Partial Discharge Analysis in Generator Stator Winding Using Artificial Neural Network

This paper analyses the Partial Discharge (PD) on the stator terminals of synchronous generator. This is necessary to collect experimental data for the analysis. First، exploiting the measurement devices، special signals that describe the partial discharges are repeatedly collected. Then، based on the current standards، the collected empirical data are subjected to interpretation. To ease the interpretation process، an Artificial Neural Network is trained and validated. We have used a double layers forward perceptron neural network which is trained by Levenberg–Marquardt algorithm that utilizes least square method as the performance index. As the case study، three gas turbine-generators located in Shahre-Rey power plant (Rey Power Generation Management Company) have been subjected to repeatedly data collection. The mentioned generators are manufactured by Mitsubishi with 85 MW of nominal power. Generally، partial discharge analysis has the following practical implication about the probable defects: lamination of the internal terminal، mobility within the main insulation and discharge into the groove in stator of synchronous generator.