The Kynix Blog - Power

Overview: This article explores various AC-DC topologies, control strategies, and technical specifications crucial for enhancing efficiency and performance in chargers. It also addresses current challenges and advancements in the field. To achieve a significant reduction in the volume and weight of electric vehicles, off-board chargers must be used for both fast and ultra-fast DC charging. The topologies and control strategies of AC-DC for off-board chargers as shown in Fig. 1 are covered in this article, focusing on technical specifications, current developments, and challenges. Fig. 1: Circuit topology of AC-DC power stage (a-f) Source: IEEE Access The topologies shown here work well with fast DC charging. The rated power of the rectifiers can be increased to satisfy the demand for fast DC charging with an adaptable and appropriate design. Three-Phase Buck-Type RectifierFor an AC-DC rectifier in an electric vehicle charging station, there are critical requirements, such asPower factor correction (PFC)Low THDHigh efficiencyHigh-power density MeritsBecause it can provide all of the above properties, the three-phase buck-type rectifier (TPBR) as shown in Fig. 1(a) is an appropriate option for the AC-DC power stage. Furthermore, when compared to boost-type three-phase rectifiers, TPBR offers anInherent inrush current free startingBroader output voltage control rangePhase-leg shoot-through protectionOvercurrent protection circuit during short circuit DemeritsDistributed parasitic capacitances between the ground and the DC link output are another problem for TPBR when it operates at high frequencies. These capacitances produce input current distortion, particularly under conditions of low load. High step-down voltage gain is generally recommended when comparing different EVs on the road, taking into account their differences in battery range. Because the standard TPBR modulation index is less than 0.5, which increases losses and affects power quality, matrix-based TPBR is a good option in this situation. Swiss RectifierThe Swiss rectifier (SR), a variant of TPBR, is illustrated in Fig. 1(b). MeritsTPRB, with eight switches compared to six switches, offersGreater efficiencyLower common-mode noiseLower conductionLower Switching loss Because of its circuit nature, SR allows for the implementation of DC-DC converter control techniques. Furthermore, space vector pulse width modulation (SVPWM) may be avoided for SR, making control simpler. Interleaving SRs provides advantageous features likeReduces current and voltage rippleReduces filter requirementsIncreases powerHigh bandwidthReliability DemeritsOne of its main drawbacks is that SR only permits unidirectional power flow. However, to enable vehicle-to-grid functioning, bidirectional SR can be constructed at the expense of additional electrical components and a complex structure. Vienna RectifierWhen compared to a three-phase boost PFC rectifier, the three-phase Vienna rectifier (VR) in Fig. 1(c) operates similarly, but the power flow is unidirectional. Three-phase VR is made up ofThree boost inductors at the inputSix fast rectifier diodesSix switches (two per leg)Two split capacitors at the output VR utilizes a bipolar DC bus design, which improves power flow capability. On the other hand, input current distortion must be avoided by correcting the voltage imbalance in the bipolar DC bus topology. The power losses of several VR topologies were analyzed, and the structure shown in Fig. 1(c) had the fewest losses. As seen in Fig. 1(d), the switches are used in place of the diodes to guarantee bidirectional power flow. Another name for this architecture is a three-phase, three-level T-type rectifier. MeritsVR is commonly employed in high-power applications because of itsStraightforward control mechanismHigh power densityHigh power efficiencyUnity power factorReduced-number switchesLow THDNeutral connection-free constructionThere is no need for a dead zone switching drive since the voltage stress on the switches is half that of the DC link voltage. DemeritsEven if it still retains the three-level converter’s advantages, VR shares many of the disadvantages, such as the need for DC-link capacitors. VR frequency is reduced to about 250 kHz for an improved balance between high-power density and efficiency utilizing standard PCB technology. If this limit is exceeded, input current distortion could result, which would lower the quality of grid power. Three-Phase Boost-Type RectifierA three-phase six-switch boost rectifier (TPSSBR) is shown in Fig. 1(e). It hasThree inductors connected in series with a three-phase input AC sourceSix switches on three legs. Inductors are used to increase the input current voltage and decrease its harmonic content. The top and bottom switches are switched in a complementary manner. MeritsThe three-phase boost rectifier is a good fit for the AC-DC power stage of the EV charger because of itsStraightforward designContinuous input currentBidirectional operationHigh-output DC voltageLow current stressFew switchesStraightforward control schemeLow THDHigh efficiency DemeritsThe reverse recovery loss that the antiparallel diodes experience in the TPSSBR makes the switching loss of the MOSFETs worse. To lessen the anti-parallel diodes' reverse recovery loss, an ultra-fast DC rail diode has been incorporated at the DC-link side. This topology also preserves gentle switching, prevents bridge short-through issues, and guarantees automated step-up operation. Zero-voltage transition (ZVT) and zero-current transition (ZCT) TPSSBRs can also be used to provide soft switching as shown in Fig. 1(f). Multilevel AC-DC ConverterResearchers frequently use the multilevel converter (MLC) architecture, which