A Novel Weather Information-Based Optimization Algorithm for Thermal Sensor Placement in Smart Grid

Although dynamic thermal rating is an effective and proper tool in assisting the planning and operation decisions of a smart grid, it closely depends on the weather information to determine the deployment of large numbers of sensors. This paper proposes a method, named gappy proper orthogonal decomposition-based genetic algorithm (GPOD-GA) for effectively determining the minimum number of thermal sensors and the optimal placements. The middle section of 345 kV transmission lines in the Taipower system was selected as a test grid and hourly weather data obtained from the Central Weather Bureau of Taiwan was used to examine the validity of the proposed method. The results show that the proposed method can significantly reduce the number of sensors that should be originally deployed on each span of power grid lines. A decrease of 64.61% and 79.61% of sensor deployment is obtained for the case of the entire grid and the individual transmission lines, respectively. Using the partial measurements from the minimum sensor deployments determined by the GPOD-GA algorithm, the accuracy test results also indicate that conductor temperatures of all lines in the power grid can be fully tracked and accurately reconstructed.