A Framework for Phasor Measurement Placement in Hybrid State Estimation Via Gauss–Newton

Li, Xiao; Scaglione, Anna; Chang, Tsung-Hui

doi:10.1109/tpwrs.2013.2283079

Cited by 47 publications

(39 citation statements)

References 47 publications

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“…However, they indicate that the inconsistency of PSO method may affect the quality of computation; therefore, the authors for developing this method recommend more research. Recently, the Gauss-Newton (GN) algorithm has been used in two studies by the same team [28], [29] for finding the optimal PMUs placement for a hybrid state estimation by considering the accuracy and the convergence of SE process. GN method is used in these studies for solving the iterative SE algorithm efficiently.…”

Section: Khandelwal Et Al International Journal Of Emerging Researcmentioning

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: Khandelwal Et Al International Journal Of Emerging Researcmentioning

confidence: 99%

Recent Development in Power System Dynamic State Estimation

Khandelwal¹,

Saxena²,

Tandon³

2017

IJERMT

View full text Add to dashboard Cite

show abstract

“…Since is Gaussian, is also Gaussian with covariance matrix , with , and the conditional pdf of (10) given and is (11) …”

Section: A Pmu Measurements With Sampling Errorsmentioning

confidence: 99%

“…However, in practice there are only sporadic PMUs deployed in the power grid due to expensive installation costs. In spite of this, through careful placement of PMUs [9]- [11], it is still possible to make the power state observable. As PMU measurements are linear functions of power states in rectangular coordinates, once the observability requirement has been satisfied, the network state can be obtained by centralized linear least-squares [12].…”

Section: Introductionmentioning

confidence: 99%

Distributed Hybrid Power State Estimation Under PMU Sampling Phase Errors

et al. 2014

IEEE Trans. Signal Process.

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Phasor measurement units (PMUs) have the advantage of providing direct measurements of power states. However, as the number of PMUs in a power system is limited, the traditional supervisory control and data acquisition (SCADA) system cannot be replaced by the PMU-based system overnight. Therefore, hybrid power state estimation taking advantage of both systems is important. As experiments show that sampling phase errors among PMUs are inevitable in practical deployment, this paper proposes a distributed power state estimation algorithm under PMU phase errors. The proposed distributed algorithm only involves local computations and limited information exchange between neighboring areas, thus alleviating the heavy communication burden compared to the centralized approach. Simulation results show that the performance of the proposed algorithm is very close to that of centralized optimal hybrid state estimates without sampling phase error.

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“…Recently, the Gauss-Newton (GN) algorithm has been used in two studies by the same team [26], [27] for finding the optimal PMUs placement for a hybrid state estimation by considering the accuracy and the convergence of SE process. GN method is used in these studies for solving the iterative SE algorithm efficiently.…”

Section: Accuracy Requirementsmentioning

confidence: 99%

A review on phasor measurement units placement for state estimation studies

Abood

Sreeram

2014

2014 Australasian Universities Power Engineering Conference (AUPEC)

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State estimation operation depends on the measurement numbers, types and locations. Therefore, the adoption of an efficient placement method for selecting the required numbers of meters and their location is an essential process for power systems monitoring. In fact, the Phasor measurement Unit (PMU) is, currently, one of the most efficient devices for measuring real-time electrical quantities. Consequently, this paper aims to present the main categories for PMUs placement strategies according to the objectives of PMUs employment. The PMUs placement strategy should take into consideration 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. A review of literatures that address PMUs placement methods is presented in this paper in addition to suggestions for developing the PMUs placement methods to be more feasible for modern power networks.

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A Framework for Phasor Measurement Placement in Hybrid State Estimation Via Gauss–Newton

Cited by 47 publications

References 47 publications

Recent Development in Power System Dynamic State Estimation

Recent Development in Power System Dynamic State Estimation

Distributed Hybrid Power State Estimation Under PMU Sampling Phase Errors

A review on phasor measurement units placement for state estimation studies

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