Robust Matrix Completion State Estimation in Distribution Systems

Due to the insufficient measurements in the distribution system state estimation (DSSE), full observability and redundant measurements are difficult to achieve without using the pseudo measurements. The matrix completion state estimation (MCSE) combines the matrix completion and power system model to estimate voltage by exploring the low-rank characteristics of the matrix. This paper proposes a robust matrix completion state estimation (RMCSE) to estimate the voltage in a distribution system under a low-observability condition. Tradition state estimation weighted least squares (WLS) method requires full observability to calculate the states and needs redundant measurements to proceed a bad data detection. The proposed method improves the robustness of the MCSE to bad data by minimizing the rank of the matrix and measurements residual with different weights. It can estimate the system state in a low-observability system and has robust estimates without the bad data detection process in the face of multiple bad data. The method is numerically evaluated on the IEEE 33-node radial distribution system. The estimation performance and robustness of RMCSE are compared with the WLS with the largest normalized residual bad data identification (WLS-LNR), and the MCSE

Solving Optimal Power Flow for Distribution Networks with State Estimation Feedback

Conventional optimal power flow (OPF) solvers assume full observability of the involved system states. However, in practice, there is a lack of reliable system monitoring devices in the distribution networks. To close the gap between the theoretic algorithm design and practical implementation, this work proposes to solve the OPF problems based on the state estimation (SE) feedback for the distribution networks where only a part of the involved system states are physically measured. The SE feedback increases the observability of the under-measured system and provides more accurate system states monitoring when the measurements are noisy. We analytically investigate the convergence of the proposed algorithm. The numerical results demonstrate that the proposed approach is more robust to large pseudo measurement variability and inherent sensor noise in comparison to the other frameworks without SE feedback

A methodology for transient state estimation based on numerical derivatives, optimal monitoring and filtered measurements

This paper proposes a methodology for transient state estimation in power systems. The proposed methodology is formulated using approximation methods for derivatives to relate the state variables to measurements. It does not require knowledge of the steady state to establish the pre-disturbance operation conditions. The method uses an optimal monitoring system based on topological analysis to obtain full observability. A saving index is introduced to analyze the effectiveness of the instrumentation used. The adverse effect of noisy measurements in the estimation process is mitigated using an Infinite Impulse Response (IIR) filter. A transient index is introduced to estimate the fault location. The transient state estimation is assessed using two test systems. The results are validated through direct comparison against those obtained by simulation using SimPowerSystems toolbox of Simulink®. With the proposed methodology, the transient state estimation can be obtained with an important saving in the implementation of the measuring system and with considerably less computational effort