generates alternating voltage levels from many lower levels of direct current voltages. There are three main types of MLC:Neutral Point Clamped (NPC) MLCFlying Capacitor (FC)Cascaded H-Bridge (CHB) MeritsAn MLC converter's fundamental method of operation is to use switches, capacitors, and voltage sources to create a staircase waveform at the output. Because MLC can supply high power with higher efficiency and power density, it is a preferred option for the AC-DC power stage in EV fast and ultra-quick charging applications. Some of the distinctive features of an MLC areLess voltage stress on the switches in high-voltage applicationsLow EMIReduced voltage transition between levelsLow THDSmaller dv/dtMinimization of magnetic components to allow superior performance Summarizing the Key PointsThe article discusses advanced AC-DC power stage technologies tailored for electric vehicle chargers, emphasizing efficiency and performance improvements.It gains a thorough understanding of the crucial role that topologies, control strategies, and technical specifications play in optimizing on-board charging systems.It explores the dynamic evolution of fast and ultra-fast DC charging solutions, addresses current obstacles, and showcases technological advancements.It also showcases the latest developments in onboard chargers that contribute to reducing the volume and weight of electric vehicles, meeting the growing demand for efficient charging solutions. ReferenceSafayatullah, M., Elrais, M. T., Ghosh, S., Rezaii, R., & Batarseh, I. (2022). A Comprehensive Review of Power Converter Topologies and Control Methods for Electric Vehicle Fast Charging Applications. IEEE Access, 10, 40753–40793. https://doi.org/10.1109/access.2022.3166935

Introduction Power electronics are pivotal in efficiently converting, controlling, and conserving electric power across residential, commercial, and industrial applications. Employing solid-state electronics helps adjust motor speeds, maintain uninterrupted power flow, enable high-frequency power supplies, integrate renewable energy, and positively impact energy usage from electric vehicles to data centers and spacecraft systems to high-speed rail; power electronics touch every arena. At the epicenter of this technology are semiconductor-switching devices like diodes, MOSFETs, IGBTs, and thyristors that shape and regulate power flow. Two stalwarts dominate for medium to high power needs - the metal-oxide-semiconductor field effect transistor (MOSFET) and the insulated gate bipolar transistor (IGBT). Selecting a suitable device is crucial to optimize overall system performance. This article provides a comparative analysis of these two technologies to help design engineers make an informed choice. Understanding Power MOSFETsPower MOSFETs are specialized transistors designed to switch on/off rapidly, allowing precise and speedy power transfer control. They can transition between cut-off and saturation modes in nanoseconds. This swift switching capability stems from their unique insulated gate structure, requiring minimal gate current to trigger state changes. Built-in body diodes facilitate the continuous conduction of load currents in either direction. Silicon has traditionally been the mainstream material, but new comprehensive bandgap materials like silicon carbide and gallium nitride promise significantly higher efficiency. With high breakdown strength, lower losses, and higher junction temperature capacity, these advanced materials drive a significant shift in power electronics. Exploring IGBT DevicesInsulated gate bipolar transistors (IGBTs) aim to combine the best attributes of power MOSFETs and bipolar junction transistors. They integrate the simple gate control of MOSFETs with the superior high current handling capacity of BJTs. A key feature enabling high collector current density is conductivity modulation, where electron and hole injection sustains current flow. However, this also slows down switching transients. The insulating layer blocks high voltages but leads to larger chip sizes. Modern IGBTs lower losses through innovations like trench gates, carrier lifetime control, and field stop layers. Advanced packaging technologies also boost power density and thermal performance. But slower switching speeds and conduction losses at low currents remain innate drawbacks. Comparing Key Application DomainsMOSFETs' ultrafast and controllable switching ability makes them perfect for switch mode power supplies (SMPS), Class D audio amplifiers, DC-DC converters, and lighting controls needing precise regulation. These applications demand fast dynamic response and low losses at moderate voltage and current levels.IGBTs, on the other hand, are extensively used in motor drives, uninterruptible power supplies (UPS), electric traction systems, wind turbines, HVDC transmission, and high power factor correction equipment. These applications require ruggedness to withstand network voltage fluctuations, high DC link voltages, and surge currents during motor commutation or load changes. IGBTs can reliably handle hundreds to thousands of amperes thanks to conductivity modulation but at the expense of switching speed. Analyzing Switching CharacteristicsMOSFETs can transition between on and off states extremely fast, within nanoseconds. This enables them to comfortably operate at frequencies in the MHz range for switch mode operations. However, their switching speeds are limited by charging and discharging intrinsic capacitances across drain, source, and gate terminals during the high di/dt and dv/dt transients.In contrast, IGBTs switch on and off much slower - in the range of microseconds to milliseconds, depending on load conditions. Their switching times