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.

Overview: This article proposes a wind-solar hybrid power system that combines solar and a wind turbine power dispatching system that uses a battery and supercapacitor hybrid energy storage subsystem in the process of cost minimization.The proposed wind solar hybrid power system (WSHPS) architecture, which combines a wind energy system (WES) and a photovoltaic energy system (PVES), is shown in Fig. 1.Architecture of Wind Solar Hybrid Power SystemThe PVES has a 1 MW PV array, a maximum power point tracking (MPPT) controller, and a unidirectional DC/DC boost converter. An AC/DC rectifier, a pitch angle controller, and a 1.5 MW direct-drive three-phase permanent magnet synchronous generator (PMSG) linked to a wind turbine make up the WES.Fig. 1. A wind-solar hybrid power system with HESS Source: IEEE AccessPhotovoltaic Energy SystemThe output of the PV array is very sensitive to two environmental factors: PV irradiation and PV cell temperature. MPPT with incremental conductance (IC) controls the duty ratio of the unidirectional boost converter to draw the maximum amount of power from the PV array. In contrast to the more traditional methods used to extract maximum power from PV systems, an IC MPPT is easy to implement and very effective. As a result, IC MPPT has seen widespread application despite the fact that it can cause slight fluctuations in the maximum power point. One nonlinear device that can be modeled as a current source is a photovoltaic cell. The PV output power and capacity factor are both negatively affected when the PV cell temperature is higher than the ambient temperature.Wind Energy SystemThe WES consists of a wind turbine (WT), permanent magnet synchronous generator (PMSG), pitch angle control, drivetrain, and power converter. Without a gearbox, the WES-based PMSG can connect to the WT. PMSG, based on WES, utilizes a two-step process for energy conversion. The WT blades first convert the kinetic energy into mechanical energy. The second step is for the shaft to transmit the mechanical energy to the PMSG, which then uses the energy to generate electricity.LCL FilterTo satisfy smart grid regulations, an inverter's interaction with the grid additionally necessitates a small output harmonic filter. Because of its superior efficiency and ability to dampen harmonics, an LCL filter has been developed.Calculating the Dispatched PowerFurthermore, the WT's output is proportional to the wind speed passing through the rotor. The real solar irradiance, temperature, and wind speed data recorded at NREL to forecast the dispatched power hour by hour for a full day is expressed as PGrid,ref. Therefore, the WSHPS and HESS will continue to contribute the required amount of power to the utility grid throughout each hourly dispatching period. The WSHPS relies on both the PV array and the WT system to generate an average output power throughout each dispatching period.Dispatchable Power from Photovoltaic Energy SystemThe average output power of the PV array is calculated for each dispatching period using the average irradiance and temperature from the NREL solar statistics inputs. Input factors, including solar cell type, number of parallel cells, and number of series cells, as well as environmental circumstances, are used by the PV array module in Matlab/Simulink to generate power-voltage characteristic curves. NREL's solar data has a resolution of one sample per minute. To generate solar data with a resolution of 120 samples/minute, the cubic spline interpolation method is used. After that, the mean operation method is used to get the average irradiance and temperature for each dispatching time. PPVES,est is the estimated power of the PVES derived from the average irradiation, whereas ηPVES,est is the estimated efficiency of the PVES derived from the average temperature. The ultimate estimated power dispatchable by PVES (PPVES) can be written as follows: PPVES = PPVES,est * ηPVES,est    (1)Dispatchable Power from Wind Energy SystemSimilarly, the estimated WES dispatchable power (PWES) is determined. Based on user input parameters such as base wind speed, base rotational speed, blade pitch angle, and maximum power at base wind speed, the WT model in MATLAB/Simulink gives the WT power characteristic curve. Then, the average wind speed is obtained using the mean operation and cubic spline interpolation methods. The PWES is an estimated power output based on the average wind speed. Finally, Equation (2) is used to determine the typical power output of the wind solar hybrid power system, which is expressed as PWSHPS. PWSHPS = PPVES + PWES    (2)Hybrid Energy Storage SystemEach ESS is connected to a bidirectional DC/DC converter, and the HESS is paired in parallel with the WSHPS. Parallel connections between the WSHPS and HESS and the DC-link capacitor bank that functions as the DC bus lead to a three-level T-type inverter that provides clean, stable DC power. By regulating the current through the power converters, it is possible to regulate the output power from the WSHPS and HESS in this architecture. Because of its great efficiency, low total harmonic distortion (THD), and lower common-mode voltage, a three-level T-type inverter is used. Controlling the system power that is fed into the utility grid is the responsibility of the HESS. Calculating the HESS reference power (PHESS,ref) is as simple as subtracting the PGrid,ref from the PWSHPS: PHESS,ref =  PGrid,ref - PWSHPS   (3) Rapidly fluctuating power components can severely shorten a battery's service life. To assign high-frequency power reference components for the supercapacitor energy storage system SESS (PSESS,ref) and low-frequency power reference components for the battery energy storage system BESS (PBESS,ref), the PHESS,ref is supplied through the LPF. In addition, when the ideal value of depth of discharge (DOD) is determined, a rule-based state of charge (SOC) control algorithm is used to keep the BESS SOC within the optimal range (DOD optimum). As with the SESS, after the best value of DOD has been determined, a rule-based SOC control algorithm is put into place to govern the SESS SOC.HESS DOD OptimisationThe DOD and the rate