Prediction of polluted insulator based on leakage current resistance insertion performance of short and medium transmission line model

The main objective of the transmission lines is to deliver power from the generator to the customers, with less losses and without any interruptions. however, pollution sources are increasing around the world, which are affecting one of the most important components of a power line, namely, the high voltage outdoor insulators. the accumulation of pollution on the surface of the insulator can affect its physical properties and create leakage current resistance. under suitable conditions, this resistance will lead to leakage current on the surface of the insulator. in previous studies, leakage current measurement on the insulator surface was ignored because it is negligible. however, increasing pollution levels and the large number of transmission line insulators should take into account the effect of leakage current resistance in the transmission line model. in this thesis, an improved model is introduced to examine the effect of leakage current resistance on the parameters of the transmission line, the amount of additional active power losses, voltage drop and increased real power generation in power networks for both short and medium transmission lines. three levels of leakage resistance (high, medium, and low) that represent the three levels of pollution are incorporated into the transmission line model through a series of delta to star and star to delta conversion using a two-port network concept. then, by inserting the leakage current resistance, a simulation model was used to measure leakage current and voltage of the leakage current resistance. a simulation sensor is used to predict the level of pollution on the insulator and the location of highly polluted insulators using artificial neural network. this study was able to determine the changes in each parameter and the effects of these changes on the active power losses and voltage drop in three different systems. the application of the improved model have shown an increase in detection of power losses by 25.63% in high pollution conditions at the insulators in all short and medium transmission lines. thus, to compensate for these high losses, the system needs to increase real power generation by 0.61% when compared with during normal conditions. the prediction results by the simulation model for the 5- bus system clearly demonstrated that the overall correct classification rates for the predicted pollution levels were very high at 97.67% and 98.03%, for both short and medium models, respectively. meanwhile, the correct classification rate for the predicted locations of highly polluted insulators is 100% for both short and medium models. the results obtained in this study offer accurate information for polluted transmission line insulators, which could be used for maintenance and calculation of power loss for polluted insulators, in order to keep the power system in a reliable state