Document Type
Thesis
Date of Award
5-31-1991
Degree Name
Master of Science in Electrical Engineering - (M.S.)
Department
Electrical and Computer Engineering
First Advisor
Walid Hubbi
Second Advisor
Bernard Friedland
Third Advisor
Yeheskel Bar-Ness
Abstract
Two classes of bad data (BD) identification methods in power system state estimation are studied.
The first class is the perturbation method. In this method, the obtained measurements are perturbed a few times. Each time a state is obtained from which measurement corrections are calculated. The statistics of the corrections corresponding to a BD are different from those corresponding to a healthy data. The method uses those differences for identification purposes.
The effectiveness of different indicators is studied. These include the normalized residual, the weighted residual, and residuals incorporating the statistics of the corrections.
The success rate of using the different indicators is defined and calculated. The tests are done using the IEEE 14-bus and the 30-bus systems. Different measurements configurations are used. The effects of other factors are also tested and results are presented.
The presented results show that the new indicator 4, is the most effective indicator. Its success rate is a few percent higher than the widely accepted normalized residuals. The indicator Rnp is better than normalized residual indicator not only on an average bases, but also in every case individually. The computational requirements for this method is little higher than those of the normalized residual method.
The second class includes three schemes. These are based on the independent equations method in that a sensitivity matrix is defined. The matrix relates the suspected BD to the healthy ones. Scheme I and III are tested using two systems respectively and single and multiple bad data cases are studied. They are successful in identifying bad data.
Recommended Citation
Zou, Shichun, "Bad data identification in power systems" (1991). Theses. 2713.
https://digitalcommons.njit.edu/theses/2713