are dictated by minority carrier injection and storage dynamics during turn-on and turn-off, respectively. The conductivity modulation mechanism in IGBTs that enables efficient high current operation also adds more delay during transients. Cost, complexity, and application-specific demands impact device selection, too. Analyzing Conduction LossesMOSFETs offer shallow conduction losses at nominal currents, enabling high efficiency. This stems from majority carrier transport through the drain-to-source channel unimpeded by minority charge storage effects. However, the drift component of on-state resistance limits efficiency at high currents due to velocity saturation.In contrast, IGBTs showcase deteriorating conduction losses at low currents but start outperforming MOSFETs above a few amperes current. This reversal occurs due to conductivity modulation wherein electron and hole injections sustain rising collector current density. IGBTs skip past velocity limits at high currents to achieve significantly higher efficiency. Rating on Voltage and Current MetricsLatest generation SiC MOSFETs boast blocking capabilities exceeding 1.7 kV, while GaN variants enable 1.2 kV switch-mode supplies. Commercial IGBT voltage ratings range from 1.2 kV to 1.7 kV presently. However, IGBT packages reliably exceed 1000 A without secondary breakdown concerns for conducting hundreds of amperes. MOSFETs lag on current density metrics presently. Sensitivity to High-TemperaturesIGBT performance depends significantly on temperature swings and self-heating, needing careful thermal management. MOSFETs show lower sensitivity thanks to the absence of conductivity modulation effects. But hotspots can still accelerate aging and degrade long-term MOSFET reliability over time. Cost Considerations Thanks to process maturity, MOSFET design and production costs have been considerably reduced, making them economical for low- and medium-power applications. However, large-area silicon IGBTs can be fabricated at lower costs to score over MOSFETs in high-voltage, high-current areas. Emerging devices like SiC MOSFETs and GaN transistors promise tremendous performance gains but remain expensive. Gazing into the FutureWith continual advances in device structure, doping profiles, and material quality, MOSFET and IGBT technologies are poised to realize higher efficiency, power density, and reliability metrics. Novel cooling techniques leveraging direct liquid immersion or integrated microchannel heat sinks are being explored to dissipate heat from smaller footprints. Clever gate driver techniques and modern packaging methods will help extract the full potential from both devices. Another active area is developing hybrid modules that combine IGBTs and SiC MOSFETs to leverage their complementary strengths for optimal overall performance. The future looks brighter with the increasing maturity of wide bandgap devices and greater systems-level integration! Making the Optimal ChoiceMOSFETs excel for applications demanding nimble and accurate load control, typically up to a few kilowatts. IGBTs are the bedrock where large voltage blocks and high surge current capacity warrant extra ruggedness. However, cost budget, cooling challenges, reliability requirements, and desired switching frequencies also guide decision-making. Designers must weigh tradeoffs between conduction losses, switching frequencies, thermal management complexity, and hardware overheads while selecting the optimal power semiconductor switch. Conclusion In the vast power electronics landscape, MOSFETs and IGBTs remain the primary switching devices for most applications. MOSFETs stand out in environments needing nimble and accurate switching control up to a few kilowatts. IGBTs are the bedrock for systems where large voltages and surge currents demand extra ruggedness. Device selection requires carefully weighing metrics like losses, operating frequency, cooling needs, and costs. With continual technological upgrades, these devices will continue transforming future power management solutions.

Overview: The article highlights the trade-off between power efficiency and electromagnetic noise, which can have a significant impact on the sensitivity of wireless receivers. The article includes a study of GaN-based power modules and provides guidelines. Compared to conventional silicon (Si) devices, wide band gap (WBG) semiconductors like gallium nitride (GaN) have become commonly used in power supply electronics. In contrast to conventional Si, WBG semiconductors (such as GaN) offer better material qualities and can operate power devices at greater temperatures, higher voltages, and quicker switching rates when used in the power supply's output stage. As a result, WBG semiconductors increase the efficiency and compactness of power modules, which leads to their widespread adoption in a range of applications, including robotics, automotive electronics, and the Internet of Things. What is the impact of electromagnetic noise on wide-band devices?Faster switching and higher voltage produce less energy loss, but they also result in more power noise because of the periodic switching currents that flow through power semiconductors. This means that there is no way to avoid a trade-off between noise emissions and power efficiency.Role of Electromagnetic Inference and Electromagnetic CompatibilityIn close proximity to one another, this also causes issues with near-field electromagnetic interference (EMI) between electrical components. Power modules using WBG devices, such as GaN and SiC, are maturing faster than ever, but it is also important that the EM compatibility (EMC) measurements have a wider frequency range. Up to 1 GHz is typically the frequency range in which power module EMC requirements are established. Electrical noise (EM noise) can have a big effect on the sensitivity of wireless receivers supporting LTE when they are close, like within a few meters. EMI between wireless communication systems and WBG semiconductors has become a widespread issue with IoT devices. The article includes an EM noise study of GaN-based power modules in the frequency band (up to 6 GHz) for mobile communications.Experimental Setup of Gallium Nitride Power ModuleThis research involves the preparation of two power modules, calledGaN module AGaN module B These modules comprise isolated gate drive circuits employing CMOS devices and GaN-based half-bridge circuits. Although the two modules share the same block architecture in Fig. 1, the assembly structures differ based on the individual design parameters.  