With the adoption of smart meter or automated metering infrastructure (AMI) and big data platforms to collect and analyze AMI meter data, utilities are equipped to continuously monitor and proactively manage distribution transformer overloading.
Ameren Illinois and SAS Institute worked together to create a cost-benefit case for distribution transformer health monitoring along with a detailed proof concept for their predictive model.
Distribution transformer is the most vital asset in any electrical distribution network. Hence, distribution transformer health monitoring and load management are critical aspects of smart grids. Transformer health monitoring becomes more challenging for smaller transformers where attaching expensive health monitoring devices to the transformer is not economically justified. The addition of Advanced Metering Infrastructure (AMI) in smart grids offers significant visibility to the status of distribution transformers. However, leveraging vast amount of AMI data can be daunting.
This paper uses the hourly usage data collected from Ameren Illinois‘ AMI meters to determine distribution transformer outage, failure, and overload. The proposed methodology not only detects and visualizes outage and congested areas in near real time, but also detects transformers and distribution areas with a long history of outage and congestion. This paper also offers a predictive algorithm to enhance regular equipment maintenance schedules and reduce repair truck trips for unscheduled maintenance during unplanned incidents like storms. SAS® Enterprise Guide®, SAS® Enterprise Miner™, and SAS® Visual Analytics were used to efficiently produce the information necessary for operational decision-making from gigabytes of smart meter data.
UAI Members can access the full whitepaper at Distribution Transformer Health Monitoring and Predictive Asset Maintenance.
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