Sparsity-Based Error Detection in DC Power Flow State Estimation

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 $l_1$-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. The effectiveness of the proposed sparsity-based decomposition-DC power flow approach is demonstrated on the IEEE 118-bus and 300-bus test systems

Systematic evaluation for multi-rate simulation of DC Grids

With wide applications of power electronic devices in modern power systems, simulation using traditional electromechanical and electromagnetic tools suffers low speed and imprecision. Multi-rate methods can enhance efficiency of simulation by decreasing the scale of systems in small time-steps. However, the existing traditional methods for multi-rate simulation suffer the problems of instability and simulation errors. These have hindered the application of multi-rate simulation in power industry. Therefore theoretical evaluation on different multi-rate simulation methods is crucial to understand the feasibility and limitation of the methods, and to contribute to overcome the drawbacks of the traditional methods. In this paper, the multi-rate simulation performance based on two traditional technologies and a Modified Thevenin Interface are evaluated to provide an overall feasibility of multi-rate algorithms in the power simulation. The Modified Thevenin Interface is proposed to overcome the drawbacks in synchronization. Three theorems are proposed and proved for theoretically analyzing the stability of the simulation methods. Error analyses of the multi-rate methods are performed to identify the relationships between errors and simulation conditions. Besides, the accuracy and efficiency performance in a practical project of VSC-MTDC shows the feasibility and necessity by using multi-rate simulation. Through the theoretical analysis, the issues of stability and accuracy of multi-rate simulation for the DC grids have been better understood, based on which an improved simulation algorithm has been proposed to overcome these issues. Long-term system dynamics of large-scale systems containing DC grids and fast transients of HVDC converters can be investigated simultaneously with high speed and sufficient accuracy

Sparsity-Based Error Detection In Dc Power Flow State Estimation

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