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At least 379 records · Page 21

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DOE OSTI2025

Technical Assistance for Digital Assurance: DistribuTECH Workshop

Idaho National Laboratory, the Department of Energy’s Grid Deployment Office, and other national laboratories are collaborating to ensure the U.S. energy infrastructure is reliable, resilient, and secure. This involves strategically leveraging digital technologies to modernize the grid, enhance its resilience against all hazards and disruptions, and fortify national energy security across a diverse energy portfolio. In this workshop you will learn about Idaho National Lab’s Technical Assistance programs, where organizations will be matched with a national laboratory subject matter expert to focus on their key topical area. The technical assistance offered through this track is designed to be responsive to a rapidly changing regulatory landscape and cutting-edge technologies that enhance grid reliability and efficiency. Users will be guided through a tailored analysis and mitigation program to determine their current security posture and given assistance in evaluating supply chain and protection choices against potential consequences.

Digital assurance↗

DOE OSTI2020

First-principles Modeling and Design of Solid-State Interfaces for the Protection and Use of Lithium Metal Anodes

Li-ion batteries are one of the most advanced energy storage technologies in use today. Li-ion batteries are used in a multitude of applications ranging from consumer electronics, medical devices, sensors and grid storage. However, improving the capacity and energy density delivered by current Li-ion technology requires advanced materials research into novel chemical systems. In this project we have focused particularly in the use of solid-state electrolytes with lithium metal electrodes. Research into all solid-state batteries (ASSB) with Li metal electrodes has significantly expanded in recent years, however most studies reported experimental findings, which left substantial room for theoretical and modeling work as a tool to understand and determine design principles allowing reliable and safe use of ASSBs with Li metal. Among the remaining obstacles preventing reliable use of ASSBs with a Li metal electrode, the stability of the interface between the solid electrolyte and Li metal, and the propagation/dendrite formation of Li metal and resulting mechanical degradation of the electrolyte are key phenomenon that are yet to be fully understood. In the current project we have addressed these two coupled phenomena using first principles calculations and mesoscale continuum modeling. We have obtained chemical and electrochemical stability windows for several solid electrolyte materials. Additionally, from mathematical and numerical modeling of Li protrusion and dendrite initiation during plating and stripping we have determined design criteria in terms of chemical, electrochemical, and mechanical properties and operating conditions for which stable deposition can occur. We also considered the effects of mixed electronic-ionic conduction in solid electrolytes, which has more recently been suggested as another important mechanism involved in ASSB failure. Throughout our work we have successfully addressed important questions necessary for the use of ASSB’s. We have determined guiding principles for materials properties and operating conditions necessary to operate ASSB’s. And have proposed novel solid electrolyte materials with predicted chemical stability an ionic conductivity. Although this represents significant progress in our understanding, open questions remain in order to fully develop reliable and safely operate ASSBs with Li metal. Future work, building on this project will require further experimental, theoretical and simulation efforts to address remaining questions.

25 ENERGY STORAGE↗

DOE OSTI2023

Visualizing Fault Induced Traveling Waves In Medium Voltage Systems

Traveling waves are induced in power systems during most transient events in the grid. These waves travel close to the speed of light in overhead lines and 50% to 60% the speed of light in underground cables. Even though traveling wave-based protection schemes for transmission systems are available commercially, traveling waves in medium voltage distribution networks are still in research space. Compared to transmission system, medium voltage distribution systems contain more reflections and refractions. Thus, visualization is challenging and is critical in locating faults in distribution network. To address this visualization challenge, this paper presents an open-source tool to visualize the traveling waves using Bewley lattice approach. The developed visualization tool will be useful for the protection engineers to detect and triangulate fault locations in the medium voltage systems and isolate the faults.

