Abstract-This paper presents a new approach for identifying the measurement error in the DC power flow state estimation problem. The proposed algorithm exploits the singularity of the impedance matrix and the sparsity of the error vector by posing the DC power flow problem as a sparse vector recovery problem that leverages the structure of the power system and uses l1-norm minimization for state estimation. This approach can provably compute the measurement errors exactly, and its performance is robust to the arbitrary magnitudes of the measurement errors. Hence, the proposed approach can detect the noisy elements if the measurements are contaminated with additive white Gaussian noise plus sparse noise with large magnitude, which could be caused by data injection attacks. The effectiveness of the proposed sparsity-based decomposition-DC power flow approach is demonstrated on the IEEE 118-bus and 300-bus test systems.
In REF , the measurement errors in DC power flow are identified by exploiting the singularity of the impedance matrix and sparsity of the error vector.
Sparsity-based error detection in DC power flow state estimation
2016 IEEE International Conference on Electro Information Technology (EIT)
2016 IEEE International Conference on Electro Information Technology (EIT)
Computer Science Mathematics