of change of the charging-discharging power are the two most important factors in determining the ESS's useful life. There is an almost exponential link between cycle life and DOD consumption. There are two primary determinants of ESS costs: (i) the ESS's expected service life and (ii) the ESS's minimum capacity. The minimal capacity of the BESS increases as the DOD decreases in use. However, the BESS's service life decreases with increasing discharge depth. Thus, the simulations are run with all possible values of the BESS DOD to find the optimal value of DOD that results in the cheapest BESS for dispatching the WSHPS electricity. Similarly, research into the ideal DOD for the SESS has been conducted. Unlike Li-ion batteries, supercapacitors can be charged and drained indefinitely. Therefore, the total number of charging-discharging cycles for the SESS is taken to be constant.HESS Cost MinimizationThe BESS and SESS use the LPF as their power reference. Minimum SESS capacity is proportional to the LPF time constant, while minimum BESS capacity is inversely related to the LPF time constant. The total cost of the HESS can be reduced by selecting an appropriate value for the filter time constant. The PSO strategy is used to determine the optimal LPF time constant once the suitable cost formula of the HESS as a function of the LPF time constant has been acquired via the curve fitting method. Because of its many benefits, including easy implementation, increased credibility in locating global optimums, the need for the adjustment of only a small number of parameters, and rapid convergence, the PSO method is used. Although genetic algorithms are also commonly used as an optimization approach in renewable energy systems, the PSO typically provides faster evaluation times and higher-quality solutions.Estimation BESS and SESS LifespanThe charging-discharging characteristics of the BESS over a period of time are utilized to evaluate its service life due to the fluctuating nature of the WSHPS output power. Because of calendar aging, the BESS's predicted lifetime decreases. Calendar aging and cycling are both taken into account by the SESS aging model.Estimating the Cost of HESSThe ESS cost is examined while taking into account the costs associated with both cycle and calendar aging. The capital cost, power conversion system cost, and operation and maintenance (O&M) cost of the ESS make up its total expense. Thus, it is possible to estimate the overall cost related to the BESS (CBat,overall) using equation (4): CBat,overall = CCap + Cconv + CO&M     (4)Summarizing the Key PointsThe article proposes a wind-solar hybrid power system that combines solar and wind turbine power dispatching systems.The system uses a battery and supercapacitor hybrid energy storage subsystem to minimize costs.The wind energy system consists of a wind turbine, permanent magnet synchronous generator, pitch angle control, drivetrain, and power converter.The photovoltaic energy system has a 1 MW PV array, a maximum power point tracking controller, and a unidirectional DC/DC boost converter.The article aims to optimize energy storage and power dispatching in wind-solar hybrid systems for cost-effective and reliable electricity supply.ReferenceRoy, Pranoy, Jiangbiao He, and Yuan Liao. “Cost Minimization of Battery-Supercapacitor Hybrid Energy Storage for Hourly Dispatching Wind-Solar Hybrid Power System.” IEEE Access 8 (2020): 210099–115. https://doi.org/10.1109/access.2020.3037149.

Overview: This article discusses the output capacitance losses and dynamic threshold voltage in Gallium nitride devices. The output capacitance losses are a significant percentage of the device's total loss. The dynamic threshold voltage is a very important factor in power applications. In the area of technological advancements, Gallium nitride (GaN) devices have emerged as a promising solution for various applications. However, despite their growing deployment, there remain persistent uncertainties surrounding their stability, reliability, and robustness. In both academia and industry, there is a growing focus on addressing the challenges related to the stability, reliability, and robustness of GaN devices. Gallium nitride high-electron mobility transistors (GaN HEMTs) have stability issues like dynamic on-resistance, dynamic threshold voltage, and output capacitance losses. All of these things are very important in power applications, especially at high frequencies. This article provides a detailed discussion on output capacitance losses and dynamic threshold voltageWhen using gallium nitride, how does output capacitance loss impact stability?GaN HEMTs are responsible for the output capacitance losses. When the off-state power device's equivalent output capacitance is charged and discharged, this loss occurs. In an ideal capacitor, this loss would be zero. Large-signal, dynamic double sweep in GaN HEMTs leads to power loss because of hysteresis in the relationship between the output charge and the drain-to-source bias. This loss problem has just been brought to light in GaN HEMTs; however, it was first noticed in Si superjunction devices. GaN HEMTs are experiencing significant output capacitance losses. In high-frequency soft-switching applications, this loss starts to become a significant percentage of the device's total loss from the perspective of the system. This loss is often significantly smaller than the other device losses in hard switching (HSW) or low-frequency applications. Unexpected increases in junction temperature can severely degrade system performance.Methods to Determine Output Capacitance LossThis loss has been quantified using a variety of approaches, including calorimetric (thermal) and electric (Sawyer-Tower, nonlinear resonance, and unclamped inductive switching), as shown in Fig. 1. There are benefits and drawbacks to each of these approaches. Fig. 1. Output Capacitance Loss Determining MethodThermal MethodCalorimetric MethodOne of these methods is the calorimetric method, which involves connecting the device under test (DUT) in parallel with an active switch, leaving the DUT unpowered while the active switch controls the drain-to-source bias, and figuring out the output