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At least 253 records · Page 14

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DOE OSTI2026

A High Power Density and High Efficiency Traction Drive Based on a Segmented Inverter and an Axial and Radial Flux Hybrid Machine

Eaton Corporation and Oak Ridge National Laboratory (ORNL) collaborated to develop a high performance traction drive system aimed at increasing power density and overall efficiency. The system integrates Eaton’s hybrid flux motor—combining radial and axial flux paths to convert end-winding and structural elements into torque-producing components—with ORNL’s high-power-density segmented inverter. This motor design achieves higher torque density within the same footprint as traditional radial flux machines by effectively utilizing end turns. ORNL’s segmented inverter reduces the DC bus capacitor requirement by over 50% compared to standard voltage source inverters (VSIs), achieving power densities exceeding 100 kVA/L. The dual-module architecture also enables six-phase operation, aligning with Eaton’s motor design. Together, the motor and inverter offer a compact, efficient traction drive system with the potential for significant reductions in system weight and improvements in powertrain efficiency.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Predictive Coordinated and Cooperative Voltage Control for Systems With High Penetration of PV

In this paper, we propose a predictive coordinated and cooperative voltage control method in a power distribution system with high penetration of photovoltaic (PV) units. First, an integrated coordinated voltage control of voltage regulators (VRs) tap positions and cooperative distributed control of the reactive power output from PV inverters are used to maintain system voltages within an appropriate bandwidth. Next, solar power forecasting is applied to predict voltage changes, which are used to set the VR tap positions and capacitor switch status to prevent large voltage fluctuations. The fine tuning of voltage adjustment is then achieved by cooperative control of PV inverters to maintain a uniform voltage profile across the system. The proposed method is tested on a modified IEEE 123-node test feeder with high penetration of PVs using real measurement data and compared with the base case. Simulation results demonstrate the effectiveness of the integrated voltage control, as well as the enhancement from the predictive control through solar power forecasting-enabled voltage change estimates. Comparison to previous work in the literature shows significant improvement in terms of voltage deviation and reduction in excessive tap changes.

14 SOLAR ENERGY↗

DOE OSTI2020

Control of SiC Based Integrated DC-DC Powertrain Charger for Electric Vehicles

In this paper, an automated controller for an integrated, reconfigurable dc-dc converter for plugin electric vehicles (EVs) is presented. The integrated converter combines both an interleaved boost converter and dual active bridge (DAB) converter, sharing magnetics and switching devices. The converter switches between operating modes using the existing battery management system (BMS) contactors. The integrated converter operates in isolated DAB mode during battery charging operation. During traction operation, the integrated converter is operated in interleaved boost mode for heavy load and DAB mode during light load, high voltage operation. An online transition algorithm is used to transition from interleaved boost mode to DAB mode with minimal bus distortion. The automated controller selects the operating mode and inverter bus voltage at different torque-speed conditions to get maximum overall traction drive efficiency. An experimental prototype of the integrated converter demonstrates the automated controller functionality.

Anwar, Saeed↗

DOE OSTI2025

LLM-Based Adaptive Distribution Voltage Regulation Under Frequent Topology Changes: An In-Context MPC Framework

This paper proposes a large language model (LLM) based adaptive inverter control for distribution voltage regulation under frequent topology changes. We leverage the ability of the LLM to perform in-context learning and create a topology-adaptive surrogate model for power flow calculation. The surrogate model is then integrated with a long short-term memory-based load forecaster and a model predictive control (MPC) scheme to achieve the optimal inverter control that adapts to frequent topology changes. Unlike many existing works that assume fixed-topology grids or require the knowledge of all possible topologies when training a model, the proposed in-context MPC method tackles the distribution voltage control problem under various topologies and adapts to unknown topologies with limited data requirement for fine-tuning. The effectiveness of our method is demonstrated on a modified IEEE 123-bus test system.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Solar-battery integrated DC system

A DC bus is connected to a PV array, a battery or both, to power a grid connected inverter load on the bus. A system and method is described for controlling current flow on a DC bus regardless of the type of inverter. A converter receives a DC battery voltage. A switching module controls current flow through the converter. A current generator generates a reference current in response to a reference voltage. A comparator connected at the output of the current generator compares the reference current with a battery current and outputs a signal to a controller. The controller generates a reference value. A PWM generator modulates the switch module to control current flow and energy flow between the DC bus and the battery. The current from the DC bus to the inverter is controlled to allow proper grid-connected operation regardless of inverter type.

