Enhancement of power grid monitoring based on data weighting

TL;DR

This paper introduces a data weighting method using Analytic Hierarchy Process to improve power grid monitoring by emphasizing critical signals, enhancing data quality, and reducing operational risks in large-scale energy networks.

Contribution

It proposes a novel weighted observability calculation method based on AHP, tailored for power grid data to improve monitoring accuracy and data validation.

Findings

Improved data quality in the Iranian power grid network.

Corrected erroneous data using the weighted observability approach.

Enhanced network monitoring and operational risk assessment.

Abstract

With their expansion, national power grid have had to work with huge sets of data received from a vast number of substations and power plants. Given their large volume and variety, these data can be classified as big data. Managing this massive amount of data is certainly challenging. Depending on the application, parts of these data are more important for real-time network operation. Computing a network's observability score without assigning weights to different signals may not provide a complete picture of the received data's validity and thus lead to incorrect assessments of the network status. Consequently, signals critical to the network operation and functions of an energy management system (EMS) should be assigned higher weights in observability calculations. The weighted observability alongside the classic non-weighted observability can serve as an indicator of each area's condition in comparison to that of other areas and so greatly facilitate the monitoring and verification of transmitted data. For calculating a weighted observability, the current paper presents a method based on the Analytic Hierarchy Process (AHP), in which higher weights are assigned to data that are more valuable for and widely used by operators. The national electricity network of Iran was chosen for the evaluation of the proposed method's effect on data quality and operational risk. The results indicated that the introduced method positively affects the quality of received data and also corrects erroneous data in the network.