Bewley Lattice↗

DOE OSTI2021

Cybersecurity for Grid Connected eXtreme Fast Charging (XFC) Station (CyberX) (Final Scientific/Technical Report)

This report summarizes the activities conducted under the DOE VTO funded project DE- EE0008451, where ABB Inc. (ABB), in collaboration with Idaho National Laboratory (INL), APS Global (APS), and XOS Trucks (XOS) pursued the development of a cyber-resilient extreme fast charging (XFC) management system. This project entitled Cybersecurity for Grid Connected eXtreme Fast Charging (XFC) Station (CyberX) focuses on a resilient architecture for smart charging EV Supply Equipment (EVSE) device control and Coordinated Anomaly Detection System (CADS) features that can be added at the charging site depot level to increase cybersecurity. The project was split into two budget periods focused first on developing the threat model and resilient control concepts and second on testing, improving, and validating those developed resilient control algorithms and features with a focus on key vulnerabilities identified during the threat assessment portion of the project. During the first budget period of the CyberX project, the ABB led team focused on activities to identify, model, and quantitatively prioritize high-impact attack scenarios with potential cyber-physical effects while also modeling and developing concepts for a resilient control system that could securely address integration of DERs and other resources with EV charging. Development of the security focused XFC management system (XMS) was accomplished first by offline simulation using a developed XFC station or depot with 480V input level and simulating measurement inputs to monitoring and control systems in concept development. A representative distribution grid model was developed supporting an EV charging site model with BESS and 6 general EV charging models. These EV charging models allowed multiple configurations of charging level, multiple connected protection and measurement devices, and simulation function to show general compromise of EV, BESS, and protection features based on parallel threat analysis. During the second budget period, the EV site and supporting systems model was developed in more detail and converted from offline model to real-time to real-time with EV charging hardware in the loop (HIL). The resilient control architecture developed as concept in the first part of the project was further tested and validated for integration of local energy resources and XFC charging station site equipment while maintaining cybersecure operating principles. The proposed resilient architecture for smart charging and cybersecurity features consists of two main concepts developed and tested within the project. The first concept is an XFC management system (XMS) consisting of a hardware gateway, software platform, and Supervisory Control and Data Acquisition (SCADA) or Distribution Management System integration components. The second concept is a Coordinated Anomaly Detection System (CADS) which forms a primarily software-related subsystem of the total CyberX solution focused on monitoring system measurements, estimation of measurement states, and predicting current at the utility point of interaction based on machine learning for anomaly detection.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS2010

Validation of a Three-Dimensional Ablation and Thermal Response Simulation Code

The 3dFIAT code simulates pyrolysis, ablation, and shape change of thermal protection materials and systems in three dimensions. The governing equations, which include energy conservation, a three-component decomposition model, and a surface energy balance, are solved with a moving grid system to simulate the shape change due to surface recession. This work is the first part of a code validation study for new capabilities that were added to 3dFIAT. These expanded capabilities include a multi-block moving grid system and an orthotropic thermal conductivity model. This paper focuses on conditions with minimal shape change in which the fluid/solid coupling is not necessary. Two groups of test cases of 3dFIAT analyses of Phenolic Impregnated Carbon Ablator in an arc-jet are presented. In the first group, axisymmetric iso-q shaped models are studied to check the accuracy of three-dimensional multi-block grid system. In the second group, similar models with various through-the-thickness conductivity directions are examined. In this group, the material thermal response is three-dimensional, because of the carbon fiber orientation. Predictions from 3dFIAT are presented and compared with arcjet test data. The 3dFIAT predictions agree very well with thermocouple data for both groups of test cases.

Chen, Yih-Kanq↗

DOE OSTI2022

Visualizing Fault Induced Traveling Waves in Medium Voltage Systems: Preprint

Traveling waves are induced in power systems during most transient events in the grid. These waves travel close to the speed of light in overhead lines and 50% to 60% the speed of light in underground cables. Even though traveling wave-based protection schemes for transmission systems are available commercially, traveling waves in medium voltage distribution networks are still in research space. Compared to transmission system, medium voltage distribution systems contain more reflections and refractions. Thus, visualization is challenging and is critical in locating faults in distribution network. To address this visualization challenge, this paper presents an open-source tool to visualize the traveling waves using Bewley lattice approach. The developed visualization tool will be useful for the protection and safety engineers to detect and triangulate fault locations in the medium voltage systems and isolate the faults.