Atcitty, Stanley↗

DOE OSTI2022

Performance Evaluation of Distributed Energy Resource Management Systems Using Software- and Hardware-in-the-Loop

Distributed energy resource management systems (DERMS) are an important emerging technology for coordinating distributed energy resources (DERs) integrated in power distribution networks. These solutions implement a software platform to monitor, control, and coordinate DERs in real time to provide aggregate grid services to the electric utility or distribution system operator. To move DERMS technology forward, research and performance validation in a laboratory environment are needed to ensure proper function of DERMS prior to field deployment. This presentation presents a generic hardware-in-the-loop (HIL) platform to evaluate performance of DERMS using software-in-the-loop (SIL) and power HIL. The platform provides realistic testing in a laboratory environment, including accurate modeling of full-scale distribution systems, DERMS software controllers, and at-power distributed energy resources (DERs), such as solar and battery inverters. Two case studies are presented to demonstrate how the platform can be used independently as an integrated development and validation approach for different projects. Laboratory testing results are included to showcase the voltage regulation performance of each study and the benefits of implementing such a platform for DERMS evaluation.

DERMS↗

DOE OSTI2022

Enabling Innovation in Wind Turbine Design Using Artificial Intelligence

The Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements (INTEGRATE) project is developing a new inverse-design capability for wind turbine rotors using invertible neural networks. This artificial intelligence (AI)-based technology can capture complex nonlinear aerodynamic effects 100 times faster than alternative design approaches.

aerodynamics↗

DOE OSTI2022

Enabling Innovation in Wind Turbine Design Using Artificial Intelligence

aerodynamics↗

DOE OSTI2024

Simple and Robust Carrier-Based PWM Technique for Single-Stage Three-phase Rectifier Indirect Matrix Converter

Indirect matrix converters (IMC) can offer high efficiency, high power density, and bidirectional power flow capability with a single-stage three-to single-phase AC-AC power conversion integrating a conventional three-phase active rectifier with a primary side full-bridge inverter of a dual active bridge converter. However, the IMCs generally require space vector modulation techniques, leading to complex sectors and dwell times calculations. A practical implementation of the modulation methods with a commonly used digital signal processor becomes even more challenging when considering power factor correction, harmonics reduction, and deadtime between sector transitions. This paper proposes a simple and robust carrier-based pulse width modulation (PWM) technique determined through a voltage-source inverter (VSI) to current-source inverter (CSI) to IMC conversion. This translation enables the adoption of all conventional VSI control methods with active and reactive power control in the IMCs through simple carrier and reference waveform comparison. The VSI vectors are first one-on-one matched with CSI vectors and translated to IMC modulation signals without calculating sectors and dwell times. Nine different carrier waveforms are proposed, and the converter performance is analyzed. Grid current THD and efficiency are estimated for each modulation method. The symmetric A and B methods had the lowest THD of the grid current, 2.80%, and the efficiency remained at approximately 98.3%, making it the most suitable carrier-based method proposed in this paper. The space vector PWM (SVPWM) modulation method was implemented to the DSP and generated the expected twelve gate signals.