Configured as a half-bridge circuit, the output stage is filled with two discretetransistors based on GaN technology. Gate drive circuits are the key component of the control unit. A pulse pattern generator controls the amount of duty and frequency of pulse messages that come in. The external source signals used in this experiment had the following configurations for their parameters: 1) 0 and 12 V for the primary power supply;2) 100 kHz and 1 MHz for the pulse frequency;3) 50% for the pulse duty ratio.Measurement of Electromagnetic NoiseResearchers utilize a magnetic field probe to capture the near-field electromagnetic noise (EM noise) from the device under test (DUT). Everything is enclosed in an anechoic cage to block out surrounding noises. The high-sensitivity measuring method served as the basis for this measurement setup. In order to cover the wireless communication bands for fifth-generation (5G) and LTE wireless systems, the frequency range of interest is 6 GHz. To keep things simple, the measurements below were taken at the power module's output stage with no load. The EM sources are put to the test in a variety of operating conditions by sending source signals and probing at different points in the GaN module assembly. By changing the external signal source's settings, the power supply module was able to function in two distinct modes.Module AOne was established as the basic operational condition, withMains: 12 VOperating frequency: 100 kHzDuty ratio: 50%, with all circuits driven.Hence, the control unit and the GaN device were monitored for their radiated noise. Module BOn the other hand,The GaN device's switching function is disabledThe main power supply is set to 0 V In this instance, the control unit's noise component is the only radiated noise that is visible. So, the source of the radiated noise in the power supply module was studied by changing the state of the circuit's operation and comparing the noise components that were picked up. The above experiments (Fig. 2 and Fig. 3) show what happens when the output stage is not working (the red line does not include EM noise from the output stage) and when it is working (the blue line includes EM noise from the output stage and the control unit).   Results And ConclusionA spectrum analyzer measures the average electromagnetic noise, as Fig. 2 illustrates. Below 1.5 GHz, electromagnetic noise from the output stage is detected. Harmonic components of the switching frequency that the pulse generator sets are primarily responsible for this noise. A two-sided structure was used to look at the frequency characteristics of EM noise coming from GaN module B's control unit and output stage on the right side. As shown in Fig. 3, EM noise from the output stage was primarily detected below 2 GHz. The main sources of noise areAn output stage with WBG power transistors that switch periodically.The control and gate driver stages have CMOS digital circuits that get their clock signal from outside or even inside the chip. The EM noise from the output stage usually takes up most of the lower frequency side, as seen in Fig. 3. The frequency range and noise level of EM noise based on GaN transistors change based on how fast the switching power modules are running. While the noise from the control circuit is more likely to be on the upper frequency side, as seen in Fig. 2. In conclusion, control circuits in switching modules as well as output stage circuits are the targets of noise controls for wireless communications. The intrinsic characteristics of circuit architectures determine the electromagnetic noise of the control unit, which is independent of the power supply module's operational circumstances. This necessitates doing an EM noise evaluation on a particular product and customizing EMI countermeasures for it. Summarizing the Key Points●Gallium nitride technology revolutionizes power supply electronics with its superior material qualities, enabling higher operating temperatures and faster switching rates.●The trade-off between power efficiency and electromagnetic noise is a critical consideration when utilizing gallium nitride based power modules.●Electromagnetic interference between electrical components, particularly in the frequency band up to 6 GHz, necessitates thorough evaluation and implementation of control measures.●The intrinsic characteristics of circuit architectures determine the electromagnetic noise of the control unit, highlighting the need for customized electromagnetic interferance countermeasures tailored to specific products. ReferenceWatanabe, Koh, Misaki Komatsu, Mai Aoi, Ryota Sakai, Satoshi Tanaka, and Makoto Nagata. “Analysis of Electromagnetic Noise From Switching Power Modules Using Wide Band Gap Semiconductors.” IEEE Letters on Electromagnetic Compatibility Practice and Applications 4, no. 4 (December 2022): 92–96. https://doi.org/10.1109/lemcpa.2022.3207234.