capacitance loss from the change in junction temperature. This technique permits the measurement of the loss of the device under test in active soft-switched converters without regard to the operating frequency. However, system calibration in this approach may be time-consuming, and isolating device output capacitance loss from other losses may be difficult. At low power levels, the calorimetric measurement may also lose some of its precision.Electrical MethodElectrical technique implementation and related data processing are typically easier.Sawyer-Tower TechniqueTo generate the sinusoidal excitation, the Sawyer-Tower technique uses a network that includes the DUT, a reference capacitor, and a power amplifier. Since the DUT is always turned off, the input voltage and the capacitor voltage can be used to determine the DUT's large-signal charge-voltage waveforms; the output capacitance loss can then be extracted from the hysteresis of the waveforms.Nonlinear Resonance or Unclamped Inductive Switching TechniquesThe DUT can be switched on or off when using nonlinear resonance or unclamped inductive switching techniques.ChallangesWhile these electrical systems require a less complex setup, noise and variation in the waveforms and equipment used (such as narrow probe bandwidth, probe delays, and waveform distortion at high frequencies) may have an impact on their accuracy. Calorimetric and Sawyer-Tower methods only include the device in its off-state, so they can't be used to investigate how on-state current affects output capacitance loss. The output capacitance loss data from different approaches requires careful consideration of these factors. Finally, there is still a disagreement over where exactly the output capacitance loss in GaN HEMTs originates, despite widespread agreement that carrier trapping or de-trapping causes output capacitance hysteresis and is a major contributor. The relevant traps' physical origins, location, time constant, and energy level remain unknown. Output capacitance loss has been linked to both leakage current in the epitaxial structure and resonance on the Si substrate. There haven't been many reports on methods for minimizing output capacitance loss because its cause isn't fully understood. Redesigning the GaN HEMT architecture and epitaxial stack has been proven experimentally to decrease the output capacitance losses. Output capacitance loss has a major effect on the device selection for high- and very-high-frequency power converters from the perspective of the application. An established approach to characterization that takes into account both the on and off states of the device and faithfully depicts its steady-state switching in converters would greatly speed up this process.What causes threshold voltage in gallium nitride devices?The instability of the threshold voltage at high bias temperatures in Si and SiC MOSFETs has been a central topic of study for decades. GaN HEMTs of varying gate designs were also investigated. GaN metal-insulator-semiconductor (MIS) HEMTs were the primary focus of early research. In MIS-HEMTs, just like in Si and SiC MOSFETs, trapping at the insulator/GaN interface or in the bulk dielectric is what causes the unstable threshold voltage.Dynamic Threshold VoltageRecent years have seen a shift in research attention to commercial p-gate HEMTs as p-gate gradually becomes the prevailing E-mode GaN technology. Unlike the threshold voltage instability seen in MOSFETs and MIS-HEMTs, the dynamic threshold voltage in SP-HEMTs is an inherent characteristic of the floating p-GaN layer. Fig. 2 depicts the SP-HEMT gate stack, which comprises a back-to-back set of p-GaN Schottky junctions coupled with a p-Gan/AlGaN/GaN p-n junction. This "floating" p-GaN layer is the result of the fact that its charges cannot be successfully supplied or removed in fast switching since the bias state (forward or reverse) of these two junctions is opposite each other. Fig. 2. Typical trapping locations Source: IEEE Transactions on Power Electronics Positive dynamic threshold voltage shifts are common due to the charge storage process in p-GaN. The off-state blocking voltage and switching frequency both contribute to a larger threshold voltage shift. An Ohmic contact on p-GaN is a notable component of the hybrid-drain gate injection transistor since it facilitates efficient charge supply and extraction and, in turn, a reliable threshold voltage. Trapping may potentially play a role in the dynamic threshold voltage, in addition to the free-floating p-GaN. There are two trapping mechanisms that can affect a threshold voltage shift when operating under a forward gate-to-source bias. The first technique causes a negative threshold voltage shift by recoverable hole trapping. The second mechanism causes a positive threshold voltage shift because electrons are trapped and take time to recover. The dynamic threshold voltage shift may have a significant impact on switching processes in devices. Power loss in SP-HEMT grows as the reverse conduction voltage rises with a positive shift. The dynamic threshold voltage of SP-HEMTs will influence the majority of their turn-on losses. As a result, the gate's dependability is compromised, and a large gate-drive voltage is required to properly turn on the device. Therefore, the dynamic threshold voltage should be taken into account in circuit simulations to accurately portray real-world circuit properties. The switching transients in a phase-leg circuit have been recently analyzed using a SPICE model with a dynamic threshold voltage.What are the additional problems associated with composite devices?Given their multi-chip nature, composite devices may experience instability problems stemming from both the GaN HEMTs and the interconnections between the Si devices and the GaN HEMTs. For instance, there have been reports of instability in cascode GaN HEMTs. A diverging oscillation can arise due to a capacitance mismatch between the GaN and Si switches during high-current turn-off situations. Internal switching losses may also rise as a result of the bond wires' inductance between the switches and the Si avalanche. The current generation of commercial cascode GaN HEMTs does not