Storms, high temperatures, trees, accidents...they all are sources of customer outages. Some cannot be mitigated ahead of time, but others can. Through the use of the asset information in your GIS, combined with usage data from a AMI, Meter Data Management System, or Customer Billing System, you can analyze the peak load on your transformers. The results of the analysis can then be visualized in geographic and tabular format. It can also become the basis for making decisions on preventative maintenance prioritization and/or asset replacement and upgrades.
In this example, the Operations department has indicated that a number of customers have called complaining about power quality during peak usage. Furthermore, a number of transformers have actually exploded due to the power demands of the customers connected to them.
Burning overhead transformer Damage from a transformer explosion in a vault
Where to start?
Since the electric distribution network is mapped and network connectivity maintained, some relatively simple analysis can be done to find transformers that are nearing, or beyond, acceptable limits of loading.
To begin, I asked the Billing department for customer usage (kWh) data, along with the Meter ID associated with that usage, from the months of July and August as those months make up the summer peak months in this example. That was delivered in an Excel file which was loaded into the geodatabase as a new table.
The next step was to build a geoprocessing tool to determine the peak load at each transformer. While there are many methods to determine transformer loading and sizing, (this presentation presents a few) I chose one that made use of the monthly load data provided by the Billing department. The tool was created by building a Python script that would:
- Select a transformer
- Trace the secondary attached to the transformer and return a set of service points electrically connected to the transformer
- Select the meters at each service point
- Select the customer usage data for each meter at the load point
- Determined the daily load for each meter
- Sum the daily load of the connected meters to get the total demand at the transformer in (kW)
- Multiply total demand by the Demand Slope Coefficient which allows for a diversity factor to be applied to the daily total
- Add a Demand Constant, representing a base demand on the transformer, to the result and providing a total demand on the transformer in kW
- Divide this total by the Power Factor to determine the total demand in kVA. The power factor is the ratio of real power flowing to the load to the apparent power in the circuit. This results in the demand on the transformer in kVA.
- Finally, the actual demand in KVA is compared to the transformer's rated kVA to determine whether the transformer is overloaded.
The script allows the user to adjust the different parameters (Demand Slope Coefficient, Demand Constant, Power Factor, & the Overload Percentage) in the calculation or accept the defaults.
While this produces great results, analyzing each transformer in the study area individually was not practical. To simplify the work, second Python script was developed to allow the user to select a feeder so that the load on each transformer on the feeder could be determined in a batch process.
Sharing the results
After running the script on all the feeders in the Greeley Substation the results can be joined to the transformer feature class. This allows for the load analysis results to be displayed geographically using a number of options. Of course you can leverage the full power of ArcGIS on the desktop to create a map that can be printed and shared.
However, not everyone has a license of ArcMap or the knowledge to use it effectively. So, depending upon the audience, this type of information might be better shared as a web map through the organizations ArcGIS Online account or an internally-hosted Portal for ArcGIS Online site. This approach simplifies access to the information for non-GIS professionals. In this case, the dark red circles represent transformers that are at 179% of rated KVA or higher and need to be addressed.
In the image above, you can see that an overload percentage of 170% was used to determine how much load (in kVA) a transformer can support while still operating within the acceptable limits. Additionally, the tool calculated the Summer Peak Load (in kVA) to be 198% for the selected transformer.
Yet a third option would be to export the results to a Excel file and make use of Esri's Maps for Office tools. This gives the end user access to both the power of Excel to sort, query, and analyze the results as well as the value of a geographic view into the data.
Combining the geographic view and the tabular information makes it possible to formulate a plan of action to address the overloaded transformers. The southwest portion of the service territory has many overloaded transformers, some critically overloaded. It makes sense that the initial transformer upgrades are performed in this area to mitigate the risk of transformers exploding or starting fires.
Additionally, these areas point to where a demand response program that might provide incentives to utility customers to reduce power consumption at peak times. These programs may be enough to offset enough load to avoid having to replace a transformer. Additionally, this information could be used to help target the marketing of such a program to specific neighborhoods and tailor the program based upon the usage patterns of that community.
In the northwest portion of the service territory there are many transformers that are approaching a load of 170% of the rated kVA. In this area a monitoring program can be established to regularly receive new load information from the Billing department to ensure that network and infrastructure improvements match load growth.
While this exercise produces some interesting and valuable information, it is just a start as to what could be done with a Transformer Load Analysis program.
Integrating AMI and MDM systems with the GIS makes it possible to get much more accurate peak load information than that which was generated with this example. With more fine-grained data to analyze from the MDM (such as 15-min interval usage data) rather than a monthly reading and a daily average load it would be possible to determine much more accurately the transformers that are at or above 170% of the rated kVA.
Alternatively, the Transformer Load Analysis geoprocessing tool could be published as a service that integrates with other ArcFM Server tools and applications to leverage the results. For instance, a designer could quickly check the available capacity of a transformers at the tap point for a new service drop by calling the service. Another example is a Dispatcher, perhaps using Responder, could check the load on a transformer that is the predicted outage device to check whether the transformer needs to be upgraded prior to rolling a truck.
Bringing It Home
As utilities seek to improve business processes and operate more efficiently it is becoming more and more vital to be able to show larger returns on the GIS investment. To help generate those returns ArcGIS and ArcFM are evolving to provide both the tools and the platform to analyze the data deeply and collaborate with map-based information more easily.