Bewley Lattice↗

DOE OSTI2024

Advanced Sensor Deployment for Distribution System State Estimation and Fault Identification

Distribution systems are currently facing steep operational challenges as a result of the rapidly increasing integration of renewables and other distributed energy resources (DERs) at both the primary and secondary circuit levels. Distribution utilities and system operators have traditionally had some visibility of their primary circuits using low-frequency supervisory control and data acquisition systems, and they have had very poor if not zero visibility of the secondary circuits where the presence of DERs is constantly increasing. Therefore, this paper presents simulation studies to demonstrate the benefits of an advanced, high-fidelity sensor technology, called as the Meta-Alert System (MAS), developed by Electrical Grid Monitoring, Ltd. (EGM), on the distribution grid. First, a reliable model of the EGM sensors is developed, and then two use cases, distribution system state estimation (DSSE) and fault identification are simulated to evaluate the performance of the MAS technology. Simulation results on the Electric Power Research Institute J1 feeder demonstrate that the MAS can effectively participate in system-level DSSE programs and can detect and locate faults faster than traditional distribution protection schemes.

distribution system↗

DOE OSTI2024

Data-driven Modeling for Grid Edge IBRs: A Digital Twin Perspective of User-Defined Models

Recent events in Odessa have brought attention to the challenges associated with the interaction between Inverter- Based Resources (IBRs) and the transmission and distribution system. The NERC event diagnosis report has highlighted sev- eral issues, emphasizing the need for continuous performance monitoring of these IBRs by system operators. Key areas of concern include the mismatch of control and protection perfor- mance of IBRs between the original equipment manufacturer (OEM)-provided models and field measurements. The inability to replicate the realistic response can result in incorrect reliability and resilience studies. In this paper, we developed an approach on how to emulate the behavior of an IBR using measurement data obtained for system operators to utilize in real-time and long- term planning. Two experiments are conducted in the phasor domain and electromagnetic transients (EMT) domain to emulate the behavior for grid forming and grid following inverters under various operating conditions and the effectiveness of the proposed model is demonstrated in terms of accuracy and ease of utilizing user-defined models (UDMs)

Mahapatra, Kaveri [BATTELLE (PACIFIC NW LAB)]↗

DOE OSTI2025

Environmental DNA as a tool for hydropower impact assessments: current status, special considerations, and future integration

Globally there is an urgent need to find sustainable solutions to balance energy production with the protection of vulnerable species and conservation of biodiversity. This is particularly critical for freshwater ecosystems, habitats, and species that may be impacted by hydropower development and operations needed to meet energy grid demands. Reliable and accurate environmental impact assessments (EIAs) that identify the biological, physical, or social impacts of hydropower are key to ensure biodiversity, ecosystem, and societal sustainability. The analysis of environmental DNA (eDNA) has the potential to transform hydropower EIAs, management and mitigation planning, and decision-making procedures. Further, the incorporation of eDNA surveys into EIAs during both hydropower planning and continued operations may streamline regulatory processes by improving our understanding of potentially impacted biota and habitats and evaluating environmental impacts mitigation. Here, we: (i) highlight current understanding and use of eDNA in freshwater environments; (ii) examine critical considerations for eDNA integration into hydropower EIAs and biological monitoring; (iii) identify knowledge gaps in eDNA analysis and applications unique to hydropower-regulated systems; and (iv) discuss future opportunities to bolster the incorporation of eDNA into hydropower research including regulatory acceptance and public engagement. While we acknowledge that there are several factors that may complicate the broad adoption of eDNA as a tool for assessing the impacts of hydropower, we anticipate that growing confidence in eDNA through hydropower-specific protocols, calibrations, and validations will overcome these inherent uncertainties.

aquatic biodiversity↗

DOE OSTI2021

A Transformational Natural Gas Fueled Dynamic SOFC for Data Center in-Rack Power (Final Technical Report)

Data centers consume 250TWh or 1% of global electricity use in 2019. This electricity demand is expected to grow exponentially in the future. The current data center power supply relies on electric grids with diesel engines and batteries as backup; the latter is critically important because a sudden loss of power could erase crucial data, causing catastrophic economic loss. However, such a double protection architecture is costly, inefficient and polluting. In search for better in-rack power system, fuel-cell based generators have recently emerged as a promising alternative to diesel engines and batteries because of its potential to be more cost-effective, more efficient and less polluting. Among many types of fuel cells, solid oxide fuel cells stand out to be a front runner because of their ability to directly operate on natural gas that has an existing infrastructure. However, there are several critical challenges facing SOFC generators to meet the rigorous power requirements of data centers: high cost, poor reliability and more importantly, slow response to the datacenters’ dynamic load change. The unexpected overload conditions in data centers can cause fuel starvation; the latter can initiate microcracks in the anode structure, thus jeopardizing the lifetime of the SOFC generator. The current efforts to increase transient power capabilities of traditional SOFCs and therefore protect SOFC stacks from internal damage during transient loading, rely primarily upon mathematical algorithms. One of the challenges to these control methodologies is the physical limitations of the fuel delivery system such as slow response of mass flow controllers to fast power demand.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2026

