Smart Diagnosis of power quality disturbances using Bayesian networks

In the power energy industry there is a growing interest in measuring and improving the reliability and security of the distribution energy grid. In this process, the power quality (PQ) disturbances have become a major concern due the uncertainty associated to possible affectations of these kinds of disturbances to the user loads and the utility operation. As consequence, probabilistic and graphical methods have to be implemented in order to locate the most probable cause and effect of certain PQ disturbance helping the analyst in the decision making process. Bayesian networks are a useful tool to include the fault knowledge model in probabilistic inference algorithms to determinate the most probable causes and effects of a PQ disturbance in a modeled situation. This paper presents the development of a tool for PQ diagnostics implemented with National Instruments LabVIEW®. The tool is applied for SAG diagnostics in a given scenario; the results reveals that the tool can diagnose the disturbance with high accuracy in each simulation, and represents a good complement for PQ measuring instruments.