Enphase Energy Successfully Demonstrates IQ Solid-State Transformer Technology Converting 13.8 kV AC to 800 VDC
Key Highlights
- ➤IQ SST converts 13.8 kV AC to stable 800 VDC for AI compute racks
- ➤132 power modules maintained balanced voltage sharing through rapid load changes
- ➤Enphase plans 34.5 kV demonstration in coming weeks
- ➤Megawatt-scale IQ SST racks targeted before end-2026; customer pilots planned for 2027
Expert Statements
Raghu Belur, Co-founder and chief product officer at Enphase Energy
“Over the past 20 years, we've built and shipped nearly 90 million microinverters, gaining extensive experience in designing and manufacturing reliable power electronics. IQ SST brings that same approach to AI data centers, moving us closer to delivering power more efficiently to AI compute racks.”
Hans van Antwerpen, Chief technology officer at Enphase Energy
“The system performed exactly as designed. All 132 modules shared the voltage evenly, and the 800 V output held steady through every load step. The architecture scales by adding power modules in series, so 13.8 kV gives us a clear path to 34.5 kV.”
FREMONT, Calif., Oct. 27, 2026 (GLOBE NEWSWIRE) -- Enphase Energy, Inc. (NASDAQ: ENPH), a global energy technology company, today announced a major milestone in the development of its IQ(®) Solid-State Transformer (IQ SST), successfully demonstrating 13.8 kV AC to 800 VDC conversion at its research and development lab in Austin, Texas. The test system consisted of 132 power modules, with 44 connected in series on each of three phases, operating together as a single system. The system maintained a stable 800 VDC output through load steps simulating the fast-changing demands of AI compute racks.
"Over the past 20 years, we've built and shipped nearly 90 million microinverters, gaining extensive experience in designing and manufacturing reliable power electronics,” said Raghu Belur, co-founder and chief product officer at Enphase Energy. "IQ SST brings that same approach to AI data centers, moving us closer to delivering power more efficiently to AI compute racks."
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