Protection System Validation Using Post-Event Anomaly Classification with Machine Learning

Power system protection devices have transitioned over the past few decades from mechanical to analog devices, then to solid state and finally digital. Relays and their associated critical network of equipment have significantly increased in complexity. Even internally, relays have gained significant intricacy, with relatively simple overcurrent or differential functions now being assisted by a myriad of other functions. This is necessary as the grid becomes more complex, but it brings increased difficulty in monitoring and upkeep. Misoperation caused by improper relay settings or malicious actions is a constant challenge faced by all utilities. These improper settings can be difficult to identify and may require exhaustive post-mortem analysis, typically after a major outage event has already occurred. A mechanism is needed for monitoring the behavior of protection systems to validate that they act and perform as expected. This work presents a concept for a machine learning (ML) system capable of validating the performance of protection systems by classifying anomalous events and characterizing protection system responses based solely on available current and voltage measurements. As a first step in its development, an experimental dataset is generated, and a random forest model is implemented with high accuracy in distinguishing four power system scenarios.

24 - POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Protection System Validation with Machine Learning Anomaly Classification

A poster for the Early Career Poster Session. Power system protection devices have transitioned over the past few decades from mechanical to analog devices, then to solid state and finally digital. Relays and their associated critical network of equipment have significantly increased in complexity. Even internally, relays have gained significant intricacy, with relatively simple overcurrent or differential functions now being assisted by a myriad of other functions. This is necessary as the grid becomes more complex, but it brings increased difficulty in monitoring and upkeep. Misoperation caused by improper relay settings or malicious actions is a constant challenge faced by all utilities. These improper settings can be difficult to identify and may require exhaustive post-mortem analysis, typically after a major outage event has already occurred. A mechanism is needed for monitoring the behavior of protection systems to validate that they act and perform as expected. This work presents a concept for a machine learning (ML) system capable of validating the performance of protection systems by classifying anomalous events and characterizing protection system responses based solely on available current and voltage measurements. As a first step in its development, an experimental dataset is generated, and a random forest model is implemented with high accuracy in distinguishing four power system scenarios.

24 - POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Total Effective Dose from Radiologic Emissions from INL Facilities for Calculation of Population Dose for the INL 2024 Annual Site Environmental Report

Total effective radiation dose from airborne releases was calculated using air dispersion modeling performed by the National Oceanic and Atmospheric Administration (NOAA) Idaho Falls Office using their HYSPLIT computer model (Stein et al. 2015; Draxler et al. 2013), and the Dose Multi-Media (DOSEMM) dose assessment model (Rood 2019) . The objective of these calculations was to provide a grid of total effective dose across a model domain that encompasses a 50-mile (80-km) radius from any Idaho National Laboratory (INL) Site source. In addition to INL Site sources, releases from the Radiological and Environmental Sciences Laboratory (RESL) (IF-683) and IF-603 located at the INL Research Center (IRC) within the Idaho Falls city limits were also included. Due to tracking limitations, radionuclides released from IF-611 and IF-603 are modeled as released from IF-603. The dose results will be combined with GIS software to compute a total population dose for the calendar year (CY) 2024 and will be reported in the INL Annual Site Environmental Report (ASER). This report does not cover the population dose calculation and only documents generation of the gridded dose file.

