Optimal sensor placement in distribution network based on super resolution network

With the development of smart grids and emerging measurement technologies, the massive growth of distribution grid data may impact the reliable, economic, and safe operation of the distribution network. For a large-scale distribution network state estimation, the strategy of measuring data for a distribution grid is critical to its economy and reliability. This paper proposed a distribution network state estimation model based on a graph convolutional neural network. The proposed algorithm uses a genetic algorithm to optimize the sensor locations, frequency of sensors in the distribution network and configures of devices to guarantee the proposed state estimation data accuracy. With minimizing costs of investment and operation, the proposed graph convolutional neural network provides super-resolution state estimation data of the distribution network by using low-resolution measurement data. The proposed method is tested and verified by the IEEE33 distribution network system and the testing result demonstrates the feasibility and effectiveness of the proposed model and algorithm.