Sparse Error Correction for Power Network Parameters

Transmission line parameters play a key role when accurately modeling the power network. However, several factors such as environmental conditions, errors in manufacturing data, and uninformed network changes can introduce errors to the line parameter information at the control center. In this paper, we leverage the sparse nature of parameter errors and propose an iterative greedy algorithm for nonlinear sparse error correction of parameter errors based on SCADA measurements. A benchmark approach of sparse error correction requires long computation time due to the slow convergence of the iterative reweighted least squares algorithm, with the issue getting aggravated as the power network scales. In the experiments using the IEEE 57-bus and IEEE 118-bus test cases, we demonstrate that our approach outperforms the benchmarks in terms of error correction performance and computation latency.