Overview: The article discusses the importance of maintenance in ensuring the reliability and safety of power electronic systems. It outlines the steps involved in maintenance, including condition observation, anomaly identification, defect diagnosis, and remaining useful life prediction. Power electronic systems are subject to a variety of risks, including catastrophic failures, despite the careful consideration of dependability characteristics during design and control. This is because of the complex and demanding operating settings of power electronic systems. For field applications, power electronic components, converters, and systems must be extremely reliable and safe. What are the steps in maintenance to make the power electronic system more reliable?Preventive maintenance systems are useful ways to guarantee that planned functions are carried out as intended. The steps in maintenance of power electronic system includesCondition observationIdentification of anomaliesDiagnosing defectsRemaining Use Life (RUL) predictionThe above actions coincide with the IEEE standard framework of prognostics and health management for electronic systems. Condition ObservationPower electronics condition observation consists ofIdentification of system parametersPreprocessing dataMining featuresThe data from the condition observation is used to discover informative and hidden patterns that form the foundation for the prognostic and health management applications that follow. Identification of System ParametersIdentification of system parameters involves the gathering of data for important components.Characteristics of power electronic systems includesExtremely small space inside a power moduleExtremely fast switching frequencyRelatively insignificant parameter changes in terms of aging, etc.Because of these characteristics, developing specific hardware for parameter identification is quite a challenging task.A noninvasive approach that uses existing physical signals to indirectly get information or estimate relevant information without the need for additional hardware implementation is one of the more promising methods.Therefore, a sensorless and cost-effective option can be used for condition monitoring, which is good for people who work in industry. In general, there are two types of methods for identifying system parameters:Model-freeModel-based. Preprocessing data and Mining featuresThe goal of data preprocessing and feature mining is to improve the quality of the raw data so that it can be used for applications like problem diagnostics.Improving the quality of data involves the following steps to make it more organized. The steps are as followsData cleaning to minimize noiseData clustering is used to find groups of related data pointsDensity estimation is used to determine the distribution of the dataData compression to reduce the number of features by projecting large-sized data to small-sized dataData fusion to combine various information sources, and moreWhen data preparation and feature mining are done correctly, the performance of the ensuing prognostics and health management applications—such as diagnostic accuracy—can usually be greatly enhanced. Identification of Anomalies and Diagnosing DefectsThe anomaly detection process focuses on identifying unusual patterns and making a binary decision. When the nominal parameters or rated system characteristics exceed the predetermined safety range, it gives an indication.The fault diagnosis finds and identifies the specific failure modes after the unusual changes happen.The classification, regression, or clustering tasks are essentially anomaly detection and fault diagnosis. When a new fault signature arrives, it identifies the fault label based on the learned relationship from the training stage.Anomaly detection and fault diagnosis techniques fall into two categories:Supervised learningUnsupervised learning Remaining Useful Life (RUL) PredictionIn the design phase, lifetime prediction serves to support the characteristics of a population of units known as the ‘Design for Reliability’. It is one of the crucial components of prognostics and health management.The purpose of the estimation of RUL is not to accurately predict the lifespan of a population of units. Based on condition monitoring data, it predicts the remaining lifespan of each single unit in operation. For applications where availability, safety, or reliability are crucial, RUL prediction is used as an extra tool to lower uncertainty.The lifetime estimate is subject to several challenges, such asInaccuracies in model calibrationManufacturing tolerancesDifferences in operational environments and workloadWhen a particular unit is operated in the field, these uncertainties lead to inaccurate reliability estimations. The following areas require greater attention in order to improve the practicality of AI-based RUL prediction techniques for field applications. Quantification of uncertaintyFor RUL prediction, being able to measure uncertainty is more important than for other regression-related tasks, like control functions. Since the RUL is a random variable, quantifying the confidence interval is crucial for making the best decisions.All of these uncertainties—due to population heterogeneity, measurement noise, various operating settings, etc.—should be considered in a workable practical solution. Quantifying the uncertainty using AI algorithms is quite difficult.A few practical options areThe use of particle filters in neural networks (NNs)Bayesian-based artificial intelligence techniques (e.g., Gaussian process, RVM)Monte Carlo methodsStochastic data-drivenStochastic, data-driven approaches are an interesting option to explore. These approaches can naturally yield the probability density function of the RUL for the purpose of computing the confidence interval. Adaptive capabilityThis is the crucial stage for real-world applications and is related to the model parameter tuning layer in Fig. 1 that connects the offline and online models. If an AI approach lacks adaptive flexibility, its use is limited.Power electronics have difficulties because the operational conditions of the training dataset, which is often acquired through accelerated testing trials, differ significantly from those of the in-situ system (i.e., the test data). Most of the research makes the assumption that the in-situ system's operational parameters are the same as those of the training dataset, which could not be the case in real-world applications.Therefore, the AI-based RUL prediction method's adaptability is essential for bridging the gap between research in academia and practical implementations in industry.Detailed mapping relationship derivations and transfer learning of degradation characteristics under different operating settings (temperature, voltage, humidity, etc.) are also interesting ways to tune model parameters. This means that system models need to be studied in great detail.Fig. 1 shows a methodical flowchart of power electronic system maintenance tasks. It typically comprises the three elements listed below.             Summarizing the Key PointsMaintenance of power electronic systems involves condition observation, anomaly identification, defect diagnosis, and remaining useful life prediction to ensure reliability and safety.The IEEE standard framework for prognostics and health management is applicable to power electronic systems, emphasizing the importance of a comprehensive maintenance approach.Data preprocessing and feature mining are crucial for improving the quality of raw data, enhancing the performance of prognostics and health management applications.AI-based remaining use life prediction techniques face challenges in real-world applications, requiring quantification of uncertainty and adaptability.Power electronic systems require an adaptive maintenance strategy to bridge the gap between research and practical implementation in industry, addressing operational parameter variations. ReferenceZhao, Shuai, Frede Blaabjerg, and Huai Wang. “An Overview of Artificial Intelligence Applications for Power Electronics.” IEEE Transactions on Power Electronics 36, no. 4 (April 2021): 4633–58. https://doi.org/10.1109/tpel.2020.3024914.

