Smart Metering Technology

Coelho, Paulo; Gomes, Mário; Moreira, Carlos

doi:10.1007/978-3-319-98687-6_4

Cited by 7 publications

(4 citation statements)

References 109 publications

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“…This equipment has essential roles in reaching the intelligent and precise operation of microgrids, in case their outputs are reliable and secure. Thus the security of smart meters has a direct impact on the efficient performance of microgrids [2].…”

Section: A Motivationmentioning

confidence: 99%

Securing Microgrid Optimal Energy Management Using Deep Generative Model

et al. 2021

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This paper investigates the effect of data integrity attacks on the central control of the microgrids (MGs), which can lead to severe blackouts and load shedding. It assesses this cyber attack from the steady state and optimal scheduling point of view. In order to stop the cyber hacking, a new deep learning-based framework has been developed based on the generative adversarial networks (GANs). In this framework, two networks compete with each other, wherein the first network generates fake data, and the second one is responsible for the data classification. In order to get into the most optimal features, a new optimization method based on a modified teaching-learning based optimization (TLBO) algorithm is also devised to reinforce the GAN model and help a better matching training process. In addition, a new modification is introduced for TLBO to avoid premature convergence and provide high population diversity. To show the effectiveness of the proposed framework, a real dataset of several smart metering devices in a MG has been tested. Results illustrate the high performance of the proposed framework, comparing to the well-known conventional detection frameworks with hit rate of 93.11%, miss rate of 6.89%, false alarm rate of 7.76% and correct reject rate of 92.24%.

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Section: A Motivationmentioning

confidence: 99%

Securing Microgrid Optimal Energy Management Using Deep Generative Model

et al. 2021

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“…Because of this, PLC does not always seem to be the most suitable candidate for a stable connection for high-speed networks. The article does not focus on NPLC, mainly due to limited transmission speed, although NPLC systems are considered in smart grid and smart metering applications [ 1 ], presented in articles [ 2 , 3 , 4 , 5 ]. NPLC technology is considered in the related works section.…”

Section: Introductionmentioning

confidence: 99%

Broadband Power Line Communication for Integration of Energy Sensors within a Smart City Ecosystem

Sláčik

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Rusz

et al. 2021

Sensors

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The popularity of the Power Line Communication (PLC) system has decreased due to significant deficiencies in the technology itself, even though new wire installation is not required. In particular, regarding the request for high-speed throughput to fulfill smart-grid requirements, Broadband Power Line (BPLC) can be considered. This paper approaches PLC technology as an object of simulation experimentation in the Broadband Power Line Communication (BPLC) area. Several experimental measurements in a real environment are also given. This paper demonstrates these experimental simulation results as potential mechanisms for creating a complex simulation tool for various PLC technologies focusing on communication with end devices such as sensors and meters. The aim is to demonstrate the potential and limits of BPLC technology for implementation in Smart Grids or Smart Metering applications.

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“…Power Line Communications (PLC) are expected to be a key component of smart-grids, allowing grid usage to be monitored without adding extensive infrastructure. PLC systems use existing power lines as two-way communication channels and can be implemented in a broad range of scenarios, including home/office networks, device-specific billing, smart energy management, automatic meter reading, and power outage detection [1][2][3][4][5][6]. PLC systems are classified into three categories based on bandwidth: ultra narrowband (UNB) systems have frequencies ranging from 0.3-3 kHz, narrowband (NB) systems have a frequency range of 3-500 kHz, and broadband (BB) systems have frequencies of 1.8-250 MHz [7].…”

Section: Introductionmentioning

confidence: 99%

Sensitivity-Informed Bayesian Inference for Home PLC Network Models with Unknown Parameters

2021

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Bayesian inference is used to calibrate a bottom-up home PLC network model with unknown loads and wires at frequencies up to 30 MHz. A network topology with over 50 parameters is calibrated using global sensitivity analysis and transitional Markov Chain Monte Carlo (TMCMC). The sensitivity-informed Bayesian inference computes Sobol indices for each network parameter and applies TMCMC to calibrate the most sensitive parameters for a given network topology. A greedy random search with TMCMC is used to refine the discrete random variables of the network. This results in a model that can accurately compute the transfer function despite noisy training data and a high dimensional parameter space. The model is able to infer some parameters of the network used to produce the training data, and accurately computes the transfer function under extrapolative scenarios.

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Smart Metering Technology

Cited by 7 publications

References 109 publications

Securing Microgrid Optimal Energy Management Using Deep Generative Model

Securing Microgrid Optimal Energy Management Using Deep Generative Model

Broadband Power Line Communication for Integration of Energy Sensors within a Smart City Ecosystem

Sensitivity-Informed Bayesian Inference for Home PLC Network Models with Unknown Parameters

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