location Method Based on PMU Measurements in Distribution Network

2015 ◽  
Vol 775 ◽  
pp. 409-414
Author(s):  
Bing Jun Li ◽  
Su Quan Zhou ◽  
Xiao Xiang Lun
Keyword(s):  
Power System ◽  
State Estimation ◽  
Distribution Network ◽  
Measured Data ◽  
Optimal Measurement ◽  
Location Method ◽  
Measurement Placement ◽  
Minimum Number ◽  
Harmonic Source ◽  
The Cost

It is of great importance to identify the location of the harmonic sources for the harmonic governance in the power system. Applied with optimal measurement placement (OMP) and harmonic state estimation (HSE), this paper presents a novel process based on PMU measurements to locate the harmonic sources in the distribution network. Considering the cost and the observability, the OMP can provide a scheme of the measurement placement with the minimum number of PMU measurements. In order to simplify the HSE equation, the measured data are converted to the form of voltage by the method proposed in this paper.By solving the HSE equation, the location and magnitude of the harmonic source are evaluated. The methodology is applied to the IEEE 33-bus system, and the obtained results are properly analyzed.

Power System State Estimation: Detection of Topological Errors

1981 ◽  
Vol PER-1 (1) ◽  
pp. 19-19
Author(s):  
R. L. Lugtu ◽  
D. F. Hackett ◽  
K. C. Liu ◽  
D. D. Might
Keyword(s):  
Power System ◽  
State Estimation ◽  
System State ◽  
Power System State Estimation ◽  
Topological Errors

scholarly journals Trust assessment of power system states

2020 ◽  
Vol 3 (S1) ◽  
Author(s):  
Michael Brand ◽  
Davood Babazadeh ◽  
Carsten Krüger ◽  
Björn Siemers ◽  
Sebastian Lehnhoff
Keyword(s):  
Power Systems ◽  
Power System ◽  
Data Acquisition ◽  
State Estimation ◽  
Trust Model ◽  
Proof Of Concept ◽  
Process Data ◽  
Acquisition Process ◽  
Functional Correctness ◽  
Data Acquisition Process

Abstract Modern power systems are cyber-physical systems with increasing relevance and influence of information and communication technology. This influence comprises all processes, functional, and non-functional aspects like functional correctness, safety, security, and reliability. An example of a process is the data acquisition process. Questions focused in this paper are, first, how one can trust in process data in a data acquisition process of a highly-complex cyber-physical power system. Second, how can the trust in process data be integrated into a state estimation to achieve estimated results in a way that it can reflect trustworthiness of that input?We present the concept of an anomaly-sensitive state estimation that tackles these questions. The concept is based on a multi-faceted trust model for power system network assessment. Furthermore, we provide a proof of concept by enriching measurements in the context of the IEEE 39-bus system with reasonable trust values. The proof of concept shows the benefits but also the limitations of the approach.

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