In AI, Technical Paper
AI for detecting RISO faults in solar plants
Low isolation resistance (RISO) faults are the most common type of DC fault in solar power systems, and approximately 50% of these faults go undetected because ground fault protection devices generally do not sense the small (<1 Amp) current. This white paper dives into the technicalities of RISO faults and presents a case study in which the detection of this type of fault by SmartHelio’s predictive analytics software resulted in the saving of energy, time, and money for a global energy client.
In a nutshell
- ✔️ Low isolation resistance (RISO) on the DC side of a solar power system indicates a fault.
- ✔️ RISO faults are common and often go undetected, which can cause energy losses and severe damage to the system.
- ✔️ SmartHelio's predictive analytics uses AI pattern recognition to detect RISO faults and provide prescriptive guidance to correct them.
- ✔️ Early detection of RISO faults can save time, money, and energy losses and improve the overall performance of solar PV assets.
Who is this for?
- ➡️ Asset managers
- ➡️ O&M teams
- ➡️ Solar installation stakeholders
- ➡️ Heads of innovation
Get access
Please use the form provided below to fill in your information. Once submitted, you will receive an email containing the full paper as a PDF.
