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.
All Science Journal Classification (ASJC) codes
- Signal Processing
- Information Systems
- Hardware and Architecture
- Computer Science Applications
- Computer Networks and Communications