Key communication technologies, applications, protocols and future guides for IoT-assisted smart grid systems: A review

Qays, Md Ohirul; Ahmad, Iftekhar; Abu-Siada, A.; Hossain, Md. Liton; Yasmin, Farhana

doi:10.1016/j.egyr.2023.01.085

Cited by 64 publications

(16 citation statements)

References 97 publications

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“…This smart grid architecture uses both renewable and non-renewable types of resources. Renewable sources contain solar panels, wind turbines, biogas facilities, and bio-fuel power generation, while non-renewable sources include nuclear and thermal power stations [7].…”

Section: Smart Grid Architecture With Web Supportmentioning

confidence: 99%

The application of the internet of things technology in the smart grid

2024

ACE

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The development of electric power and the energy industry is an important guarantee for national economic development. With the explosion of population and the development of science and technology, the power system needs to be more intelligent and networked to facilitate management and optimization. In recent years, the Internet of Things (IoT) technology has continued to develop through the combination of Internet technology and sensor technology, and the fast network transmission speed as well as the miniaturization and refinement of sensing can help the construction of the smart grid. This paper analyzes the application of IoT technology in the smart grid, including different types of smart grid architecture and the practical application of the IoT technology in the whole process of power transmission. Besides, the paper also discusses different application links and network levels. At present, the IoT technology has been applied to the smart grid information collection, transmission, and processing process, participating in information management in the power station, power transmission, substation, and other links. However, due to the complexity of processing the massive data of the power grid and the multiple network connections during the transmission process, ensuring the security of information and networks has become a challenge. While realizing high-speed communication, it is necessary to ensure the security of users as well as the information communication and networks.

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Section: Smart Grid Architecture With Web Supportmentioning

confidence: 99%

The application of the internet of things technology in the smart grid

2024

ACE

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“…The proposed loT-based PV performance monitoring system can be shown in Figure 1. IoT-based monitoring system application as shown in Figure 1 is developed in 4 architectural layers, namely, perception layer, network layer, processing layer, and application layer [22]. The perception layer is related to sensors, actuators, A/D converters, and microcontrollers.…”

Section: Iot-based Pv Monitoring Systemmentioning

confidence: 99%

Real-Time Monitoring of Photovoltaic Systems and Control of Electricity Supply for Smart Micro Grid-PV using IoT

Iksan,

Purwanto,

Sutanto

2024

TEM Journal

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This paper aims to develop a photovoltaic (PV) performance monitoring system applied on a micro scale using the Internet of Things (IoT). Previous monitoring systems had limitations in platform flexibility, low-cost devices, hardware complexity, and stability of the data transfer process. For this reason, this research proposes an IoT architecture that uses Arduino devices, mini WIFI and an open-source platform, so that it can be easily developed further. This research also develops innovations in controlling the use of electrical energy sources from PV and utility networks. This monitoring system is applied to PV installations with a capacity of 1KW which is capable of monitoring electrical data in the form of current, voltage, power, energy and frequency obtained from PV panels, batteries, loads and electrical utilities. Monitoring data is displayed in a visual form that can be accessed via web-based and mobile applications. This research has developed an innovation in controlling the use of electrical energy sources from PV and Utility networks by using several sensors as parameters to determine the right time and condition in controlling the use of electrical resources.

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“…The most prominent component of AMI is smart meters. Smart meters increase the frequency of collection of energy consumption data, enabling advanced data analysis that was not possible before [ 6 ]. A smart meter records and transmits energy consumption of the customers at specific intervals for billing and management [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Smart Grid Security: An Effective Hybrid CNN-Based Approach for Detecting Energy Theft Using Consumption Patterns

Gunduz,

Das

2024

Sensors

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In Internet of Things-based smart grids, smart meters record and report a massive number of power consumption data at certain intervals to the data center of the utility for load monitoring and energy management. Energy theft is a big problem for smart meters and causes non-technical losses. Energy theft attacks can be launched by malicious consumers by compromising the smart meters to report manipulated consumption data for less billing. It is a global issue causing technical and financial damage to governments and operators. Deep learning-based techniques can effectively identify consumers involved in energy theft through power consumption data. In this study, a hybrid convolutional neural network (CNN)-based energy-theft-detection system is proposed to detect data-tampering cyber-attack vectors. CNN is a commonly employed method that automates the extraction of features and the classification process. We employed CNN for feature extraction and traditional machine learning algorithms for classification. In this work, honest data were obtained from a real dataset. Six attack vectors causing data tampering were utilized. Tampered data were synthetically generated through these attack vectors. Six separate datasets were created for each attack vector to design a specialized detector tailored for that specific attack. Additionally, a dataset containing all attack vectors was also generated for the purpose of designing a general detector. Furthermore, the imbalanced dataset problem was addressed through the application of the generative adversarial network (GAN) method. GAN was chosen due to its ability to generate new data closely resembling real data, and its application in this field has not been extensively explored. The data generated with GAN ensured better training for the hybrid CNN-based detector on honest and malicious consumption patterns. Finally, the results indicate that the proposed general detector could classify both honest and malicious users with satisfactory accuracy.

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Key communication technologies, applications, protocols and future guides for IoT-assisted smart grid systems: A review

Cited by 64 publications

References 97 publications

The application of the internet of things technology in the smart grid

The application of the internet of things technology in the smart grid

Real-Time Monitoring of Photovoltaic Systems and Control of Electricity Supply for Smart Micro Grid-PV using IoT

Smart Grid Security: An Effective Hybrid CNN-Based Approach for Detecting Energy Theft Using Consumption Patterns

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