Estimating Complex Power Magnitudes Using a Bank of Pre-Defined PFs Embedded in ANNs

Measuring complex power magnitudes (or apparent power) of transmission, sub-transmission, and distribution lines is a very important practice. It is used in many analysis, including: power system operation, protection, reliability, and electricity markets. Realistic networks could contain many sensors and instrument devices to provide online measurements of these variables. The communication between field, control, and supervisory levels is done through different protocols that could be integrated with different automation and energy management systems. Thus, the chance of failure to any of these different layers of online measurement always exists; which is a familiar task assigned to maintenance departments. This paper tries to solve this crucial problem by estimating these measurements without depending on any of these online devices. The idea here is to train an artificial neural network (ANN) based on a dataset created from a large number of offline power flow solutions. The numerical results show that this instruments-free power estimator (IFPE) is a highly significant and effective tool to predict any apparent power directly by just knowing the power settings of units and loads and the present status of branches.