Leveraging AMI Data for Distribution System Model Calibration and Situational Awareness

Peppanen, Jouni; Reno, Matthew J.; Thakkar, Mohini; Grijalva, Santiago; Harley, R.G.

doi:10.1109/tsg.2014.2385636

Cited by 94 publications

(59 citation statements)

References 25 publications

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“…To deal with the missing or inaccurate smart meter data, the paper [49] has used the pseudo measurement generator that utilizes a weighted average combination of historic data and interpolated/extrapolated data from previous or future measurements. Positive sequence equivalent models are used for parameter estimation.…”

Section: Smart Gridmentioning

confidence: 99%

Survey of wireless big data

Qian

Zhu

Zhang

2017

J. Commun. Inf. Netw.

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Wireless big data describes a wide range of massive data that is generated, collected and stored in wireless networks by wireless devices and users. While these data share some common properties with traditional big data, they have their own unique characteristics and provide numerous advantages for academic research and practical applications. This article reviews the recent advances and trends in the field of wireless big data. Due to space constraints, this survey is not intended to cover all aspects in this field, but to focus on the data aided transmission, data driven network optimization and novel applications. It is expected that the survey will help the readers to understand this exciting and emerging research field better. Moreover, open issues and promising future directions are also identified.

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Section: Smart Gridmentioning

confidence: 99%

Survey of wireless big data

Qian

Zhu

Zhang

2017

J. Commun. Inf. Netw.

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“…Studies [7,8,[10][11][12][13][14][15]17,18,24,25,27,29] show the application of this approach to validate proposed strategies or devices. In summary, the following are examples of typical applications for snapshot analysis and validation:…”

Section: Time-sequential Simulationmentioning

confidence: 99%

“…For instance, studies [7,9,[14][15][16][19][20][21][22][23][24][26][27][28]30] show that the information obtained from this approach is compelling evidence for design and corroboration in many applications. In summary, the following are growing uses of time-sequential simulations in smart grid studies: It is possible to classify several time-sequential simulation approaches in terms of the ratio of computation time over simulated time.…”

Section: Time-sequential Simulationmentioning

confidence: 99%

Statistical Inference for Visualization of Large Utility Power Distribution Systems

et al. 2017

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Electrical variable visualization has been widely applied to report the performance and effectiveness of novel devices and strategies in utility power distribution systems. Many graphical alternatives are useful to demonstrate critical characteristics of distribution systems such as voltage regulation or power flow. This visualization of electrical variables can also be an effective approach to analyze, compare and evaluate large-scale systems. However, there is a lack of generalized visualization strategies oriented to perform electrical validations of smart grid strategies in large distribution systems. In this paper, we show that the proposed probabilistic density evolution is a powerful resource for long-term time-sequential simulations. Examinations with an IEEE 8500 node test feeder shows that the proposed approach increases the circuit situational awareness and reduces the validation time. To illustrate this methodology, the dynamic voltage condition was simulated and analyzed to recognize the global effect of voltage regulating equipment. The results show an accurate and convenient support that can be interpreted at first glance. The proper use of long-term field measurements and short time-step simulations is a robust method for future grid research, such as designing an optimum operation of intelligent devices or diagnosing electrical interoperability issues in complex grids.

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“…A recent study [7] found the number of installed smart meters in the United States to have reached 45.8 million, which represents a penetration level of approximately 30%. Other researchers [8][9][10][11] established that AMI systems fail to record between 2.7% and 9.4% of meter readings in a given year.…”

Section: Introductionmentioning

confidence: 99%

Topology-Based Estimation of Missing Smart Meter Readings

Kodaira

Han

2018

Energies

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Smart meters often fail to measure or transmit the data they record when measuring energy consumption, known as meter readings, owing to faulty measuring equipment or unreliable communication modules. Existing studies do not address successive and non-periodical missing meter readings. This paper proposes a method whereby missing readings observed at a node are estimated by using circuit theory principles that leverage the voltage and current data from adjacent nodes. A case study is used to demonstrate the ability of the proposed method to successfully estimate the missing readings over an entire day during which outages and unpredictable perturbations occurred.

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Leveraging AMI Data for Distribution System Model Calibration and Situational Awareness

Cited by 94 publications

References 25 publications

Survey of wireless big data

Survey of wireless big data

Statistical Inference for Visualization of Large Utility Power Distribution Systems

Topology-Based Estimation of Missing Smart Meter Readings

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