Evaluating Performance of Wireless Sensor Network in Realistic Smart Grid Environment

Nassef, Laila; Elhebshi, Remah; Jose, Linta

doi:10.5121/ijwmn.2018.10303

Cited by 4 publications

(5 citation statements)

References 10 publications

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“…The smart grid enables two‐way communication, a low‐cost network, and a power delivery network in an electric system. Some of the important applications of using smart grids are remote monitoring for power systems, fraud detection in data transmission, automatic metering, controlling load, demand response, distribution automation, and diagnosis of faults in transmission lines 9 . The performance of the electric power system is ensured by smart grids instead of smart meters, communication technology, renewable energy generation, and modern sensing devices.…”

Section: Introductionmentioning

confidence: 99%

“…Some of the important applications of using smart grids are remote monitoring for power systems, fraud detection in data transmission, automatic metering, controlling load, demand response, distribution automation, and diagnosis of faults in transmission lines. 9 The performance of the electric power system is ensured by smart grids instead of smart meters, communication technology, renewable energy generation, and modern sensing devices. Due to the failures of equipment, natural accidents, disasters, and capacity limitations in the electric system, it is important to maintain the efficiency, safety, and reliability of sensed data transmission.…”

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confidence: 99%

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Automatic fault identification in WSN‐based smart grid environment

Murthy

Mahadevaswamy²

2022

Int J Communication

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Wireless sensor network (WSN) plays a vital role in the smart grid (SG) environment. Due to the fault tolerance characteristics, cost reduction, and large-scale convergence, SG introduces many unique challenges caused by system and functional devices. To solve this problem, a WSN-based SG network is used to identify faults. During data transmission, faulty nodes occur in the transmission line. The node failures, calibration, network failures, low battery, dead nodes, environmental changes, software failures, and so on lead to the interruption in data delivery and spoil the entire WSN-based SG network. In order to tackle these problems, the new WSN model is designed to detect the faults in the transmission line based on the SG environment. This paper uses the Adaptive Zigbee-Aquila communication protocol (AZACP) to find the optimal shortest path for transferring data. AZACP finds the shortest optimal path for transmitting the sensed data to the base station with low cost and less time consumption. Fault detection automatically identifies the fault in the transmission line and isolates the faulty nodes to ensure efficient data transmission in WSN. Here, enhanced recurrent equilibrium neural network (ERENN) is introduced to identify the fault in data transmission. The proposed approach provides better performance in terms of evaluating performance metrics like throughput, delay, reliability, average residual energy, number of total transmissions, network lifetime, efficiency, and bit error rate (BER).

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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Automatic fault identification in WSN‐based smart grid environment

Murthy

Mahadevaswamy²

2022

Int J Communication

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“…The recent technological developments in wireless technologies have enabled the deployment of the smart grid [1], which is the next generation of electrical power grid proposed to solve problems associated with traditional power grid [2]. The massive increase in diverse applications have resulted in exponential increase in demand for spectrum which have created immediate need to make available new licensed and/or unlicensed spectrum bands [3]. Wireless Sensor Networks (WSNs) have been considered as a promising technology to enhance capabilities of monitoring, control, and maintenance of the entire smart grid from power generation to transmission, distribution and consumption [4].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy based Reliable Cooperative Spectrum Sensing for Smart Grid Environment

Nassef¹,

Alhebshi²

2020

IJACSA

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The huge demand for spectrum has created immediate need to make available new licensed and/or unlicensed spectrum bands to satisfy the explosive growth of spectrum demands and to satisfy the quality of service requirements of diverse applications. Spectrum shortage and harsh environment have become a challenging bottleneck to achieve reliable communications in the smart grid. Cognitive radio is the emerging technology to achieve both spectrum and reliability awareness. Cooperative spectrum sensing takes advantage of spatial diversity to reduce the impact of receiver uncertainty. However, the harsh smart grid environments limit advantageous of cooperation due to variations of signal to noise ratio on which energy detection technique depends on. This paper proposes a reliable spectrum detection for a cluster based cooperative spectrum sensing in harsh smart grid environment, where cognitive cluster heads and a centralized cognitive radio based fusion center are deployed to solve both spectrum and reliability problems. The proposed fuzzy inference system is based on three fuzzy descriptors of energy difference, link quality, and local probability of detection. The results show the superiority of proposed fuzzy based fusion scheme to enhance accuracy of spectrum decision in harsh environment.

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“…) cos ( min t energy Function Fitness + = (9) The node should satisfy the minimum energy consumption and minimum cost requirement is considered as best path for data transmission. By calculating the fitness function, the optimum shortest path for data transmission is selected.…”

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confidence: 99%

Automatic Fault Identification in WSN Based Smart Grid Environment

Rekha

Mahadevaswamy²

2023

Preprint

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Wireless Sensor Network (WSNs) plays a vital role in smart grid (SG) environment. Due to the fault tolerance characteristics, cost reduction and large scale convergence. SG introduce many unique challenges caused by system and functional devices. To solve this problem, a WSN based SG network is used for identifying faults. During the process of data transmission, faulty nodes are occurred in the transmission line. Due to the nodes failures, calibration, network failures, low battery, dried sensors, environmental changes and software failures etc., which leads to the interruption in data delivery and spoil the entire WSN based SG network. In order to tackle these problems, the new WSN model is designed to detect the faults in the transmission line based on the SG environment. In this paper, Adaptive Zigbee-Aquila communication protocol (AZACP) is used to find the optimal shortest path for transferring data. AZACP finds the shortest optimal path for transmitting the sensed data to base station with low cost and less time consumption. Fault detection is the process of automatically identifying the fault in the transmission line and isolate the faulty nodes to ensure the efficient data transmission in WSN. Here, Enhanced Recurrent Equilibrium Neural Network (ERENN) is introduced to identify the fault in data transmission. It recognize the strength of the signal to transmit the sensed data and checks the quality of the data in transmission line between the nodes. The proposed approach is implemented in MATLAB software and compared with existing approaches like Adaptive Error Control (AEC), Gallager Humble Spira (GHS), Genetic Algorithm-Ticket Based Routing (GA-TBR), Improved Grid based Routing and Charging (IGRC) and Emperor Penguin Optimized Self-healing Strategy (EPOSH). The proposed approach provides better performance in terms of evaluating performance metrics like throughput, delay, reliability, average residual energy, number of total transmission, network lifetime, efficiency and Bit Error Rate (BER).

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Evaluating Performance of Wireless Sensor Network in Realistic Smart Grid Environment

Abstract: ABSTRACT

Cited by 4 publications

References 10 publications

Automatic fault identification in WSN‐based smart grid environment

Automatic fault identification in WSN‐based smart grid environment

Fuzzy based Reliable Cooperative Spectrum Sensing for Smart Grid Environment

Automatic Fault Identification in WSN Based Smart Grid Environment

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