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Digital Twin discovers issues in a fraction of the time
Digital Twin discovers issues in a fraction of the time
by RES | Mar 14, 2022 | Reading time: 2 min
The growth of renewable energy projects has seen the parallel growth of smarter, more intuitive data analysis tools for performance optimisation. No two renewable energy projects are the same, as they are dependant on their environment, location and variable weather conditions to generate energy.
To maximise generation from a site, data analysis solutions must be customisable and intuitive, encompassing the unique challenges for each individual renewable asset. RES teams employ a model of each asset, called a “digital twin.”
Digital twins are advanced weather-based simulation tools that can support the operation team for troubleshooting even the string level issues on a site. Site outages and major issues are easy to mitigate as they’re often detectable by the local SCADA, triggering alarms and notifications. With digital twins, however, the unexpected minor change in output is recognised by the model and can be amended quickly.
In the case of one 100MW+ solar farm managed by RES, the digital twin is an exact model of the site down to each string, combiner box, and inverter. The digital twin picked up that an inverter on the site was performing at only 20% of its expected power. This issue had not triggered any SCADA alarm to alert the Operations and Maintenance (O&M) crew. RES alerted the O&M based on observations from the digital twin, and the O&M team found that mice had chewed through leads due to a recent mice plague in the area.
If the issue had remained undiscovered without the help of the digital twin, the damage would have cost the client $8000 per month and may have taken up to a year to uncover in standard reporting. The leads were fixed and the site was returned to 100% production within a week, reducing the cost of the incident by over 90%.
Cost of this incident would have been $8k per month. Without our digital twins, it might take 12 month(max) until it would be identified during the annual maintenance. We helped the project to reduce the period from 1 year to 1 week.
