A Novel Sensor Placement Strategy for an IoT-Based Power Grid Monitoring System

• Published in: IEEE internet of things journal Vol. 7; no. 8; pp. 7773 - 7782
• Main Authors: Jiang, Joe-Air, Wang, Jen-Cheng, Wu, Hung-Shuo, Lee, Chien-Hsing, Chou, Cheng-Ying, Wu, Li-Cheng, Yang, Yu-Cheng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.08.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Binary particle swarm optimization (BPSO); Conductors; Decision making; dynamic thermal rating (DTR); Electric power grids; Internet of Things; Internet of Things (IoT); Meteorological data; Meteorology; Monitoring; Particle swarm optimization; Power lines; Power transmission lines; Remote monitoring; Sensors; Smart grid; Temperature distribution; Temperature measurement; Temperature sensors; Transmission line measurements; Weather; Taiwan
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2020.2991610

Dynamic thermal rating (DTR) is a technique that can effectively reduce the complexity of the decision-making processes for a smart grid. Internet-of-Things-based DTR monitoring systems can be used to achieve reliable and low-cost remote monitoring of power grids, but this method is heavily reliant on collecting accurate real-time meteorological data by sensors deployed on the power lines. However, deploying sensors on each span of the line may not be feasible due to the high cost of such sensors. Thus, this article proposes a modified binary particle swarm optimization (MBPSO) strategy to solve multiobjective combinational decision problems. The proposed method is able to determine the minimum number of sensors required to achieve nearly ideal performance. A 161-kV line located between Xizhi and Minquan, part of the Taiwan Power Company transmission system, was selected as the experimental target. The MBPSO algorithm was solved based upon hourly meteorological data provided by Taiwan's Central Weather Bureau. The results obtained with the proposed method show that only one sensor needs to be deployed on the third span of the line to effectively monitor more than 89.6% of high conductor temperature events, and the root mean square error on the reconstructed conductor temperature distribution is less than 0.8 °C.