Benson, Mikayla↗

DOE OSTI2025

Electromagnetic Transient Simulation of Photovoltaic Inverter Using Implicit-Explicit Solver

This paper introduces the implementation of electromagnetic transient (EMT) simulations of a photovoltaic (PV) inverter module using the Implicit-Explicit (ImEx) solver in the Suite of Nonlinear and Differential/Algebraic Equation Solvers (SUNDIALS). This study demonstrates the effectiveness of the ImEx solver in overcoming the challenges inherent in simulating the complex dynamics of PV inverter modules. Furthermore, using SUNDIALS’ ImEx solver module ARKODE for EMT simulation automates key aspects of the process, such as numerical integration, providing substantial benefits including enhanced consistency, faster implementation, reduced human error, and the capability to handle the complexities of advanced numerical integration. By conducting comparative simulations with an implicit method used in commercial software, the research showcases the ImEx solver’s capability in achieving high accuracy and reliability. Results indicate that leveraging the ImEx approach significantly enhances modeling fidelity and reduces simulation setup times, offering a promising tool for the EMT analysis of PV inverter systems in power electronics-dominated power grids.

Choi, Jongchan [ORNL] (ORCID:000000025952455X)↗

DOE OSTI2021

Stabilizing the Power System in 2035 and Beyond: Evolving from Grid-Following to Grid-Forming Distributed Inverter Controllers (Final Technical Report)

Under existing grid operations, large synchronous generators provide sufficient rotational inertia to form a rigid backbone for the bulk power system. With photovoltaics (PV) forecasted to provide more than 600 GW of generation by 2050 under the U.S. Department of Energy’s SunShot Initiative objectives, however, it is clear that power electronic inverters will play a dominant role in future systems, and low-inertia stability must be ensured to maintain system reliability. Today, the risks to system stability can be observed on geographically small islands, such as Hawaii, which contain a relatively large amount of installed PV. These risks stem from a fundamental shortcoming of contemporary control strategies—existing inverter controllers cannot guarantee grid stability. Given that future power systems driven by sustainable resources will be characterized by low inertia, locations such as Hawaii provide a glimpse into the obstacles facing future power systems. Considering these challenges, the aim of this project was to develop and demonstrate distributed inverter controllers that enable the reliable control of low-inertia power systems with hundreds of gigawatts of integrated PV.

14 SOLAR ENERGY↗

DOE OSTI2023

Integrated Large-Scale Data Management Platform for Photovoltaic Power Conversion Equipment (PCE) Reliability Data

To meet the demand for accuracy and real-time capability of PV system degradation evaluation, massive volume data is needed to run high-fidelity and high-efficiency simulations and perform advanced data analysis. However, PV farm operators have a series of difficulties with PV inverter data, such as data collection from multiple channels, massive data storage, data management and massive data analysis. To address these challenges, we developed an integrated data management platform capable of data acquisition, processing, storage, query, and performing big data analysis utilizing AI algorithms. The platform can also achieve data correctness verification and provide an effective distributed data management solution to retrieve massive data and establish a connection to distributed computational frameworks.

data management platform↗

DOE OSTI2025

RE-INTEGRATE EMT Simulation Software: Graph Convolutional Network for Sparse Matrix Pattern Detection

The increasing complexity of power networks, driven by proliferation of inverters, presents analytical challenges that simplified models often fail to capture, necessitating Electromagnetic Transient (EMT) simulations. EMT models are represented as discretized differential-algebraic equations (DAEs), forming a linear system Ax = b that is computationally intensive to solve. Due to inherent sparsity of adjacency matrix A, distinct patterns emerge that, when accurately identified, enable efficient solver selection to minimize computation time. However, identifying ideal pattern is complicated by numerous reordering algorithms and limited structural insights. To address this, we introduce a Graph Convolutional Network (GCN) model for classifying sparse matrix patterns common in power system analysis. The model, achieving 96% test accuracy, is validated using PV plant models of 125 MW capacities connected to New England 39-bus transmission system (TS), and further scaled to a 4,992-bus network with 384 PV plants, yielding 191, 616 × 191, 616 sized A matrix. For all cases, the GCN model accurately identifies the matrix’s intrinsic sparse pattern, demonstrating its potential to enhance solver performance in EMT analysis.

