Optimal Meter Placement for Robust Measurement Systems in Active Distribution Grids

Liu, Junqi; Ponci, Ferdinanda; Monti, Antonello; Muscas, Carlo; Sulis, Sara

doi:10.1109/tim.2013.2295657

Cited by 131 publications

(92 citation statements)

References 28 publications

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“…This follows the recommendation in [23]. We find that the 0.7 crad of [22] is not to be trustworthy for our system. In our simulation, the criterion for |AE| max is defined as 0.07 crad.…”

Section: Dsse Evaluation Metrics and Criteriasupporting

confidence: 69%

“…In [21], the performance of DSSE is acceptable if the |RME| max and the maximum absolute relative voltage angle error of more than 95% of cases are under 1% and 5% respectively. In [22], when more than 95% of cases' |RME| max and |AE| max are under 0.7% and 0.7 crad the state estimation performance is satisfactory, where 1(crad)=0.01(rad). In [9], a 0.6% threshold of |RME| max is used to determine if the DSSE result is satisfactory.…”

Section: Dsse Evaluation Metrics and Criteriamentioning

confidence: 95%

See 1 more Smart Citation

Impact of Smart Metering Data Aggregation on Distribution System State Estimation

Chen

Kaleshi

Fan

et al. 2016

IEEE Trans. Ind. Inf.

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“…This follows the recommendation in [23]. We find that the 0.7 crad of [22] is not to be trustworthy for our system. In our simulation, the criterion for |AE| max is defined as 0.07 crad.…”

Section: Dsse Evaluation Metrics and Criteriasupporting

confidence: 69%

Section: Dsse Evaluation Metrics and Criteriamentioning

confidence: 95%

Impact of Smart Metering Data Aggregation on Distribution System State Estimation

Chen

Kaleshi

Fan

et al. 2016

IEEE Trans. Ind. Inf.

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“…An upgrade of the MS is mandatory in order to improve the monitoring and to implement SDN automation. Placement methods should consider both economical and accuracy constraints: for example, both SM and PMU are used in [63,64], considering also possible measurement loss, metrological degradation or network parameter uncertainty [65], while traditional instruments are used in [66,67]. Nevertheless, the DSSE will rely also on pseudo-measurements, which are power consumption/generation data that allow extending the available information on loads/generators and making the system observable jointly with the real-time measurements collected from the field.…”

Section: Distribution System State Estimationmentioning

confidence: 99%

Multidisciplinary Approach for the Development of Smart Distribution Networks

Ghiani¹,

Serpi²,

Pilloni³

et al. 2018

Preprint

Self Cite

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Electric power systems are experiencing relevant changes involving the growing penetration of distributed generation and energy storage systems, the introduction of electric vehicles, the management of responsive loads, the proposals for new energy markets and so on. Such evolution is pushing for a paradigm shift: the management must move from traditional planning and manual intervention to full "smartization" of medium and low voltage networks. Peculiarities and criticalities of future power distribution networks originate from the complexity of the system that includes both the physical aspects of electric networks and the cyber aspects, like data elaboration, feature extraction, communication, supervision and control; only fully integrated advanced monitoring systems can foster this transition towards network automation. The design and development of such future networks require distinct kinds of expertise in the industrial and information engineering fields. In this context, this paper provides a comprehensive review of current challenges and multidisciplinary interactions in the development of smart distribution networks.

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“…In this paper, the best distribution SE is achieved by minimizing the difference between the measured values and calculated values. Recently, the research in [35] reused the Genetic algorithm as an intelligent technique for finding the optimal PMU placement for the state estimation of active distribution systems. In this research, both the accuracy constraint the measurements deficiency is studied in addition to the effect of the DGs on the power flow.…”

Section: Khandelwal Et Al International Journal Of Emerging Researcmentioning

confidence: 99%

“…For achieving the optimal placement, Monte Carlo method is used in addition to Genetic algorithm. Despite the cost of PMUs, more measuring devices and PMUs should be used for ensuring the required accuracy for robust SE [35].…”

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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Optimal Meter Placement for Robust Measurement Systems in Active Distribution Grids

Cited by 131 publications

References 28 publications

Impact of Smart Metering Data Aggregation on Distribution System State Estimation

Impact of Smart Metering Data Aggregation on Distribution System State Estimation

Multidisciplinary Approach for the Development of Smart Distribution Networks

Recent Development in Power System Dynamic State Estimation

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