A Review of Distribution System State Estimation Methods and Their Applications in Power Systems

Abstract

This paper summarizes a review of the distribution system state estimation (DSSE) methods, techniques, and their applications in power systems. In recent years, the implementation of a distributed generation has affected the behavior of the distribution networks. In order to improve the performance of the distribution networks, it is necessary to implement state estimation methods. As transmission networks and distribution networks are not similar due to variations in line parameters, buses, and measuring instruments, transmission state estimation cannot be implemented in distribution state estimation. So, some aspects, such as accuracy, computational time, and efficiency, should be taken into account when designing distribution state estimation methods. In this paper, the traditional methods are reviewed and analyzed with data-driven techniques in order to present the advantages and disadvantages of the various methods.

Distribution State Estimation and Its Impact of Load Modeling

“…Critical measurements contain negligible residuals, making it impossible to identify their inaccuracies. If the removal of any measurement from a set of measures makes the remaining measurements important, the set is said to be a minimally dependent set (MDS) [23][24][25][26][27][28]. The absolute values of the normalized residuals for each measurement in an MDS are equal.…”

Bibliometric study of distribution system state estimation: advances and challenges

“…Later, it was included and analyzed the impact and implementation of synchronized or non-synchronized D-PMU. These advances and developments can be found in several state-of-the-art reviews [2][3][4][5][6][7]. In [2,4] technologies, obstacles, challenges, and components of a DSSE are presented and analyzed.…”

“…In [5] highlights the importance of using Machine and Deep Learning to address DSSE. The advantages, disadvantages and applications and a summary of different DSSE methods is presented in [6,7].…”

The Efficiency of the Kalman Filter in Nodal Redundancy

“…The Kalman filter and its extension, namely, the extended Kalman filter (EKF), represent fundamental tools in the precise and up-to-date estimation of the state of dynamic systems, playing a crucial role in a wide range of fields from electrical engineering to robotics [11,12]. These algorithms are especially relevant in environments where the fusion of information from multiple sources is imperative to obtain reliable estimates of the system state.…”