Overview: This article discusses the challenges faced by smart grids. It also briefs on how the Energy Internet and the use of blockchain and IoT technologies are potential solutions to smart grid security challenges. A decade ago, the idea of a "smart grid" was the foundation of bright dreams, now, it's the most talked-about issue in the industry of renewable sources. The smart grid is a multidimensional energy infrastructure idea that can be implemented using a wide range of available technologies. The incorporation of a "smart grid" into today's electrical infrastructure is crucial for the following reasons: What are the challenges faced by smart grids?Skepticism Among Industries First of all, industries are still hesitant about the advancement of smart grid projects. The misconception among industries is that government commitments cannot be fulfilled and that smart grid projects are moving slowly forward. Furthermore, despite the fact that governments fund the creation and testing of smart grid pilot projects, the industries engaged in the installation of these projects have little passion for investing in the technology, which has an impact on the system's development. Security Issues Second, there are numerous security risks and associated difficulties that can affect the architecture and infrastructure of smart grids. Threats and difficulties include terrorism, theft, disasters caused by nature, and cyberattacks. An actual security breach may result inPower outagesA breakdown in the information and technology infrastructureDisruption in the power marketNetwork cascade failureEndanger human safety In summary, issues with technology privacy, permission, and authentication are identified as smart grid security challenges. The Energy Internet may also have similar problems, but using technologies like blockchain and the Internet of Things (IoT) should make security breaches less likely and less harmful, and they should also make recovery easier with little assistance from humans. Decreased Penetration of Electric Vehicle Thirdly, a barrier to the widespread use of electric vehicles in the energy sector is the low market penetration of these vehicles with vehicle-to-grid (V2G) capability. Repeated charging and discharging of the battery is necessary for effective V2G operation, which results in battery deterioration. Even though scientists are optimistic about lithium-ion (LFP) batteries, more study is needed to determine how to maximize the battery life of V2G-enabled vehicles for the technology to be implemented effectively. Complexities Posed by Microgrid Fourth is using micro-grids to improve smart grids. The installation of microgrids with smart grids presents few technological and regulatory hurdles. Inbalanced supply and demand can lead to issues with frequency and voltage in microgrids. When generators are connected and disconnected using a "plug-and-play" feature, these issues may worsen. Variations in the power production from the connected renewable energy systems make it difficult to maintain a steady state for the microgrid. Furthermore, a greater proportion of renewable energy could cause transmission and distribution difficulties in the current network. The incorporation of suitable protection devices becomes essential as the system becomes more complicated. Because micro-grid infrastructure comprises a bi-directional power flow, the protection mechanism differs from standard power systems. Additional information on micro-grid protection schemes should also be considered. Development of Strandards Lastly, it is necessary to address the issues raised by the regulation of communication devices, cyber-security devices, and compatibility and conformity to standards. Countries have assigned various groups the task of creating standards for smart grid interoperability. The design, development, and production of devices that meet international standards is one of the main obstacles to deploying smart grid infrastructure. The Energy Internet The Energy Internet is allegedly able to solve many of the aforementioned problems. It serves as the energy system's forthcoming revolution. It will make it possible to put less focus on large-scale centralized power generation and more on numerous tiny, dispersed generation systems. Government investment in generating facilities may be minimized as a result of prosumers now owning a larger portion of the power generation industry. Households and other small-scale users who can construct local power plants to buy and sell electricity are encouraged to invest via the Energy Internet. By doing this, governmental organizations' investment burden is lessened when they spend on building infrastructure. It provides advanced capabilities to facilitate flawless electricity exchange through the Energy Internet. Current security threats and challenges are addressed when this infrastructure is supported by innovative technologies like blockchain and IoT. However, as technology develops, new security threats are probably going to appear, and ongoing cybersecurity innovations are going to address them. Research in the field of Energy Internet helps optimize storage devices to reduce battery wear. The Energy Internet can also use distributed energy systems management algorithms to best address ongoing smart grid issues brought on by the unpredictable and variable nature of renewable energy systems. Future integration of artificial intelligence (AI) and machine learning (ML) algorithms into the Energy Internet, which provide additional support. Lastly, government agencies must coordinate with other relevant international entities to address the concerns of standardization and interoperability. Energy Internet can fill up the gaps left by the smart grid's shortcomings. Management of Energy Internet Markets The markets for green gas, liquid fuels, and renewable heating in the future will affect the power market. The Energy Internet has the ability to reconfigure itself into a multi-energy system in this regard. A fully operational energy market for the energy cells can be integrated into the Energy Internet architecture. As an illustration, the current electricity exchanges in some countries operate using an auction-based bidding system. This technique works well in static liberalized markets where it is simple to predict the market structure and