have internal bond wires between the two chips. Instead, the Si chip is stacked directly on the source pad of the GaN HEMT, which reduces the connectivity-induced loss. False turn-on events, however, are possible, as are catastrophic failures brought on by SC oscillations. Cascode GaN HEMTs and direct-drive devices, on the other hand, rarely have gate instability because a Si MOSFET drives them largely or because extra protection circuits are copackaged with the GaN HEMT.Summarizing the Key PointsGallium nitride (GaN) devices are a promising solution for various applications. Despite their growing deployment, there remain uncertainties surrounding their stability, reliability, and robustness. GaN HEMTs have stability issues like dynamic on-resistance, dynamic threshold voltage, and output capacitance losses. Output capacitance losses are a significant percentage of the device's total loss. Dynamic threshold voltage is a very important factor in power applications, especially at high frequencies. Addressing the challenges related to the stability, reliability, and robustness of GaN devices is a growing focus in both academia and industry.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.

Power grids are becoming more decentralized as renewable energy sources take over as the dominant factor. These cutting-edge technological advancements, while providing opportunities for greater productivity.Why is a new reliability framework necessary?The new components of today's power systems bring up novel difficulties that necessitate a new reliability framework, which has recently been implemented. Assessing the reliability of modern power systems necessitates not only assessing various electro-magnetic and mechanical stability difficulties but also introducing new ideas related to local reliability.New Reliability ConceptA new methodology for reliability analysis in contemporary power systems should be established in order to address the issues brought on by new power system technology. It could keep the main ideas of adequacy and security while also taking into account the effects of grid modernization.Modern Power System Adequacy AssessmentThe cyber-physical structure of the current power system, which consists of three layers—power, communication, coupling, and decision—explains the adequate nature of this system. The proposed adequacy assessment framework is depicted in Fig. 1 in order to address all the drawbacks of reliability evaluation methodologies.Fig. 1. Framework for modern power system adequacy assessment. Source: IEEE Open Journal of Power Electronics As illustrated in Fig. 1, the suggested framework allows for the evaluation of the cyber-physical power system's suitability at three hierarchical levels: generation, generation-transmission, and distribution.GenerationFirst and foremost, sufficient generation system capacity is needed to meet system demand as a whole. As a result, the generating sufficiency in HL I can be assessed similarly to the sufficiency of the traditional power system, as illustrated in Fig. 2(a).Fig. 2. Conventional framework for adequacy assessment. Source: IEEE Open Journal of Power ElectronicsCyber-Physical Generation-Transmission SystemTo make sure that the cyber-physical generation-transmission system in HL II is good enough, the effects of the cyber-layers and the effects of distribution generation must be modeled. Large-scale generation units and distribution networks based on microgrids are shown in simplified form in Fig. 3(a). The microgrids are modeled as a specific node at a Point of Common Coupling (PCC), which is depicted in Fig. 3(b), in order to assess the adequacy of these systems. Fig. 3. Scalable framework for modern power system adequacy: a) main structure as a simplified grid; b) equivalent model of microgrids from distribution systems; c) local adequacy for each microgrid. Source: IEEE Open Journal of Power Electronics Depending on the topology and accompanying power management technique inside each microgrid, this special PCC node may be a load or a generation unit for each microgrid in a distribution network. For example, in the substation microgrid comprising medium-scale generators to provide its load, the equivalent load (which is equal to the generation minus the load) can be taken into account at the PCC in Fig. 3(b). Additionally, the equivalent generation can be assumed at the PCC in Fig. 3(b) if the substation microgrid's generation is greater than its load. The substation's internal generation unit availability, load power, and upstream switch reliability all have an impact on this equivalent generation unit's availability. The MV distribution networks can therefore be characterized for transmission system analysis as equivalent loads or generations. The cyber-physical availability model, as shown in Fig. 2(b), can therefore be used for modeling the reliability of the cyber-physical transmission system.Cyber-Physical Distribution SystemThe reliability of cyber-physical distribution networks can be modeled in HL III for each microgrid based on its structure in HL III-A and for the distribution network in HL III-B, as illustrated in Fig. 1. In distribution networks, there are four different types of microgrid structures: single-customer, partial feeder, full feeder, and substation microgrid. The single customer microgrid's adequacy can be modeled by simplifying its structure, as seen in Fig. 3(c). The distribution network outside of the single-customer microgrid is represented in this form as an equivalent generation unit. The local adequacy of the microgrid must be met depending on the application of the single-customer microgrid, such as household load, hospital load, etc. The partial or full feeder microgrid's adequacy can be evaluated similarly to the single-customer microgrid by treating the single-customer microgrids inside it as a specific equivalent node at PCC, which can be a load or generator. Additionally, by modeling the feeder microgrids as special nodes at PCC, the substation microgrid's adequacy is assessed. The distribution network adequacy assessment's primary focus is on the accessibility as well as the availability of energy sources in each sub-grid. This may