How a Solar Farm Reduced Arc-Fault-Related Downtime by 75% with SingiElectric's AFCI Combiner Box

Customer Background

A 50 MW solar farm operator in the southwestern United States manages over 150,000 PV modules across 300 acres. The facility generates 90,000 MWh annually, supplying power to a regional utility under a 20-year PPA. With a team of 12 O&M staff, the operator focuses on maximizing uptime and minimizing unplanned maintenance costs.

Challenges and Pain Points

Before switching to SingiElectric's AFCI Combiner Box, the solar farm experienced an average of 8–10 arc-fault-related trips per month. Each trip required manual inspection of the entire string, causing an average of 6 hours of downtime per event. The root cause was nuisance tripping from inverter startup transients or grid switching. The existing combiner boxes used basic fuses and circuit breakers without arc-fault detection, leading to delayed fault identification and safety risks., the lack of remote monitoring forced technicians to travel to the site for every incident, adding $2,500 in labor costs per trip.

Why SingiElectric's AFCI Combiner Box?

The operator assessed solutions from three vendors. Competitor A offered a separate AFCI module that required additional wiring and cabinet space, increasing BOS costs by 12%. Competitor B's solution had a 0.5% false trip rate under test conditions but lacked UL 1699B certification. SingiElectric's AFCI Combiner Box integrated arc-fault detection into the combiner box, eliminating extra components. It was UL 1699B certified, ensuring compliance with North American PV standards. The built-in RS485 communication enabled remote monitoring, reducing the need for site visits.

Implementation Process

The project spanned 8 weeks. First, the operator replaced 120 existing combiner boxes with SingiElectric's AFCI models (480V DC, 15A per string, 8 strings per box). The team reconfigured string layouts to align with the new box's monitoring zones. A key challenge was integrating the RS485 bus with the existing SCADA system. SingiElectric provided a Modbus mapping guide, allowing the operator to add arc-fault status and trip alarms to their dashboard within 3 days. After commissioning, the system underwent a 30-day validation period with no false trips.

Quantifiable Results

• Arc-fault-related downtime dropped from 48 hours/month to 12 hours/month, a 75% reduction.
• Nuisance trips decreased from 8–10 per month to 0–1 per month, representing a 90% improvement.
• Annual O&M costs for fault response fell from $30,000 to $7,500, saving $22,500 per year.
• The remote monitoring feature enabled real-time fault notification, reducing average response time from 6 hours to 1 hour.
• Overall system availability increased from 98.5% to 99.6%.

Client Testimonial

"The SingiElectric AFCI Combiner Box has been a important development for our O&M team. We used to spend countless hours chasing false trips. Now we get a clear alert only when there's a real arc fault, and we can dispatch technicians with the exact location. The ROI was clear within the first six months." — Operations Manager, Solar Farm Operator

Lessons and Recommendations

1. Prioritize certified arc-fault detection. UL 1699B certification ensures reliable operation under real-world conditions. Avoid solutions without third-party validation.
2. Leverage integrated monitoring. Combining arc-fault detection with communication capabilities reduces troubleshooting time and supports predictive maintenance.
3. Plan for commissioning. Allocate 2–4 weeks for validation testing to tune arc-fault thresholds and eliminate nuisance trips early.

References

• UL 1699B:2018 Photovoltaic (PV) DC Arc-Fault Circuit Protection — Defines DC AFCI testing and performance requirements for PV systems.
• IEC 62606:2013 General requirements for arc fault detection devices (AFDDs) — Provides baseline for arc-fault detection principles (referenced for AC; DC applications refer to UL 1699B).
• Lightweight, Transferable, Self-Adaptive Framework for Intelligent DC Arc-Fault Detection in PV Systems — Demonstrates advanced detection algorithms achieving 0.9999 accuracy (cited for technical context).