Multiple Fault Diagnosis in Electrical Power Systems with Dynamic Load Changes Using Probabilistic Neural Networks

power systems monitoring is particularly challenging due to the presence of dynamic load changes in normal operation mode of network nodes, as well as the presence of both continuous and discrete variables, noisy information and lack or excess of data. this paper proposes a fault diagnosis framework that is able to locate the set of nodes involved in multiple fault events. it detects the faulty nodes, the type of fault in those nodes and the time when it is present. the framework is composed of two phases: in the first phase a probabilistic neural network is trained with the eigenvalues of voltage data collected during normal operation, symmetrical and asymmetrical fault disturbances. the second phase is a sample magnitude comparison used to detect and locate the presence of a fault. a set of simulations are carried out over an electrical power system to show the performance of the proposed framework and a comparison is made against a diagnostic system based on probabilistic logic.