necessitate restrictions across sub-grids, particularly for single customers who may wish to be islanded during grid outages in order to retain their adequate supply despite the upstream microgrid's declining adequacy. A distribution network consists of numerous substations, which are connected to the high-voltage grid and to one another by MVAC or MVDC transmission systems. Thus, by modeling each substation microgrid as a particular node at their PCC, be it a load or a generator, which is connected to the main grid, it is possible to assess the adequateness of the cyber-physical distribution systems. Due to the presence of DGs and DESS, distribution system reliability, unlike traditional power systems, necessitates local adequacy assessment. The suggested scalable reliability modeling for distribution networks' microgrids ensures each microgrid's adequate suitability.Modern Power System Security AssessmentIn addition to being adequate, modern power systems also need to be secure due to the various sources of uncertainty they include. Similar to conventional power systems, security can be characterized as a system's capacity to tolerate unforeseen events. As indicated in Fig. 4, the security of modern power systems can be examined in three domains: static, dynamic, and cyber. Fig. 4. Framework for security assessment in modern power systems. Source: IEEE Open Journal of Power ElectronicsStatic SecurityThe steady-state operation of the system following any unforeseen event is referred to as static security. The system frequency, bus voltages, and temperature limits of the equipment must therefore remain within a reasonable range. In contrast to traditional power systems, converters specifically for HV and MV transmission lines require appropriate analysis of their thermal limits due to their restricted overloading capacity. Therefore, corrective measures must be taken to maintain system security because any contingency could lead to link overload. Additionally, after any contingency that results in the islanding of the microgrids, the distribution networks must guarantee that the power quality standards are met in addition to the voltage limitations. This is because the power quality requirements for various applications cannot be the same. Therefore, after islanding the microgrids, active and passive filters must be properly relocated in distribution networks to fulfill static security.Dynamic SecurityIn addition, the power system needs to be dynamically secure in case of an emergency. Modern power systems heavily rely on fluctuating energy sources with low inertia; hence, dynamic security is crucial. It could cause problems with voltage and frequency stability in the power systems. Without the proper voltage regulators, intermittent output power or renewable resources may degrade the grid voltage, which may impact the stability of the voltage. Furthermore, the absence of inertia in more or full renewable energy supplies may have an impact on the stability of the grid's frequency. Intercommuting to nearby grids with HVDC systems and using energy storage systems are required to resolve the frequency stability difficulties in the grid. The overall system security can control the size and placement of renewable energy sources, as well as the connection points, capacity, and ancillary services of HVDC networks. Proper system design can guarantee the entire security of the power system. As a result, just like traditional power systems, power system security evaluation calls for an analysis of voltage, frequency, and angular stability. Additionally, due to the widespread use of power electronic converters, the EMM stability difficulties in modern power systems must be taken into account in security evaluation. Power systems and microgrids may experience serious stability problems as a result of EMM interactions. Due to the quick dynamics of converter control systems, the EMM stability assessment within contingency analysis may be a challenging and time-consuming operation. Therefore, adequate models and tools for EMM stability analysis for security evaluation in modern power systems should be established.Cyber SecurityModern power systems are vulnerable to cyber-security vulnerabilities in addition to static and dynamic security problems. Cyber problems may be connected to either the decision layer or the communication and coupling layer. The physical malfunction of monitoring and measurement devices, as well as the lack of data availability, can have an impact on the system's performance at the communication and coupling layers. Additionally, cyberattacks affecting sensors and shift measurements, as well as physical failure of decision equipment that results in false data being injected into communication links, can lead to poor decisions and malfunctions in power systems. The security of the power system must be ensured against physical failure, data loss, and cyberattacks. These issues could have a number of detrimental effects on the system, including angular and frequency stability due to poor decision-making and a change in the demand-generation balance, issues with islanding detection and grid separation, as well as effects from equipment overloading, all of which could jeopardize the security of the entire system. Therefore, in security evaluation and management, it is necessary to consider the cyber-security of modern power systems.Summarizing the Key PointsThe decentralization of power grids due to renewable energy sources requires a new approach to assessing their reliability. The cyber-physical structure of the current power system consists of three layers: power, communication and coupling, and decision. The main ideas of adequacy and security are taken into account in new reliability framework. The new framework can address all the drawbacks of reliability evaluation methodologies. The cyber-security of modern power systems is a crucial consideration in security evaluation and management.ReferencePeyghami, Saeed, Peter Palensky, and Frede Blaabjerg. “An Overview on the Reliability of Modern Power Electronic-Based Power Systems.” IEEE Open Journal of Power Electronics 1 (2020): 34–50 https://doi.org/10.1109/ojpel.2020.2973926.

