Power Distribution System Synchrophasor Measurements With Non-Gaussian Noises: Real-World Data Testing and Analysis

Huang, Can; Thimmisetty, Charanraj; Chen, Xiao; Stewart, Emma; Top, Philip; Korkali, Mert; Donde, Vaibhav; Tong, Charles; Min, Liang

doi:10.1109/oajpe.2021.3081503

Cited by 18 publications

(6 citation statements)

References 20 publications

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“…However, the grid conditions can influence the error models. Similar results are obtained in [10], where, considering offline and online measurements, non-Gaussian distribution is also found for voltage synchrophasor errors. Moreover, [10] proposes the Gaussian Mixture Models (GMMs) for modeling the distribution of the synchrophasor measurement errors.…”

Section: Introductionsupporting

confidence: 85%

“…Similar results are obtained in [10], where, considering offline and online measurements, non-Gaussian distribution is also found for voltage synchrophasor errors. Moreover, [10] proposes the Gaussian Mixture Models (GMMs) for modeling the distribution of the synchrophasor measurement errors. In [21], an analysis of the PMU measurements from a calibration process is described, which considers the influence of the two classes of accuracy proposed in [7].…”

Section: Introductionsupporting

confidence: 85%

“…Indeed, the systematic contributions do not change in different repeated observations, and thus, if properly identified, their effects can be compensated for [9]. On the contrary, random errors, which are introduced by the unpredictable variations in the behavior of the measurement devices, play a decisive role in determining the distribution of the measurement error to be used in state estimation procedures [10]. It is important to recall that instrument transformers can give a significant contribution, mainly systematic and depending on their accuracy class, to the errors of the whole PMU measurement chain.…”

Section: Introductionmentioning

confidence: 99%

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Statistical Behavior of PMU Measurement Errors: An Experimental Characterization

Castello

Muscas

Pegoraro

2022

IEEE Open J. Instrum. Meas.

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Different power system applications based on synchrophasors measured in different nodes of the electric grid require information about the statistical distribution of the errors introduced by the Phasor Measurement Units (PMUs). The performance of these applications can be significantly affected by possible incorrect assumptions. The Gaussian distribution has been historically assumed in most of the approaches, but some more recent studies suggest the possibility of considering different distributions for a more accurate modeling of the actual situation. In this paper, proper statistical tools applied to the results achieved through a high performance experimental test system are proposed to assess the statistical distribution of PMU errors under controlled steady-state conditions, thus providing a basis for defining suitable models to be used in the specific applications.

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Section: Introductionsupporting

confidence: 85%

Section: Introductionsupporting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

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Statistical Behavior of PMU Measurement Errors: An Experimental Characterization

Castello

Muscas

Pegoraro

2022

IEEE Open J. Instrum. Meas.

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“…In recent years, several new power system technologies have been introduced which can support feeder impedance estimation by providing high-quality power grid data. For example, µPMUs are designed to measure time-stamped voltage phasors, current phasors, and frequency with high precision and high frequency, enabling improved diagnostics through affordable, high-resolution metering [10]- [12].…”

Section: Introductionmentioning

confidence: 99%

Power Distribution System Characterization With Active Probing: Real-World Testing and Analysis

Huang

Burroughs

Klauber

et al. 2023

IEEE Open J. Power Energy

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Power distribution systems are traditionally characterized using passive means such as parameter estimation and topology identification. This work investigates an active way to test and characterize dynamically changing distribution systems. The method actively perturbs local distribution networks with programmable pulses using a Grid Resonance Probe (GRP) and observes the response using a micro-phasor measurement unit (µPMU). After applying advanced data analytics, the perturbation and response measurements can reveal characteristics of distribution systems, such as feeder parameters and topologies. To validate the proposed approach, three case studies are executed on a real distribution system, including visualization, feeder connection identification, and feeder impedance estimation. Real data processing considerations and sensitivity analysis are also discussed. The proposed testing and analysis approach is expected to provide engineers a new solution to measure, parameterize, and characterize distribution systems.

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“…The mainstream measurement devices deployed at the distribution system are reviewed in the literature [7]- [9]. Among the various measurement devices, smart meters are the only measurement devices that are widely deployed between electric utilities and end users [10]- [12].…”

Section: Introductionmentioning

confidence: 99%

Smart Meter Pinging and Reading Through AMI Two-Way Communication Networks to Monitor Grid Edge Devices and DERs

Huang

Sun

Duan

et al. 2022

IEEE Trans. Smart Grid

Self Cite

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Today's power distribution system is changing to a power-electronics-enabled distribution system, especially with the increasing penetration of distributed energy resources (DERs). To monitor and manage those electronic devices and DERs at the grid edge, the advanced metering infrastructure (AMI) with twoway communications presents great potentials. In the literature, extensive research explores the upstream communication from smart meters to electric utilities (e.g., meter reading) but few examine the downstream communication from the utilities to smart meters (e.g., meter pinging). This paper discussed the AMI two-way communication and its recent industrial practice in the U.S., especially the ones about applying the smart meter pinging functionality to monitor grid-edge devices and DERs. This paper then developed the two-way communication model and the network calculus method to quantify the impact of the two-way communication on the AMI network. In the end, the proposed method is validated with ns-3 simulation using the modified 13-node test feeder and real-world feeder systems.

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Power Distribution System Synchrophasor Measurements With Non-Gaussian Noises: Real-World Data Testing and Analysis

Cited by 18 publications

References 20 publications

Statistical Behavior of PMU Measurement Errors: An Experimental Characterization

Statistical Behavior of PMU Measurement Errors: An Experimental Characterization

Power Distribution System Characterization With Active Probing: Real-World Testing and Analysis

Smart Meter Pinging and Reading Through AMI Two-Way Communication Networks to Monitor Grid Edge Devices and DERs

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