network architecture. Energy cells that are integrated with the Energy Internet, however, are diverse in character and have competing objectives. Auction-based bidding might not be an effective market mechanism given this feature. Game-Theoretical Algorithms A real-time power price that reflects the dynamic supply and demand balance is one potential option. The selection of game-theoretical algorithms to establish an appropriate real-time pricing mechanism for trading among energy cells on the Energy Internet is one suitable option. Game theory models have been used to examine studies that deal with disagreements involving interactive decision-makers. In recent years, the scalability of game-theoretic algorithms has facilitated their widespread use in energy market design. The mathematical model for the day-ahead market for the competitive energy cells was developed using the Nikaido-Isoda function (NIRA) and the Relaxation algorithm. For more than three decades, businesses have relied on a specific group of numerical algorithms known as relaxation algorithms. Earlier efforts in the relaxation method greatly illustrate the technique's quick convergence and reasonable accuracy. The bilateral Shapley value and kernel are used to make sure that profits are shared fairly among consumers who work together. Blockchain Technology Virtually anything of value can be recorded in the blockchain, an uncorruptible digitally distributed ledger of economic transactions. The shared ledger that is published to every member is the foundation of how blockchain technology operates, as shown in Fig. 1. Fig. 1. Centralized transaction vs. blockchain transaction Source: IEEE Access It uses smart contracts to make sure that participants follow the rules, a distributed consensus method to make sure that everyone agrees on the proposal, and cryptography-based safety measures to make trade easier. As a result, it offers the customer a private cybersecurity solution that is strong and resilient. Additionally, blockchain reduces the possibility of double-spending that comes with digital currencies. The computation-intensive algorithm is necessary to mitigate the possibility of double-spending. New blocks are added to the blockchain, and transactions are validated using this computational technique. Specialists compete with one another to solve problems and validate these transactions. Additionally, blockchain offers attributes likeTransparency, which makes data easily auditable,Redundancy, which distributes a copy of data to all participants to prevent third-party malpractice,Immutability, which makes record alteration exceedingly difficult,Disintermediation, which does away with intermediaries like banks or energy utilities,Blockchain technology offers continuous traceability of all energy transactions as well as a comprehensive transaction record for the energy markets. But there are still some issues with the technology. Among the difficulties are those related toDigital data and metadata storageNetwork effect problemsCopyright disputesLegal concernsSummarizing the Key Points The Energy Internet can address ongoing smart grid issues brought on by the unpredictable and variable nature of renewable energy systems.Prosumers owning a larger portion of the power generation industry can minimize government investment in generating facilities.Blockchain and IoT technologies can make security breaches less likely and less harmful, and they should also make recovery easier with little assistance from humans.Disintermediation and blockchain technology offer continuous traceability of all energy transactions as well as a comprehensive transaction record for the energy markets.Difficulties related to digital data and metadata storage, copyright disputes, network effect problems, and legal concerns still exist with blockchain technology.Reference Joseph, Akhil, and Patil Balachandra. “Smart Grid to Energy Internet: A Systematic Review of Transitioning Electricity Systems.” IEEE Access 8 (2020): 215787–805. https://doi.org/10.1109/access.2020.3041031.

Overview: The article discusses the SC robustness, surge energy, and overvoltage robustness of GaN HEMTs. Additionally, the article highlights recent achievements in ultrafast SC protection circuits and alternative circuit approaches. For many applications, including motor drives, automobile powertrains, and electric grids, the ability of power devices to stand up to overvoltage, overcurrent, and surge-energy events is a crucial need for robustness. For Si and SiC power transistors, UIS (avalanche) and SC tests are typically used to measure robustness.  Does gallium nitride possess SC robustness? It is known that GaN HEMTs lack avalanche capabilities and have restricted SC robustness. Furthermore, compared to Si and SiC devices, GaN HEMTs behave considerably differently in terms of stress tolerance and failure under specific out-of-safe-operating area situations.  The SC robustness, surge energy, and overvoltage robustness of GaN HEMTs will be discussed. Fig. 1 shows an illustration of GaN SP-HEMT and GaN HD-GIT.Fig. 1. Illustration of (a) GaN SP-HEMT and (b) GaN HD-GIT. Source: IEEE Transactions on Power Electronics SC Robustness When there is a conduction path with minimum resistance between the power source and the switching transistor, SC fault occurrences take place. SC events typically drive devices into saturation mode, which stresses the device with high voltage and high conduction current. Objectives Standard SC robustness criteria are:10 μs SC withstanding time (tSC) under the bus voltage (VBUS) The driving conditions must be identical to the application-use operation.Note: The U.S. Department of Energy 2025 Vehicle Drive Roadmap states that a 2 μs tSC of the power device along with the ultrafast protection circuit is required if the 10 μs tSC is not achievable. Types of SC Robustness In power electronics systems, there are typically four types of SC situations that can occur: Arm SC, also known as the hard-switching fault (HSF) or SC type ISeries arm SCOutput SCGround SCHSF is typically used in these situations to assess the robustness of the SC power device. The findings of repeated SC tests, failure modes, and single-event tSC for GaN HEMTs are compiled in this section. Reasons for Restricted SC in GaN HEMT A lot of work has been done to figure out what limits the SC capability of GaN HEMTs, especially when the bus voltage is high. Devices fail thermally in long SC duration tests with low bus voltage. At high bus voltages, several reports point to an electrical failure.  