  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.