Overview: This article presents a review of control and modulation methods for DC-DC power converters. The focus is on high-performance power converters, but the methods are applicable to any DC-DC power converter. Pulse-width modulation (PWM) and small-signal-based feedback controls forms the basis of many commercial controller executions for DC-DC converters. Alternatively, many large-signal approaches are available. This article aims to provide a review of control and modulation methods, as well as methods for controller tuning, for DC-DC switching power converters.Do conventional control methods maximize efficiency?New, higher-level controls are inspired by the development of fast wide-bandgap switches in addition to the ongoing progress in digital signal processing and sensors. Fast processors and digital signal processing make new computational techniques for power converter control possible. Traditional methods of control almost never maximize available performance. The focus here is on high-performance converters, a rapidly expanding industry.Role of Converter TopologyThe converter topology serves as a constraint in the control process. In theory, with the right constraints, a single control method can be applied to a wide variety of circuits. Power regulation for digital electronics is very often done by voltage regulation. Using LED lighting encourages the use of current-regulated loads. Most battery chargers have settings for regulating both the voltage and the current. DC sources and loads in microgrids, as well as digital loads, benefit from droop relationships. The methods presented are not limited to these converter types; rather, they can be used with any DC-DC converter. Hard-switched converters, state feedback control, and large-signal tuning are all highlighted.Control Objectives for DC-DC ConvertersTable 1 summarizes the four different types of goals that DC-DC converter controls should meet. Both static and dynamic conditions are part of the operational necessities. Control is not always related to other operational needs, such as electromagnetic interference (EMI), efficiency, and dependability. The need for fault management and protection are typically dealt with independently. Some large-signal controllers can directly manage many requirements in Table 1 that appear to be independent. The entire set of specifications shown in Table 1 is related to converter design.Table 1. Converter Objectives With Control Implications. Source: IEEE Open Journal of Power Electronics Inductor and capacitor selection affect the ripple bands and slew rate limits. Layout and parasitics both have an impact on EMI. However, it is theoretically possible to define a cost function J(x) that is connected to all of the operating variables and converter parameters, as shown in equation (1)  where ai are weights, x are independent variables, and fi(x) are functions of x and other parameters. The root-mean-square (RMS) current and flux (associated with losses), the output voltage error and ripple, the rise time of the load current, the peak voltage stresses of the device, the peak junction temperature, and the switching frequency variation are examples. To take into account various operating points, converter topologies, and component considerations, the multi-objective optimization of power converters is formulated as a geometric program, a type of convex optimization problem. To increase the power density of DC-DC converters, it is also possible to incorporate electromagnetic effects and thermal management into the electrical design. Similar terms could have been used to define a performance index, which is the opposite of a cost function. An optimization problem can be formed from a design or control problem, and the cost function must be minimized.Control Methods to Address Timing ProblemThe timing issue is simple to frame but difficult to solve in practice. With simplified requirements, it is easy to solve for simple converters. However, the difficulty of the issue increases with the inclusion of further specification details and uncertainty. It does inspire particular methods. The goal of trajectory-based controls is to reformulate the timing problem as one with state variables. Alternatively, fast response relied on dedicated circuits like clamps. A converter is even modified with additional switches and devices to achieve faster disturbance rejection.Challenges with Solving the Timing ProblemBecause there is no simple solution to the generic switch timing problem, designers are limited to feasible methods. This typically adds two additional restrictions to those listed in Table 1. There are limitations on the converter's operating regime. Setting a mandatory minimum switching frequency is a typical example. The foundation for control design and operation is a simplified model of the converter. Implementing a small-signal linearization of an averaged model is a classic example. The first restriction reduces the amount of timing flexibility and makes the issue a cycle-by-cycle duty ratio. The second results in model-limited control, which may prevent access to the converter's full dynamic capabilities.Factors Affecting the Control MethodsThe block diagram of a fundamental feedback and feedforward buck converter control system is shown in Fig. 1. To prevent ripple effects, the feedback sensing block is band limited. Additional signal conditioning and analog-to-digital converters (ADCs) are required for digital control. For accurate output regulation or tracking, output feedback is necessary. Control or current-regulated loads can both benefit from inductor current feedback. Either output feedback or state feedback can be used to control a converter. Using input voltage, load current, or other data, feedforward action can improve disturbance rejection, lower audio susceptibility, and lower output impedance. To produce the gate signal for the controllable switch, the controller drives a modulator. A limiter function is necessary for the modulator in a boost converter. Fig. 1. Feedback control of a buck converter. Source: IEEE Open Journal of Power ElectronicsSmall-Signal ControlThere are a wide variety of uses for small-signal controllers. Network