Analysis of communication schemes for Advanced Metering Infrastructure (AMI)

Bian, Desong; Kuzlu, Murat; Pipattanasomporn, Manisa; Rahman, Saifur

doi:10.1109/pesgm.2014.6939562

Cited by 65 publications

(31 citation statements)

References 11 publications

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“…Parameters of the 4-bus microgrid are listed in Table I and II. The latency in the microgrid network can be addressed in following aspects: (1) latency from system status change and the response of the measurement, which may vary from 0.035 to 1.038s according to IEEE Standard C37.118.1™ if PMU serves as the measuring device; (2) latency from data processing, which may occurs on both sender and receiver sides, and may include algorithm processing, data compression, data encryption, etc., which usually costs 0.1 to 0.3s [28]; (3) latency from information traveling between the sender and the receiver within communication networks which is around 0.3s normally [29]. In conclusion, a reasonable approximation of the total latency in the secondary control is from 0.5s to 2.5s [30].…”

Section: Fig 3 An Exemplary 4-bus Microgridmentioning

confidence: 99%

Small-Signal Stability Analysis and Performance Evaluation of Microgrids Under Distributed Control

Yan

Shi

Bian

et al. 2019

IEEE Trans. Smart Grid

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Distributed control, as a potential solution to decreasing communication demands in microgrids, has drawn much attention in recent years. Advantages of distributed control have been extensively discussed, while its impacts on microgrid performance and stability, especially in the case of communication latency, have not been explicitly studied or fully understood yet. This paper addresses this gap by proposing a generalized theoretical framework for small-signal stability analysis and performance evaluation for microgrids using distributed control. The proposed framework synthesizes generator and load frequency-domain characteristics, primary and secondary control loops, as well as the communication latency into a frequency-domain representation which is further evaluated by the generalized Nyquist theorem. In addition, various parameters and their impacts on microgrid dynamic performance are investigated and summarized into guidelines to help better design the system. Case studies demonstrate the effectiveness of the proposed approach.

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Section: Fig 3 An Exemplary 4-bus Microgridmentioning

confidence: 99%

Small-Signal Stability Analysis and Performance Evaluation of Microgrids Under Distributed Control

Yan

Shi

Bian

et al. 2019

IEEE Trans. Smart Grid

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“…On the other hand, forecasting of load at lower voltage level is rarely conducted due to the lack of highquality data. Fortunately, the massive deployment of smart meters creates the opportunities to forecast and analyze customer consumption at a much lower level with much higher granularity [14]. By aggregating individual smart meter readings, loads at different aggregation levels can be obtained and analyzed.…”

Section: Introductionmentioning

confidence: 99%

Short-term Load Forecasting at Different Aggregation Levels with Predictability Analysis

Peng

Wang

et al. 2019

2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia)

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Short-term load forecasting (STLF) is essential for the reliable and economic operation of power systems. Though many STLF methods were proposed over the past decades, most of them focused on loads at high aggregation levels only. Thus, lowaggregation load forecast still requires further research and development. Compared with the substation or city level loads, individual loads are typically more volatile and much more challenging to forecast. To further address this issue, this paper first discusses the characteristics of small-and-medium enterprise (SME) and residential loads at different aggregation levels and quantifies their predictability with approximate entropy. Various STLF techniques, from the conventional linear regression to state-of-the-art deep learning, are implemented for a detailed comparative analysis to verify the forecasting performances as well as the predictability using an Irish smart meter dataset. In addition, the paper also investigates how using data processing improves individual-level residential load forecasting with low predictability. Effectiveness of the discussed method is validated with numerical results.Index Terms-Deep learning, machine learning, smart meter, short-term load forecasting, load aggregation level, predictability I.

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“…It is almost impossible to tap a signal being sent on a fiber without physically cutting it because the fibers have no effect of electro-magnetic fields. Also, because of non-conductivity, cables are almost passive to any kinds of interferences [21].…”

Section: Advantages Of Ofcmentioning

confidence: 99%

A survey on smart grid metering infrastructures: Threats and solutions

Mahmud

Vallakati

Mukherjee

et al. 2015

2015 IEEE International Conference on Electro/Information Technology (EIT)

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Without a reliable metering and communication infrastructure, the smart grid could become a catastrophe to national security and economy. A true smart grid infrastructure should detect all existing and predict future threats through intrusion detection methods. Smart grids are susceptible to various physical and cyber-attack as a result of communication, control and computation vulnerabilities employed in the grid. The paper provides a comprehensive study on types of threats and solutions on smart grid communication and metering infrastructures. As a part of this survey, the smart grid metering infrastructures susceptibilities and recommended remedial actions are identified. In addition, the paper details types of known attacks on existing metering infrastructure and defensive methodologies.

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Analysis of communication schemes for Advanced Metering Infrastructure (AMI)

Cited by 65 publications

References 11 publications

Small-Signal Stability Analysis and Performance Evaluation of Microgrids Under Distributed Control

Small-Signal Stability Analysis and Performance Evaluation of Microgrids Under Distributed Control

Short-term Load Forecasting at Different Aggregation Levels with Predictability Analysis

A survey on smart grid metering infrastructures: Threats and solutions

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