Identification of parallel flows in power networks through state estimation and hypothesis testing

The occurrence of multiple bilateral transactions in large interconnected power networks may expose a given power system control area to large unexpected parallel flows that are usually detrimental to the area economic operation, in addition to posing security risks. To prevent undesirable consequences as well as to take adequate compensation measures, the control area operator must be able to identify the causes of such unaccounted power flows.The purpose of this paper is to present a monitoring tool to track parallel flows due to power transactions unreported to the control area operator. The proposed tool extends the role of power system state estimation through the definition of additional state variables representing bilateral transactions, which are then estimated along with the conventional states. The unreported transactions are identified through statistical tests based on Bayes's theorem. The paper also discusses relevant computational issues related to the method's implementation. The IEEE 118-bus test system is employed to evaluate the performance of the proposed monitoring tool.