Identification of Optimal Sensor Location Based on Trade-Off Approach to Improve Resiliency of Electricity Market in Smart Grid

Jena, Prasanta Kumar; Ghosh, Subhojit; Koley, Ebha

doi:10.1109/jsen.2021.3078267

Cited by 13 publications

(6 citation statements)

References 29 publications

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“…Keeping these points in mind, this review paper attempts to provide an in-depth analysis of all the latest developments in different types of sensor technologies and how they are responsible for fault detection in various transmission and distribution system equipment. This paper also demonstrates the evolution, development, and practical deployment of smart sensors [30] and wireless sensor networks for real time measurements, condition-based health monitoring, and predictive maintenance of the smart grid [2,31,32].…”

Section: Introductionmentioning

confidence: 91%

Sensor Technologies for Transmission and Distribution Systems: A Review of the Latest Developments

et al. 2022

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The transmission and distribution systems are essential in facilitating power flow from the source multiple loads over large distances with high magnitudes of voltages and currents. Hence, the monitoring and control of various components of these structures are crucial. Traditionally, this was implemented by sensing only the grid current and grid voltage parameters through coils, clamps, or instrument transformers. However, these have bulky structures that restrict them to the substation and have installation and maintenance issues due to their direct contact with high voltage conductors. Currently, the power grid is undergoing various developments e.g., penetration of renewable energy sources, remote control, and automation, bidirectional power flow, etc. These developments call for compact and energy-efficient sensors to sense multiple grid parameters such as the magnetic field data, temperature, humidity, acoustics, etc., to enable real time, wide area monitoring and the predictive maintenance of the power grid. The goal of this paper is to summarize the advancements in sensing technologies on transmission and distribution systems over a decade and to explain their role in the forthcoming expansion of the power grids. This paper aims to outline the current state-of-affairs of sensor technology as well as to fill research gaps by exploring their limitations.

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Section: Introductionmentioning

confidence: 91%

Sensor Technologies for Transmission and Distribution Systems: A Review of the Latest Developments

et al. 2022

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“…Jena et al in [17] considered the market domain of SG for estimating its resiliency. Particularly, the authors here used optimal sensor placement techniques for the SG communication network to analyze the network resiliency.…”

Section: Related Workmentioning

confidence: 99%

Analytical Design of Synchrophasor Communication Networks with Resiliency Analysis Framework for Smart Grid

et al. 2022

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The advent of synchrophasor technology has completely revolutionized the modern smart grid, enabling futuristic wide-area monitoring protection and control. The Synchrophasor Communication Network (SCN) is a backbone that supports communication of synchrophasor data among Phasor Measurement Units (PMUs) and Phasor Data Concentrators (PDCs). The operator at the control center can visualize the health of the smart grid using synchrophasor data aggregated at PDCs from several PMUs. Since the core of the SCN is the existing IP network as an underlying communication infrastructure, the synchrophasor data is subjected to attacks that can compromise its security. The attacks, such as denial-of-service (DoS), can result in degradation of performance and even can disrupt the entire operation of the smart grid, if not controlled. Thus, a resilient SCN is a pertinent requirement in which the system continues to operate with accepted levels of performance even in response to the DoS. This article endeavors to propose a comprehensive resiliency framework for the SCN with enhanced resiliency metrics based on hardware reliability and data reliability. The proposed framework is deployed for a SCN pertaining to a practical power grid in India for its resiliency analysis. The proposed work can be regarded as a significant contribution to smart grid technology, as it provides a framework for resiliency analysis covering different aspects such as hardware reliability, data reliability, and parameters validation using the QualNet network simulator. Nevertheless, an analytical design of the hybrid SCN proposed in this work can even be extended to other topological designs of SCN.

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“…Optimal sensor placement is aimed at realizing a sensor profile or layout that achieves minimum cost as well as satisfies some prespecified performance criteria has gained traction in a broad spectrum of applications areas such as health monitoring (Tan and Zhang, 2020), distribution of medicine in disaster areas (Parque et al, 2019), indoor cultivation systems (Uyeh et al, 2022b) and smart cities (Du et al, 2019;Jena et al, 2021). Specifically, in indoor cultivation systems, optimal sensor placement has become attractive to facilitate the efficient coordination of sensors for monitoring plant life as well as providing the necessary control of the internal environmental conditions (micro-climate).…”

Section: Introductionmentioning

confidence: 99%

A genetic programming-based optimal sensor placement for greenhouse monitoring and control

Ajani,

Aboyeji,

Mallipeddi

et al. 2023

Front. Plant Sci.

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Optimal sensor location methods are crucial to realize a sensor profile that achieves pre-defined performance criteria as well as minimum cost. In recent times, indoor cultivation systems have leveraged on optimal sensor location schemes for effective monitoring at minimum cost. Although the goal of monitoring in indoor cultivation system is to facilitate efficient control, most of the previously proposed methods are ill-posed as they do not approach optimal sensor location from a control perspective. Therefore in this work, a genetic programming-based optimal sensor placement for greenhouse monitoring and control is presented from a control perspective. Starting with a reference micro-climate condition (temperature and relative humidity) obtained by aggregating measurements from 56 dual sensors distributed within a greenhouse, we show that genetic programming can be used to select a minimum number of sensor locations as well as a symbolic representation of how to aggregate them to efficiently estimate the reference measurements from the 56 sensors. The results presented in terms of Pearson’s correlation coefficient (r) and three error-related metrics demonstrate that the proposed model achieves an average r of 0.999 for both temperature and humidity and an average RMSE value of 0.0822 and 0.2534 for temperate and relative humidity respectively. Conclusively, the resulting models make use of only eight (8) sensors, indicating that only eight (8) are required to facilitate the efficient monitoring and control of the greenhouse facility.

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Identification of Optimal Sensor Location Based on Trade-Off Approach to Improve Resiliency of Electricity Market in Smart Grid

Cited by 13 publications

References 29 publications

Sensor Technologies for Transmission and Distribution Systems: A Review of the Latest Developments

Sensor Technologies for Transmission and Distribution Systems: A Review of the Latest Developments

Analytical Design of Synchrophasor Communication Networks with Resiliency Analysis Framework for Smart Grid

A genetic programming-based optimal sensor placement for greenhouse monitoring and control

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