Smart Power Grid Synchronization With Fault Tolerant Nonlinear Estimation

Effective real-time state estimation is essential for smart grid synchronization, as electricity demand continues to grow, and renewable energy resources increase their penetration into the grid. In order to provide a more reliable state estimation technique to address the problem of bad data in the PMU-based power synchronization, this paper presents a novel nonlinear estimation framework to dynamically track frequency, voltage magnitudes and phase angles. Instead of directly analyzing in abc coordinate frame, symmetrical component transformation is employed to separate the positive, negative, and zero sequence networks. Then, Clarke\u27s transformation is used to transform the sequence networks into the αβ stationary coordinate frame, which leads to system model formulation. A novel fault tolerant extended Kalman filter based real-time estimation framework is proposed for smart grid synchronization with noisy bad data measurements. Computer simulation studies have demonstrated that the proposed fault tolerant extended Kalman filter (FTEKF) provides more accurate voltage synchronization results than the extended Kalman filter (EKF). The proposed approach has been implemented with dSPACE DS1103 and National Instruments CompactRIO hardware platforms. Computer simulation and hardware instrumentation results have shown the potential applications of FTEKF in smart grid synchronization

Phasor Estimation for Grid Power Monitoring: Least Square vs. Linear Kalman Filter

International audienc

P-class phasor measurement unit algorithms using adaptive filtering to enhance accuracy at off-nominal frequencies

While the present standard C.37.118-2005 for Phasor Measurement Units (PMUs) requires testing only at steady-state conditions, proposed new versions of the standard require much more stringent testing, involving frequency ramps and off-nominal frequency testing. This paper presents two new algorithms for “P Class” PMUs which enable performance at off-nominal frequencies to be retained at levels comparable to the performance for nominal frequency input. The performances of the algorithms are compared to the “Basic” Synchrophasor Estimation Model described in the new standard. The proposed algorithms show a much better performance than the “Basic” algorithm, particularly in the measurements of frequency and rate-of-change-of-frequency at off-nominal frequencies and in the presence of unbalance and harmonics

Multi-phase state estimation featuring industrial-grade distribution network models

This paper proposes a novel implementation of a multi-phase distribution network state estimator which employs industrial-grade modeling of power components and measurements. Unlike the classical voltage-based and current-based state estimators, this paper presents the implementation details of a constrained weighted least squares state calculation method that includes standard three-phase state estimation capabilities in addition to practical modeling requirements from the industry; these requirements comprise multi-phase line configurations, unsymmetrical and incomplete transformer connections, power measurements on 4-connected loads, cumulative-type power measurements, line-to-line voltage magnitude measurements, and reversible line drop compensators. The enhanced modeling equips the estimator with capabilities that make it superior to a recently presented state-of-the-art distribution network load estimator that is currently used in real-life distribution management systems; comparative performance results demonstrate the advantage of the proposed estimator under practical measurement schemes

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