Pseudo‐Measurement Models in Distribution Networks: A Review

Abstract

ABSTRACT To enhance stability and reliability of an electric distribution system, the monitoring and data acquisition through measurement devices, sensors and communication networks is essential to maintain the system observability for proper operation and control. However, insufficient measurement devices, and malfunctions of sensors and communication networks may lead to the monitoring data losses, one solution is to use the synthetic datasets, known as pseudo‐measurements, to substitute missing data and improve the system observability. Pseudo‐measurements are created through probabilistic, statistical and machine learning techniques using historical measurements of distribution systems. In this paper, potential roles of pseudo‐measurements to enhance monitoring of distribution networks have been reviewed. Two categories of pseudo‐measurement models are examined in this review: (1) probabilistic and statistical‐based models, including parametric, semiparametric and nonparametric approaches; and (2) machine learning‐based models, including shallow (conventional machine learning) and deep learning structures. Each model's computational demands and practical applications are analysed, highlighting their advantages and limitations. This review aims to identify the research gaps of pseudo‐measurement models and suggest future research directions for robust and adaptive monitoring of distribution networks.

Topics & Concepts