analyzers and other testing tools support the useful connection to conventional frequency-domain design procedures. Small-signal controllers have distinct soft start and inrush management, protection management, and strategies to adapt to a broad load range due to the need to design for a specified operating point. Improvements in dynamic performance are the subject of a large body of research. The advantage of connecting to well-established frequency-domain design tools is a benefit of small-signal models and tuning. However, small-signal methods and models do not offer a systematic way to run dynamic response up to slew rate limits and do not take into account nonlinear factors like duty ratio saturation or current limits. Also, small-signal controls require independent blocks for large-signal startup and fault protection.Large-Signal ControlLarge-signal controllers, on the other hand, can facilitate changes between seemingly incompatible operating states. Conversions can make use of the slew rate capabilities of the converter. Both switching boundaries and operating points can be applied to the problem of starting up and handling faults. Large-signal controllers provide useful alternatives for applications requiring fast dynamic response or a broad range of load conditions. Geometric controls can be visualized as involving multi-segment boundaries for functions like startup and fault protection. To conclude, the robustness and sensitivity issues between small-signal and large-signal methods are actually fairly consistent. Knowing the parameters is helpful for both; feedforward is advantageous for both; the model performs best when it is accurate and complete; and adaptation to changing circumstances is helpful for both.Summarizing the Key PointsNew, higher-level controls for power converters are possible due to the development of fast wide bandgap switches, digital signal processing, and sensors. Converter topology serves as a constraint in the control process, but with the right constraints, a single control method can be applied to a wide variety of circuits. Pulse-width modulation and small-signal based feedback controls are commonly used for converters, but large-signal approaches are also available. Geometric controls based on piecewise-linear large-signal analysis can provide the quickest dynamic response for high-performance DC-DC converters. Low-cost digital controls make it possible to sample quickly and switch boundary controls, and high-performance DC-DC converters may benefit from the use of online adaptive geometric controls.ReferenceKapat, Santanu, and Philip T. Krein “A Tutorial and Review Discussion of Modulation, Control, and Tuning of High-Performance DC-DC Converters Based on Small-Signal and Large-Signal Approaches.” IEEE Open Journal of Power Electronics 1 (2020): 339–71. https://doi.org/10.1109/ojpel.2020.3018311.

Overview: The article discusses the rapid growth of renewable energy resources, particularly photovoltaic and wind turbines, as the most attractive power generation options due to strong government incentives and encouragement to use green energy. Over the past ten years, the use of renewable energy resources has grown rapidly throughout the world. Renewable energy sources, especially photovoltaic (PV) and wind turbines (WT), have emerged as the most attractive power generation options.Challenges in Renewable Energy Based Power SystemsThe installed wind turbine capacity increased from 540 GW to 591 GW between 2017 and 2018, while the installed solar photovoltaic capacity increased from 405 GW to 505 GW. The output of the photovoltaic and wind turbines exhibits unstable characteristics because it is heavily dependent on weather factors such as wind and cloud movement. The utility grid faces significant technical challenges with regard to power quality, generation dispatch control, and grid reliability as a result of the substantial penetration of these types of intermittent renewable energy sources. As a result, operators of renewable energy plants will face pressure to deliver consistent power, much like conventional fossil fuel power plants have done. Overgeneration and restrictions are the grid operators' growing concerns as more photovoltaic and wind turbines are connected to the grid. There are primarily two reasons for the curtailment of renewable energy, namely regional supply excess and regional transmission constraints. Although higher levels of curtailment have also been reported, the typical range of curtailment levels for wind generation is between 1% and 4%. When rigid traditional generators, like nuclear and coal plants, are unable to be used to generate lower power, negative pricing and the curtailment of renewable energy generation occur. The duck curve, which is depicted in Fig. 1, can be used to show the enormous difficulty of incorporating solar and wind energy as well as the likelihood of overgeneration and curtailment. Fig. 1. Duck curve illustration. Source: IEEE AccessThe Idea of Hybrid Power SystemsIt is generally accepted that any individual wind or solar source cannot sustainably power a load. It should also be noted that the hours of maximum output for wind and solar systems vary throughout the day and the year. The weather and climate patterns actually make solar and wind energy resources mutually beneficial. Thus, on a seasonal or daily basis, the energy produced by wind-photovoltaic resources keeps reversing. Since photovoltaic and wind turbines have benefits that complement one another in terms of power profiles, the hybrid utilization of the two should receive more attention. It is possible to develop hybridization techniques to deal with the intermittent nature of solar and wind power.Wind-Solar Hybrid Power SystemsThe wind-solar hybrid power system (WSHPS) combines photovoltaic and wind turbine subsystems to boost overall system efficiency, reduce energy storage capacity needs, and make the power grid more reliable. Wind-solar hybrid power systems are better than single photovoltaic or wind turbine systems in deficient utilities because they can compensate for unwanted intermittent variations with a single renewable energy source. In addition, the wind-solar hybrid power