42 - ENGINEERING↗

DOE OSTI2026

Post-Event Fault Identification with Machine Learning for Protection System Validation

Power system protection devices have transitioned over the past few decades from mechanical to analog devices, then to solid state and finally digital. Relays and their associated critical network of equipment have significantly increased in complexity. Even internally, relays have gained significant intricacy, with relatively simple overcurrent or differential functions now being assisted by a myriad of other functions. This is necessary as the grid becomes more complex, but it brings increased difficulty in monitoring and upkeep. Misoperation caused by accidental improper relay settings or deliberate malicious actions is a constant challenge faced by all utilities. These improper settings can be difficult to identify and may require exhaustive post-mortem analysis, typically after a major outage event has already occurred. A mechanism is needed for monitoring the behavior of protection systems to validate that their performance falls within expectations. Relays that fail to isolate a fault or trip when there is no system disturbance can be flagged for settings review in situations where this behavior may not have been noticed due to manual restoration or backup protection operations. This work presents a concept for a machine learning (ML) system capable of validating the performance of protection systems by identifying fault events and characterizing protection system responses based solely on available current and voltage measurements. As a first step in its development, an experimental dataset is generated, and a random forest model is implemented with high accuracy in distinguishing four power system scenarios.

24 - POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Online and Offline Identification of False Data Injection Attacks in Battery Sensors Using a Single Particle Model

The cells in battery energy storage systems are monitored, protected, and controlled by battery management systems whose sensors are susceptible to cyberattacks. False data injection attacks (FDIAs) targeting batteries’ voltage sensors affect cell protection functions and the estimation of critical battery states like the state of charge (SoC). Inaccurate SoC estimation could result in battery overcharging and over discharging, which can have disastrous consequences on grid operations. This paper proposes a three-pronged online and offline method to detect, identify, and classify FDIAs corrupting the voltage sensors of a battery stack. To accurately model the dynamics of the series-connected cells a single particle model is used and to estimate the SoC, the unscented Kalman filter is employed. FDIA detection, identification, and classification was accomplished using a tuned cumulative sum (CUSUM) algorithm, which was compared with a baseline method, the chi-squared error detector. Online simulations and offline batch simulations were performed to determine the effectiveness of the proposed approach. Throughout the batch simulations, the CUSUM algorithm detected attacks, with no false positives, in 99.83% of cases, identified the corrupted sensor in 97% of cases, and determined if the attack was positively or negatively biased in 97% of cases.

25 ENERGY STORAGE↗

DOE OSTI2022

Strengthening the Security of Operational Technology: Understanding Contemporary Bill of Materials

The evolution of cyber-physical infrastructure has made its security more challenging. The last few years have witnessed a convergence of hardware and software segments in various domains, including operational technology (OT) which is responsible for carrying out critical tasks such as monitoring and controlling power grids, nuclear plants, transportation, and emergency services. Both hardware and software encapsulate numerous open source and proprietary subcomponents, making it crucial for end-users to understand the composition of the products they are using. For example, wind turbines incorporate thousands of lines of code (software) used for the turbine's design, planning, operation, and analytics in addition to the numerous hardware subcomponents that construct it. Due to the highly complex nature of software and hardware, knowledge of the components and subcomponents is required to mitigate cyber vulnerabilities and defend against cyberattacks. There has also been a transformation from a traditional linear supply chain into a global, dynamic, diverse, and interconnected system. The digitization of the supply chain makes it easier to find and exploit vulnerabilities. Critical infrastructures (e.g., power grids, oil, natural gas, water, and wastewater) rely on OT to function, and if the OT is compromised, equipment damage and potential interruption of services could result. A significant security measure to protect OT systems from disruption is to develop a supply chain bill of materials (BoM) corresponding to the software and hardware used in OT, along with attestations amongst vendors and asset owners. A supply chain BoM is a proactive way to understand the inherent vulnerabilities in the system and mitigate them in advance of being exploited. BoMs bolster the trust placed in the digital infrastructure and enhance software supply chain security by sustaining the management of component obsolescence and compliance, along with the seclusion of unsafe segments of a specific product. Adopting BoM tools is becoming increasingly important across various government sectors, as evidenced by the recent U.S. executive order on cybersecurity (NIST 2021). This paper aims to classify BoMs based on structure, functionality, component type, and architecture. The work also discusses case studies to further highlight the benefits of BoMs. In addition, it identifies missing pieces in existing BoM implementations so that future research may identify bounds on where it could expect to make improvements and directly enable researchers to identify promising areas for exploration. Further, the authors provide valuable recommendations to tool developers, researchers, and standardizing organizations (policymakers), additionally benefitting critical infrastructure owners and government executives. This aids in paving a path for future work, thereby, providing suggestions to determine a tool for consumers that best suit their needs.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2014

Comparison Of Downscaled CMIP5 Precipitation Datasets For Projecting Changes In Extreme Precipitation In The San Francisco Bay Area.

