Forecast aided measurements data synchronisation in robust power system state estimation

Forecast aided measurements data synchronisation in robust power system state estimation

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Generation, Transmission & Distribution — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Backbone of a real time monitoring and controlling operations in power systems is state estimation. The main goal of the paper is to use all useful data (collected by measurements) in the process of the robust state estimation. Data acquisition by Phasor Measurement Units (PMUs) has an important role in providing an accurate robust forecasted estimator. Conventional measurements have much lower data transfer rate than the PMUs. On other hands, the number of received data from PMUs is fewer than the conventional measurements and due to this fact, the state of the system are not observable by only PMUs. This fact leads to use the benefits of all kind of measured data. Robustness of the proposed method is guaranteed by rejecting outlier (large amplitude error) by forecasting conventional measurements data and using robustness property of the Kalman filter against noise in data (small amplitude error). Moreover, the proposed estimator tracks dynamic behavior of system much faster than traditional methods. The proposed estimator has been implemented on the IEEE 9-bus and the IEEE 118-bus systems. Comparison results of the proposed algorithm with those of a traditional dynamic estimator proves the efficiency, accuracy and robustness of the proposed method against falsified data.

Related content

This is a required field
Please enter a valid email address