Component importance indices and failure prevention using outage data in distribution systems

Sammanfattning: Interruptions in power supply are inevitable due to faults in power system distribution network. These interruptions are not only expensive for the customers but also for the distribution system operator in the form of penalties. Increase in system redundancy or the use of component-specific sensors can help in reduction of interruptions. However, these options are not always economically feasible. Therefore, there is a need to check for other possibilities to reduce the risk of outages. The data stored in substations can be used for reducing the risk of outages by deriving component importance indices followed by ranking and predicting the outages. This thesis presents component importance indices derived by identifying the critical components in the grid and assigning index based on certain criterion. The model for predicting the faults is based on the weather conditions observed during the outages in the past. Component importance indices are derived and ranked based on the de-energisation time of components, frequency and impact of outages. This helps prioritize components according to the chosen criterion and adapt monitoring strategies by focusing on the most critical components. Based on categorical Naive Bayes, a model is developed to predict the probability of fault/failure, location and component type likely to be affected for a given set of weather conditions. The results from the component importance indices reveal that each component’s rank varies based on the chosen criterion. This indicates that certain components are critical with respect to specific criterion and not all criteria. However, some components are ranked high in all the methods. These components are critical and need focused monitoring. The reliability of results from component importance indices to a great extent depends on the time frame of the outage data considered for analysis. The prediction model can alert the distribution system operator regarding the possible outages in the network for a given set of weather conditions. However, the prediction of location and component type likely to be affected is relatively inaccurate, since the number of outages considered in the time frame is low. By updating the model regularly with new data, the predictions would be more accurate.