A new 8.5 kW power supply for AI and hyperscale data centers combines GaN power ICs with third-generation SiC MOSFETs. The SiC components, developed through two decades of research, use a trench-assisted planar technology that reduces case temperatures by up to 25°C compared to current devices. Testing indicates this temperature reduction can extend component lifespan by a factor of three.

The semiconductor advances include motor drive ICs for appliance and industrial applications, along with 650V bi-directional GaN technology for power conversion. New SiC modules target power grid infrastructure, renewable energy systems, EV charging stations, and uninterruptible power supplies.

Analysis from Yole Group indicates GaN and SiC technologies may represent 30% of the power semiconductor market by 2027, driven by demand for higher efficiency in data centers and electric vehicles. The technology’s reduced carbon footprint could contribute to CO2 emissions reduction targets through improved power conversion efficiency.

Technical discussions at the conference will examine how wide bandgap semiconductors can address thermal management challenges in high-power applications.

The technologies will be displayed at Hall C3, booth #129 at Trade Fair Center Messe München from November 12th-15th, 2024.

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“The new device is fabricated using our PowiGaN technology. It’s a proprietary process from PI that allows us to do very efficient power conversion,” explained Andy Smith, Director of Training at Power Integrations. “We’re marrying it with our InnoMux-2 technology, which is the ability to deliver multiple outputs with a single-stage converter both efficiently and accurately.”

The technology incorporates the 1700 V GaN switch with multi-output regulation, the company’s FluxLink digital isolation technology, and secondary-side zero voltage switching.

A key technical feature is the switch’s efficiency levels at higher voltages. Testing demonstrated consistent performance across voltage ranges. “We took one board and swapped the power IC between them… and look at that, the efficiency is pretty close to identical, which is astounding,” said Smith.

The 1700 V InnoMux-2 IC easily supports 1000 VDC nominal input voltage in a flyback configuration and achieves over 90 percent efficiency in applications requiring one, two, or three supply voltages. Each output is regulated within one percent accuracy, eliminating post regulators and improving system efficiency further by approximately ten percent. The new device replaces expensive silicon carbide (SiC) transistors in power supply applications such as automotive chargers, solar inverters, three-phase meters, and a wide variety of industrial power systems.

Smith continues, “We are able to build a 1700 V GaN transistor. Nobody else is, and the reason for that is the proprietary technology we use for our GaN.” The economics are also notable, with Smith adding that “GaN transistors are somewhere approaching the cost of silicon to manufacture. In fact, in the long term, GaN and silicon prices will probably converge.”

The market timing appears favorable. According to Ezgi Dogmus, Activity Manager at Yole Group, the power GaN device market is poised to reach $2 billion by the decade’s end.

The metering market presents an early opportunity. “Metering hits at that high voltage all day long. They have shorter design cycles than typically would be the case for an industrial application,” Smith explained, noting that “when they roll out energy meters, they tend to roll them out in large quantities, 20, 100,000 at a time.”

Radu Barsan, vice president of technology at Power Integrations, said, “Our rapid pace of GaN development has delivered three world-first voltage ratings in a span of less than two years: 900 V, 1250 V and now 1700 V. Our new InnoMux-2 ICs combine 1700 V GaN and three other recent innovations: independent, accurate, multi-output regulation; FluxLink, our secondary-side regulation (SSR) digital isolation communications technology; and zero voltage switching (ZVS) without an active-clamp, which all but eliminates switching losses.”

The InnoMux-2 with 1700V GaN technology is now available at $4.90 in 10,000-unit quantities. Power Integrations offers a reference design (RDR-1053) for a 60 W dual-output power supply to demonstrate the technology’s capabilities.

This development in power conversion technology could enable new options for high-voltage applications while balancing efficiency and cost considerations. As energy efficiency requirements evolve, technologies like this may contribute to meeting future power conversion needs.

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Electrification is in the early stages of deployment. Today, non-electric emissions dominate in most segments (Figure 1). This FAQ provides a broad and deep review of how AI and ML can support decarbonization. It begins with a layered view of how AI and digitization will be implemented across the utility grid, then reviews how AI will enable the use of phasor measurement units (PMUs) that support efficient integration of renewable energy into the transmission grid and closes with a glance of how ML is being used to unwrap and filter the data from PMUs into actionable information.

Integration of renewable energy sources into the utility grid is more than a technology problem. A layered vision of the factors involved in the decarbonization of the grid has been proposed that considers policy needs, market challenges, and technology demands.

Electric grids are heavily regulated. Development of policies that efficiently and equitably integrate renewables is expected to rely on AI to interpret the complex data sets available for renewable energy performance and costs and how to best integrate them into the transmission and distribution grids.

AI and ML will also be needed to optimize performance at the market level. There will be new market participants, from residential PV to large-scale wind farms, that will have divergent interests and, in some cases, non-compatible economic interests. AI and ML will be used to implement scenario-based optimization analyses to identify hidden tradeoffs and move toward globally maximal solutions.

Finally, the development of optimal policies and market solutions demands highly efficient technological integration of renewables into the grid. Renewables behave differently from conventional generation sources, and their integration will rely on AI and ML to enable efficient, cost-effective, and reliable solutions (Figure 2).

Phasors and the transmission grid
Transmission grid operating conditions are monitored using phasor measurement units (PMUs) that measure the magnitude and phase angle for the AC voltage or current at a specific location. More advanced designs called synchro-phasers use global positioning system (GPS) signals to time synchronize the measurements across large areas of the grid. That enables the detection of anomalous conditions that can result from physical faults on the grids, electrical disturbances, or cyber-attacks. AI and ML tools have been developed and proposed for monitoring the integrity of transmission grids. Examples include:

• Long Short-Term Memory (LSTM) neural networks can save long-term temporal relationships and data patterns.
• Convolutional Long Short-Term Memory (C-LSTM) is a hybrid ML algorithm that combines two different architectures to produce a better analysis of temporal and spatial relationships in a data set.
• Bidirectional LSTM (Bi-LSTM) combines two LSTM layers, one operating as a forward-looking analysis and the second looking back at previous data. The combination can provide a better context for data analysis.

Unwrapping and filtering
Synchrophasors in compliance with IEEE C37.118 report angles in terms of ±π radians. That’s ok if the system operates at a constant frequency, but on a real transmission grid, the frequency varies in a window around 60 Hz. That can cause the measured angles to drift, and they can jump as the frequency changes, resulting in crossing the ±π limit. This is called wrapping. Unwrapping involves the elimination of discontinuities caused by wrapping that don’t reflect any actual grid conditions.

In addition, there can be spikes in unfiltered phasor measurements that may be caused by load changes or other phenomena and don’t reflect any anomalous conditions on the grid itself. As a result, before phasor data can be used in AI or ML algorithms, it must be unwrapped and filtered (Figure 3). AI and ML can used in the tools for unwrapping and filtering the data.

Summary
AI and ML provide powerful tools for decarbonization of the utility grid. They can be used to improve policy development, market analysis and implementing the technical solutions needed for integrating renewals like real-time monitoring of grid stability and identification of anomalies using synchronphasor measurements.

References
Driving a More Sustainable Future: Transforming Transportation and Energy to put us on a Path to Carbon Neutrality, Hitachi
Energy system digitization in the era of AI: A three-layered approach toward carbon neutrality, Science Direct
Harnessing the Power of Artificial Intelligence for Collaborative Energy Optimization Platforms, MDPI energies
Hybrid AI-based Anomaly Detection Model using Phasor Measurement Unit Data, arVix
Machine learning for a sustainable energy future, Nature Reviews Materials
Why AI and energy are the new power couple, International Energy Agency

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