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

Machine-learning-based Bayesian state estimation in electrical energy systems

In many algorithmic applications in electrical power grids, state estimation (SE) represents the first step of a process chain. In SE, sensor measurements are processed to infer the most probable grid state. Classical methods such as weighted least squares (WLSs) based approaches use statistical methods that can be based on sensor noise and erroneous measurements. With these methods, only point estimates are made, which results in a lack of knowledge about prediction uncertainties. In this study, machine-learning-based methods for determining the actual state of the grid are proposed. Bayesian optimisation is applied to find the optimal hyperparameter configurations for neural networks (NNs) for SE tasks. The application of Bayesian inference using Bayesian NNs is proposed, which allows the prediction of point estimates as well as uncertainty intervals for the system states. The advantages of using Bayesian approaches in comparison to classical SE methods like WLS are shown

Enhancement of Distribution System State Estimation Using Pruned Physics-Aware Neural Networks

Realizing complete observability in the three-phase distribution system remains a challenge that hinders the implementation of classic state estimation algorithms. In this paper, a new method, called the pruned physics-aware neural network (P2N2), is developed to improve the voltage estimation accuracy in the distribution system. The method relies on the physical grid topology, which is used to design the connections between different hidden layers of a neural network model. To verify the proposed method, a numerical simulation based on one-year smart meter data of load consumptions for three-phase power flow is developed to generate the measurement and voltage state data. The IEEE 123-node system is selected as the test network to benchmark the proposed algorithm against the classic weighted least squares (WLS). Numerical results show that P2N2 outperforms WLS in terms of data redundancy and estimation accuracy

Efficiency of post-processing in PMU based state estimation of renewable energy microgrids

Power System State Estimation (SE) is a process for determining the state of all the buses in a power system (voltage magnitude and angle) based on measurements taken at a selection of a few buses. Traditionally, the only information that measurement devices could provide was the magnitude of the measured signal. On the other hand, the Phasor Measurement Unit (PMU) can measure the current phasors of the directly linked lines as well as the voltage phasors (both angle and magnitude) of the bus at which it is located. However, achieving observability of the system using only PMU devices is very expensive. In order to determine the condition of a power system, phasor measurements are employed in addition to conventional measurements. In this paper, the use of PMU measurements to estimate the state of a renewable energy microgrid (REM) has been explained and the proposed method has been verified on IEEE 21 bus microgrid. The method makes use of PMU voltage and current data after post-processing, as well as a separate linear state estimator model that makes use of the state estimate from Weighted Least Square (WLS). Using the WLS state estimation approach from conventional data, the model first estimates the state in polar coordinates. This state is then combined with PMU measurements in rectangular coordinates, to predict the system's final state