Line Switch Unit Commitment for distribution Automation Systems using Neural Networks

To enhance the cost effectiveness of the distribution automation system (DAS), this paper proposes the Artificial Neural Networks (ANNs) to derive the Line Switch Unit Commitment by minimizing the total cost of customer service outage and investment cost of line switches. The customer interruption cost is determined according to the customer type, loading, outage frequency and number of automated line switches in the feeder. The ANNs models were created for a radial feeder and an open loop feeder, and then implemented with the load growth in order to determine the year for the next line switch to be added. A sensitivity analysis of the impacts of the loading and the outage frequency in the line switch commitment is studied in this paper and a comparison between the radial feeder and the open tie feeder is also shown in the results.