Measurement placement method for power system state estimation: part I

Madtharad, Chakphed; Premrudeepreechacharn, S.; Watson, N.R.; Saenrak, D.

doi:10.1109/pes.2003.1267399

Cited by 24 publications

(26 citation statements)

References 14 publications

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“…In this field, a heuristic sequential elimination method has used by Rakpenthai et al [13,] for finding the minimal number of PMUs. In fact, Madtharad et al [14] used the same method previously with the conventional measurements. This method depends on the condition number of Jacobian matrix where the measurement location that has a minimum condition number is discarded.…”

Section: Pmu's Placement Approaches With Simulation Resultsmentioning

confidence: 99%

Recent Development in Power System Dynamic State Estimation

Khandelwal¹,

Saxena²,

Tandon³

2017

IJERMT

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Abstractue to the expansion of power systems there is a challenge of real time monitoring and control by placing important parameters at various points for efficient functioning of the power system. There can be drastic changes during hourly load fluctuations, component outages, or network switching. In such conditions, the inclusion of predicted values could degrade the power system state estimation hence the need of state estimation is must. Synchronized Phasor Measurement Units (PMU) are being placed along with the GPS (global positioning system) to enhance the monitoring of the power system. It depends on the measurement numbers, types and locations. This paper aims to present the main categories for PMUs placement strategies which considers main factors that required for enhancing the performance of a state estimation process such as reliability, accuracy and the special requirements of the modern distribution grid.Keywords-Phasor Measurement Unit, Global Positioning Systems, State Estimations, Weighted Least Square Method, Observability I. INTRODUCTIONThe enlargement of power system has made more and more intricacy so real time checking and control became very significant for the consistent operation of power system and which the Energy Management System (EMS) cures. In this system, the State estimation forms the spine of the energy management system by providing a database of the real time state of the system for using in other EMS functions [1]. Therefore, efficient and accurate state estimation is a prerequisite for an efficient and consistent operation of the power system.State estimation is a technique where the voltage, magnitude and angles at all the buses of a power system are recorded from the existing measurements. These measurements are obtained through a suitable communication standard and are processed through state estimation procedures, to obtain the voltage magnitude and angle at all the buses of the network under consideration. As we know that the power system is a quasi-static system, hence changes slowly but progressively which are driven by dynamic load so the generations also have to be adjusted accordingly, which in turn changes the flows and injections across the system, which makes the entire system dynamic in nature. Therefore, in order to have a continuous monitoring of the power system, we must perform the state estimation at regular interval of time. However, by the expansion of power system, along with addition of generations and loads, this system becomes more complex for such monitoring, which requires heavy computing resources. Hence, static state estimators may not capably capture this dynamic behavior of the power system. This lead to the necessity of another set of procedures called the "Dynamic State Estimation" (DSE) techniques, where we use the actual physical modeling of the time varying nature of the power system. These procedures have dual advantages of being more accurate and possessing the ability to predict the state of the system at next step. That is, from the ...

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Section: Pmu's Placement Approaches With Simulation Resultsmentioning

confidence: 99%

Recent Development in Power System Dynamic State Estimation

Khandelwal¹,

Saxena²,

Tandon³

2017

IJERMT

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show abstract

“…In this paper, in order to obtain the optimum measurement unit placement, the algorithm proposed in [2] and [15] is used, however in this paper the number of measurements to be obtained is N 2 measurements instead of the proposed 1 2 − N so that the removal of the measurement to obtain the relative error does not affect the observability of the network, and scale of the matrix i.e. the remaining 1 2 − N is still available in the network.…”

Section: Illustration Of Methods Usedmentioning

confidence: 99%

“…The authors propose an algorithm of optimal meter placement for the state estimation of power systems, which minimizes the total investment cost subject to a constraint of state estimation accuracy on the basis of the sequential meter addition/elimination method by reflecting the cost of individual meter installation and its contribution to the state estimation accuracy in the probabilistic sense. As it has been presented in [2] and [3], algorithm used for measurement placement for power system nonlinear state estimation has a reduced number of possible combinations to be considered using condition number method. The gain or measurement matrix has to be formulated first.…”

Section: A Backgroundmentioning

confidence: 99%

“…Same as [2] the measurement covariance matrix in (4) is determined by assuming Gaussian distributions with standard deviation of 0.001 for error in injection measurements (active and reactive), and 0.04 in voltage measurements. Then MATLAB is used to simulate the algorithm and (5) is applied to obtain the values for residuals in state variables.…”

Section: A Application Examplesmentioning

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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“…Static state estimator requires a set of real-time measurements consisting of bus injections, line flows and bus voltages as input which are collected through the Supervisory Control and Data Acquisition System (SCADA) that redundantly observes the system state along with a topology processor that determines the system topological model based on the telemetering of circuit breaker statuses [8]. It may occur that the errors affecting state estimates are not compatible with their standard deviations.…”

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confidence: 99%

Graph Theoretic Approach to Solve Measurement Placement Problem for Power System State Estimation

Motiyani¹,

Chudasama²

2014

IJAREEIE

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ABSTRACT:The meter placement problem involves selection of meters, number, type and place. This paper describes graph theoretic approach to solve the measurement placement problem for power system state estimation. The measurement configuration is designed for IEEE 14 bus system. Utilizing the proposed measurement system configuration the network observability is tested for P-δ and Q-V models by triangular factorization of the gain matrix. The designed metering scheme assures network observability and accomplishes better accuracy and bad data processing requirements for state estimator. The developed method can be used as measurement systems planning tool for power system state estimators.

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Measurement placement method for power system state estimation: part I

Cited by 24 publications

References 14 publications

Recent Development in Power System Dynamic State Estimation

Recent Development in Power System Dynamic State Estimation

Measurement loss effect on power system state estimation

Graph Theoretic Approach to Solve Measurement Placement Problem for Power System State Estimation

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