Water resource managers planning for the adaptation to future events of extreme precipitation now have access to high resolution downscaled daily projections derived from statistical bias correction and constructed analogs. We also show that along the Pacific Coast the Northern Oscillation Index (NOI) is a reliable predictor of storm likelihood, and therefore a predictor of seasonal precipitation totals and likelihood of extremely intense precipitation. Such time series can be used to project intensity duration curves into the future or input into stormwater models. However, few climate projection studies have explored the impact of the type of downscaling method used on the range and uncertainty of predictions for local flood protection studies. Here we present a study of the future climate flood risk at NASA Ames Research Center, located in South Bay Area, by comparing the range of predictions in extreme precipitation events calculated from three sets of time series downscaled from CMIP5 data: 1) the Bias Correction Constructed Analogs method dataset downscaled to a 1/8 degree grid (12km); 2) the Bias Correction Spatial Disaggregation method downscaled to a 1km grid; 3) a statistical model of extreme daily precipitation events and projected NOI from CMIP5 models. In addition, predicted years of extreme precipitation are used to estimate the risk of overtopping of the retention pond located on the site through simulations of the EPA SWMM hydrologic model. Preliminary results indicate that the intensity of extreme precipitation events is expected to increase and flood the NASA Ames retention pond. The results from these estimations will assist flood protection managers in planning for infrastructure adaptations.

Storm↗

DOE OSTI2024

Control, Fault Management, and Grid Support Functionality of an MV AC-DC Solid State Transformer based EV Extreme Fast Charging Station

Electric vehicles (EVs) have become increasingly popular in recent times while revolutionizing the consumer and commercial transportation market. The development of charging infrastructure has become one of the priorities for increasing the adoption of EVs. Extreme fast charging (XFC) technology can reduce the so-called ’range anxiety’ of consumers as they significantly reduce the charging time. With the advent of wide band-gap (WBG) power devices and improvement in power electronic converters, medium voltage (MV) solid state transformer (SST) based XFC system has the potential to replace the traditional XFC stations because of the lower footprint, ease of installation, enhanced control feature, and better system efficiency. The control system design is one of the critical aspects of the SST development process. Careful consideration and detailed analysis are required to find out suitable control method for the SST based on its topology among different centralized and decentralized control architectures. Also, the control parameters selection and potential improvement to the transient response of the controller ought to be investigated. Another major concern of the SST is different types of internal fault which reduces the overall reliability of the XFC system. As a result, designing a robust protection system is essential. Among different fault modes, open circuit switch faults have received significant attention as an active research area because of their likelihood and severe effects on converters. Therefore, the power stages used in the XFC system require functional and accurate open circuit switch fault management methods. An equally significant aspect of this SST based XFC is its compatibility in a microgrid where there is no synchronous generator present. When the grid is not available, the XFC SSTs can provide grid forming capability and continue supplying the critical loads in islanded mode. The transition between grid connected and islanded mode, especially the grid resynchronization process has to be carefully performed for the safety of the microgrid components. The challenges posed by the aforementioned issues have inspired the work done in this dissertation. Here, a 13.2 kV, 1 MVA, AC/DC SST for the XFC system is examined and a comparative analysis is conducted to select the control architecture based on feasibility of implementation and performance. A detailed control parameter design process is demonstrated considering the sensor dynamics and delay. The selected decentralized control method is augmented by introducing a novel sensor-less load current feedforward method to provide better voltage regulation at the DC bus during a change of load. Next, in the fault management section, a hierarchical failure mode effect analysis (FMEA) is proposed to enable a systematic design of the internal fault protection of the XFC SST as there are limited examples in the literature regarding the analysis of the safety and design of the protection of a power electronic converter system. Novel open circuit switch fault management methods for the converters in the system are presented. Finally, XFC SST based MV microgrid operations in grid connected mode and islanded mode are explored. A secondary control method for grid resynchronization is presented and a design process of control parameters is shown to ensure the stability of the secondary voltage and frequency regulation.

30 DIRECT ENERGY CONVERSION↗