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New data collection system equipment was installed in Xcel Energy substations and data was collected from 6 substations and 20 feeders. During Phase I, ABB collected and analyzed 793 real-time events to date from 6 Xcel Energy substations and continues today. The development and integration of several applications was completed during the course of this project, including a model-based faulted segment identification algorithm, with very positive results validated with field-gathered data discussed and included in this report. For mostly underground feeders, the success rate is 90% and the overreach rate is 90%. For mostly overhead feeders, the success rate is 74% and the overreach rate is 50%. The developed method is producing very accurate results for mostly underground feeders. For mostly overhead feeders, due to the bad OMS data quality and varying fault resistance when arcing, the developed method is producing good results but with much room for improvement. One area where the algorithm can be improved is the accuracy for sub-cycle fault events. In these cases, the accuracy of the conventional signal processing methods suffers due to most of these methods being based on a one-cycle processing window. By improving the signal processing accuracy, the accuracy of the faulted segment identification algorithm will also improve significantly. ABB intends to devote research in this area more » in the near future to help solve this problem. Other new applications developed during the course of the project include volt/VAR monitoring, unbalanced capacitor switching detection, unbalanced feeder loading detection, and feeder overloading detection. An important aspect of the demonstration phase of the project is to show the ability to provide adequate “heads-up” time ahead of customer calls or AMI reports so that the operators are provided with the much needed time to collect information needed to address an outage. The advance notification feature of the demonstration system provides this time and helps accelerate service restoration ultimately. To demonstrate the effectiveness of this feature, a demo system using substation data alone was set up to compare the minutes saved over a period of 22 months for two feeders where the real-time notification system has been deployed. The metric used for performance assessment is the time difference between the actual outage time from the OMS versus the time the notification email was received on the operators desk. Over the period of 22 months, we have accumulated over 7600 minutes (32 hours) ahead of actual outage time compared to the OMS timestamps. The significance of this analysis is that it shows the potential to reduce the SAIDI minutes and directly impact utility performance in terms of outage duration. If deployed at scale, it would have a significant impact on system reliability. To put this number in perspective, it would be helpful to assign a dollar figure to the potential savings that could be realized. According to the host utility, the average cost for each customer-minute-out (CMO) is approximately $0.30 across the operating company. This includes both direct and indirect costs such as bad press. The outage data over the previous 4 years show that the average customer count on primary/tap level outages is about 56. Accordingly, the total minutes saved amounts to 425,600 CMOs on the average. This would in turn result in a potential cost savings figure of $127,680 for two feeders alone over the period of performance. This empirical evidence validates the strong value proposition of the project that was contemplated at the onset and its potential impact to reduce outage duration in support of DOE’s goal of 20% « less

Real-Time Distribution Feeder Performance Monitoring, Advisory Control, and Health Management System