It is suggested that the high electric field produced by the hole accumulation beneath the gate—where the holes are produced by impact ionization—may be the reason for the SC failure. The relationship between electric field crowding at the drain-side gate edge and the high carrier density caused by the SC has been reported. A wafer-level transient voltage measurement keeps track of the potential profile in the gate-drain region under SC stress.  It is found that the failure is dependent on the speed at which the electric field propagates; impact ionization causes the failure when a high electric field reaches the drain edge. Results of Repetitive SC stresses on GaN HEMTs It has been documented that GaN HEMTs are not sufficiently robust to repetitive SC stresses within the single-event SC SOA. In SP-HEMTs, the repetitive SC stresses cause a decrease in drain-leakage currents and a rise in on-resistance (RDS,ON) at lower bus voltages. All of these parametric shifts point to the possibility of electron trapping during the repetitive SC operation in the buffer and gate areas. In HD-GIT repetitive SC tests, the progression of developing cracks and aluminum extrusion at this load has been seen.In cascode HEMT, two additional strategies have been identified to constrain the SC robustness The first thing that can happen is that the parasitics of the Si-GaN chip interconnection can cause the self-sustained gate oscillation to excite. This can make the GaN HEMT turn on by accident and fail. Secondly, the cascode HEMT's thermal self-regulation capability on the gate control is lower than that of HD-GITs and SP-HEMTsMethods to Overcome SC Faults Protection circuits must be included for applications where the SC fault may arise due to the short SC withstanding time of contemporary GaN HEMTs. Within 100–200 ns, the protection circuit should identify the issue and clear it. Conventional desaturation circuits have a long response time, which makes it difficult to achieve this. Ultrafast SC protection circuits for GaN HEMTs have recently been achieved by several groups. These circuits typically exhibit fault detection and clearance times of less than 100 ns. Some other good qualities that have been talked about are strong dv/dt noise immunity, use with parallel-connected GaN HEMTs, and monolithic integration with the GaN device. Alternative circuit approaches to improve the SC capability in addition to quick protection are also suggested, such as coupling the GaN HEMT to a Si mosfet.Device-level enhancements have also been reported to enhance the SC withstanding time of GaN devices, in addition to circuit techniques. Removing parts of the 2DEG channel along the width of the GaN HEMT is an easy way to minimize the saturation current. With this method, an SC withstanding time over 3 μs is possible in industrial cascode GaN HEMTs. Surge Energy Power devices would greatly benefit from the ruggedness against surge energy in addition to SC robustness. Si/SiC MOSFETs and IGBTs have relied on their avalanche ability—an impact ionization and multiplication effect—to support high current at high drain-to-source bias. Why is surge energy important for power devices? When devices are exposed to surge energy, drain-to-source bias quickly climbs to and clamps at avalanche breakdown voltage. Avalanching in the device causes the drain current to decrease to zero and the surge energy to be resistively dissipated. The dissipation of energy stops converters from circulating energy further. For this reason, avalanche ruggedness is another name for surge-energy ruggedness. An essential indicator of device robustness is avalanche energy, which is the maximum energy that a power device can dissipate without causing a thermal runway. Surge Energy in GaN HEMTS However, the intrinsic avalanche capacity is absent from GaN HEMTs. The JEDEC JC 70 committee has just identified their surge-energy robustness as a crucial evaluation problem. GaN HEMTs show a quick rise in drain-to-source bias when they are exposed to surge energy. This is because of the resonance between output capacitance and parasitic inductance in the circuit.  This standing process cannot release energy until the resonance voltage drops, which causes the GaN HEMTs to turn on in reverse. The device's overvoltage margin is the principal cause of electrical failure in the withstand process.  The convergence of overvoltage and surge-energy robustness for GaN HEMTs is demonstrated in the discussion above. GaN HEMTs can generally tolerate higher surge energies at the expense of slower switching speed when they are constructed with a larger output capacitance and a higher dynamic breakdown voltage. Any nonavalanche power device can be designed or chosen with this tradeoff in mind for a variety of applications. Summarizing the Key PointsUIS (avalanche) and SC tests are typically used to measure the robustness of Si and SiC power transistors. GaN HEMTs lack avalanche capabilities and have restricted SC robustness compared to Si and SiC devices. Standard SC robustness criteria include 10 μs SC withstanding time under the bus voltage and identical driving conditions to the application-use operation. Recent achievements in ultrafast SC protection circuits for GaN HEMTs and alternative circuit approaches have improved SC capability. And, device-level enhancements have been reported to enhance the SC withstand time of GaN devices.Surge energy, which is the maximum energy that a power device can dissipate without causing a thermal runway, is also important for power devices in addition to SC robustness since it is an essential indicator of device robustness.GaN HEMTs can generally tolerate higher surge energies at the expense of slower switching speed when they are constructed with a larger output capacitance and a higher dynamic breakdown voltage.ReferenceKozak, Joseph Peter, Ruizhe Zhang, Matthew Porter, Qihao Song, Jingcun Liu, Bixuan Wang, Rudy Wang, Wataru Saito, and Yuhao Zhang. “Stability, Reliability, and Robustness of GaN Power Devices: A Review.” IEEE Transactions on Power Electronics 38, no. 7 (July 2023): 8442–71. https://doi.org/10.1109/tpel.2023.3266365.