Power Network Parameter Correction via Sparse Unsupervised Regression

Abstract

The problem of correcting power network parameters and topology using multi-period SCADA measurements is considered. Starting from the current knowledge of parameter values, we formulate the parameter correction problem as a sparse unsupervised regression problem by exploiting the sparsity of the parameter errors. The advantage of the proposed approach is that it can localize and estimate parameter errors at the same time; there is no need for prior knowledge of error locations. Furthermore, the approach can be adapted to correct sparse errors in both parameters and topology simultaneously. We present an iterative parameter correction algorithm and demonstrate its efficacy using the IEEE 14-bus test case.