system can help the points of generation and consumption be adjacent to each other, which reduces infrastructure costs, particularly for rural electrification projects. As a result, wind-solar hybrid power system schemes at a single location are becoming a prominent trend in the worldwide transition to renewable energy. Voltage and frequency regulation, the mismatch between generated power and load demand, grid operation economics, and the scheduling of generation units are just some of the difficulties associated with the incorporation of large amounts of intermittent renewable energy into the utility. Therefore, grid operators must take extra measures to guarantee the reliability of the system. Because of the addition of solar and wind energy to the grids, fossil fuel generators, for example, need to be switched on and off or have their outputs adjusted more frequently to account for power fluctuations. In addition to raising maintenance costs, frequent cycling of fossil fuel generators also reduces efficiency. With high solar penetration, the cost of cycling ranges from $0.47/MWh to $1.28/MWh per fossil-fueled generator. Therefore, the aforementioned economic challenges necessitate a constant power dispatch commitment from the wind-solar hybrid power system framework at an acceptable interval.Energy Storage SystemsAdding the energy storage system (ESS) to the wind-solar hybrid power system framework will further mitigate the risks associated with renewable energy sources. In particular, the energy storage system makes it possible to provide supplementary services like voltage regulation, frequency regulation, harmonic reduction, transient stability, and load leveling. There are a variety of energy storage systems on the market, but two of the most popular are batteries and supercapacitors (SC). The characteristics of the battery and supercapacitors are compared in Table 1. There are many similarities between the supercapacitors and the conventional capacitors, with the main differences being the supercapacitors' smaller size and longer lifespan. Table 1: Battery and SC Performance Comparison Source : IEEE Access The battery energy storage system (BESS) has a low-power ramp rate, which indicates that the BESS charging-discharging rates are insufficient to meet peak or pulse load demand despite its high energy density property. The energy density is low, but the power ramp rate is high in the supercapacitor energy storage system (SESS). So, the supercapacitors can't keep up with the load for as long as it's needed. It's obvious that neither of these energy storage systems has both a high power density and a high energy density. Therefore, if only one kind of energy storage system is deployed to meet both the power and energy capacity specifications, a high installation cost may be needed to meet both the energy and power capacity needs.Hybrid Energy Storage SystemTherefore, a cost-effective energy storage system can be developed through the use of a hybrid energy storage system (HESS) consisting of a battery energy storage system and a supercapacitor energy storage system, with the supercapacitor facilitating the fast-changing power components passing through the battery, which increases the service life of the battery.Hybrid Energy Storage for Wind-Solar Hybrid Power SystemsThe main goal is to improve the way that renewable energy is used so that the wind-solar hybrid power system output power can be sent to the power grid every hour for a whole day, as desired. For this, the wind-solar hybrid power system architecture incorporates a hybrid energy storage system made up of lithium-ion batteries and supercapacitors, which can store the collected wind-solar hybrid power system energy and transform the intermittent energy into a reliable supply that can be dispatched when needed.Dispatching SchemeTo provide the wind-solar hybrid power system's output power to the utility grid, a dispatching scheme has been employed rather than the conventional peak shaving or smoothing approach. The wind-solar hybrid power system can be regulated like other conventional generators, such as thermal and hydropower plants, because of the utility's dispatching scheme. When combined with the dispatched scheme by which wind-solar hybrid power system output power is supplied to the grid, this flexibility extends to the utility grid in many ways, including the scheduling of generation units, the economics of grid operation, and the provision of grid ancillary services.Low Pass FilterA low pass filter (LPF) is used to split the energy produced by the hybrid energy storage system into two groups: the SC group receives power with a fast-dynamic response, while the battery group receives power with a slow-dynamic response. The battery's lifespan is increased by using this method because it helps the battery avoid rapid charging and discharging cycles and a large discharge current. In addition, the most cost-effective hybrid energy storage system for hourly dispatching of the wind-solar hybrid power system power scheme is sought by using curve fitting and Particle Swarm Optimization (PSO) techniques. The goal is to minimize the cost of the hybrid energy storage system while keeping the energy storage system's state-of-charge (SOC) within a certain range and meeting the power demand during each dispatching period.Summarizing the Key PointsRenewable energy resources, particularly solar and wind, have grown rapidly due to strong government incentives. The output of these energy sources exhibits unstable characteristics due to weather factors such as wind and cloud movement. Hybrid power systems that integrate wind and solar energy can maximize the potential of renewable energy. Technical challenges in photovoltaic and wind turbine power systems need to be addressed to overcome the unstable characteristics of renewable energy. The integration of energy storage systems can help mitigate the variability of renewable energy sources.ReferenceRoy, Pranoy, Jiangbiao He, and Yuan Liao. “Cost Minimization of Battery-Supercapacitor Hybrid Energy Storage for Hourly Dispatching Wind-Solar Hybrid Power System.” IEEE Access 8 (2020): 210099–115. https://doi.org/10.1109/access.2020.3037149.