Online topology determination and bad data suppression in power system operation using artificial neural networks

The correct assessment of network topology and system operating stare in the presence of corrupted data is one of the most challenging problems during real-time power system monitoring particularly when both topological (branch or bus misconfigurations) and analogical errors are considered. This paper proposes a new method that is capable of distinguishing between topological and misconfigurated elements or the bad measurements. The method explores the discrimination capability of the normalized innovations, which an used as input variables to an artificial neural network, whose output is the identified anomaly. Data projection techniques an also employed to visualize and confirm the discrimination capability of the normalized innovations, The method is tested using me IEEE 118-bus test system and a configuration of a Brazilian utility.