A New Method for Optimal Harmonic Meter Placement

Ketabi, Abbas; Hosseini, Seyyed Abed

doi:10.3844/ajassp.2008.1499.1505

Cited by 7 publications

(6 citation statements)

References 13 publications

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“…the remaining 1 2 − N is still available in the network. Other methods proposed in [1], [3], [4],and [5] can also be used for the similar purpose. The next step is to remove each measurement and relative error is calculated.…”

Section: Illustration Of Methods Usedmentioning

confidence: 99%

“…In [4], authors propose a hybrid genetic algorithm and simulated annealing (HGS) for solving optimal measurement placement for power system state estimation. Other methods and more details such as the use of binary genetic algorithm are more explained in [5]. In [6] and [7] tabu search algorithms are explained in these publications, an iterative search that starts from some initial feasible solution and attempt to determine a better solution in the manner of hill- climbing algorithm, etc.…”

Section: A Backgroundmentioning

confidence: 99%

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Measurement loss effect on power system state estimation

Greyson

Oonsivilai

2009

2008 IEEE International Conference on Robotics and Biomimetics

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The main objective of this research work is to classify measurements units in optimal measurement environments in the power system network. Firstly, the use of singular value decomposition is used to find the optimal measurements placement. This is the optimum environment where each measurement unit is given weight upon its effect on the state estimation accuracy. Loss of the measurement unit can be due to the bad data received and the measurement is discarded or not telemetered due to the communication link error. In this case the respective relative error in state estimation is obtained by using MATLAB simulation. The higher weighted as considered to be critical and less weighted are considered non critical measurements are identified in the power system. In this paper related quantities that are more affected are identified.Index Terms -Measurement placement, accuracy, measurement weight (index), state estimation, critical measurements.

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Section: Illustration Of Methods Usedmentioning

confidence: 99%

Section: A Backgroundmentioning

confidence: 99%

Measurement loss effect on power system state estimation

Greyson

Oonsivilai

2009

2008 IEEE International Conference on Robotics and Biomimetics

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“…Under observability criterion, it is required to have number of measurements, m greater than or equal to the number of states (n). For N bus system, considering a slack bus where phase angle of the bus is known, state variables becomes (2N-1), therefore m • 2N-1 [1], [3], [4], [5], [6], [10] and [14].…”

Section: A Backgroundmentioning

confidence: 99%

State estimation using extended SEMM in power system

Oonsivilai

Greyson

2009

2009 International Conference on Sustainable Power Generation and Supply

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In this paper, power system state estimator (SE) solution using extended state estimation matrix measurements (SEMM) is presented. Within an optimum measurement placement environment, the extended SEMM is formed to enhance the state estimator solution in accuracy. The main idea is to maintain the readings from available measurement units to extend the new SEMM. This is determined by using the correlation and/or regression analysis of the new measurements and the available measurements. Absolute voltage error is used to analyze the system performance for IEEE 14-bus and IEEE 30bus testing systems where the estimation errors are compared for both the conventional method and extended SEFM method.Index Terms-accuracy, coefficient of determinations, extended SEMM, extended measurements, optimal environment, power systems, state estimation.

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“…To improve placement quality, a suitable procedure to perform HSE such as singular value decomposition (SVD) which s a useful tool for this task, should be defined [16][17][18][19][20][21]. One of the advantages of SVD approach is that it does not require the whole network system to be observable prior to estimation.…”

Section: Introductionmentioning

confidence: 99%