Jcs. Souza et al., DATA VISUALIZATION AND IDENTIFICATION OF ANOMALIES IN POWER-SYSTEM STATE ESTIMATION USING ARTIFICIAL NEURAL NETWORKS, IEE proceedings. Generation, transmission and distribution, 144(5), 1997, pp. 445-455
Bad data identification is one of the most important and complex probl
ems to be addressed during power system state estimation, particularly
when both analogical and topological errors (branch or bus misconfigu
rations) are to be considered. The paper proposes a new method that is
capable of distinguishing between analogical and topological errors,
and also of identifying which are the bad measurements or the misconfi
gurated elements due to unreported or incorrectly reported line outage
s, bus splits etc. The method explores the discrimination capability o
f the normalised innovations (the differences between the latest acqui
red measurements and their corresponding predicted quantities), which
are used as input variables to an artificial neural network that provi
des, in the output, the anomaly identification. Data projection techni
ques are also used to visualise and confirm the discrimination capabil
ity of the normalised innovations. The method is tested using the IEEE
24-bus test system, where several types of errors have been simulated
, including single and multiple bad measurements, topology errors invo
lving branches or buses etc.