Hossain, Md Rifat [Florida International Universit↗

DOE OSTI2021

An Analysis Framework for Distribution Network DER Integration Analysis in India: Distributed Solar in Tamil Nadu

This report is part of a two-part series that represents a year-long collaboration with the Tamil Nadu Generation and Distribution Corporation Limited (TANGEDCO) on power sector planning. The first report in this series, the Pathways for Tamil Nadu’s Electric Power Sector 2017-2030 report outlines NREL’s work with TANGEDCO's electricity sector planning department to develop a model of the State’s power system and evaluate multiple scenarios of system growth given resource constraints, costs of technologies, and power sector policies. This second report in this series focuses on the rapidly transforming distribution network in the State. The report outlines a framework developed by NREL with TANGEDCO's distribution utility to quickly and accurately analyze the impacts of integrating renewable energy, specifically rooftop solar PV onto Tamil Nadu's distribution system. Together these studies help to prepare Tamil Nadu for a rapidly transforming power system.

14 SOLAR ENERGY↗

DOE OSTI2023

Integrated Large-Scale Data Management Platform for Photovoltaic Power Conversion Equipment (PCE) Reliability Data: Preprint

data management↗

DOE OSTI2024

Interleaved Cuk Converter Wave Energy System With Advanced Control and Grid Support Functions for Seamless Integration: Preprint

This paper presents an innovative wave energy conversion system employing an interleaved Cuk converter and advanced nonlinear control for seamless integration with the grid. The proposed system utilizes the interleaved Cuk converter for efficient power extraction on the DC side, while an inverter facilitates power transfer to the grid or load on the AC side. A sophisticated nonlinear control architecture based on Lyapunov energy functions ensures stable and optimal operation under varying wave conditions. Case study results demonstrate the effectiveness of the proposed system, highlighting its ability to efficiently harness wave energy and seamlessly integrate with the grid, thus paving the way for sustainable and reliable renewable energy generation. The overall system is verified via computer simulations based on MATLAB/Simulink and various case study results are presented.

HYDRO ENERGY,TIDAL AND WAVE POWER↗

DOE OSTI2025

Advancing Dynamic Modeling of Grid-Connected PV Inverter Using Bi-LSTM-Based AI Model

Power electronic converters (PECs) are widely used in modern power systems to facilitate the interconnection between various AC or DC sources and loads. Because of the extensive integration, the power system has grown into a more dynamic system in which the dynamics of the PECs must be adequately modeled. The paper presents a new bidirectional long short-term memory (Bi-LSTM) method for evaluating grid-connected inverter-based resources (IBR) dynamics. The method is tested using real hardware data from a grid -connected commercial inverter in laboratory experiments. Results show the Bi-LSTM model accurately reproduces the detailed IBR model's dynamics, even when the internal structure is unknown and parameters are unknown, preventing the disclosure of the manufacturer's confidential data.

Subedi, Sunil [ORNL] (ORCID:000000034069090X)↗

DOE OSTI2022

Invertible Neural Networks for Airfoil Design

We report the airfoil design problem, in which an engineer seeks a shape with desired performance characteristics, is fundamental to aerodynamics. Design workflows traditionally rely on iterative optimization methods using low-fidelity integral boundary-layer methods as higher-fidelity adjoint-based computational fluid dynamics methods are computationally expensive. Surrogate-based approaches can accelerate the design process but still rely on some iterative inverse design procedure. In this work, we leverage emerging invertible neural network (INN) tools to enable the rapid inverse design of airfoil shapes for wind turbines. INNs are specialized deep-learning models with well-defined inverse mappings. When trained appropriately, INN surrogate models are capable of forward prediction of aerodynamic and structural quantities for a given airfoil shape as well as inverse recovery of airfoil shapes with specified aerodynamic and structural characteristics. The INN approach offers a roughly 100 times speed-up compared to adjoint-based methods for inverse design. We demonstrate the INN tool for inverse design on three test cases of 100 airfoils each that satisfy the performance characteristics close to those of airfoils used in wind-turbine blades. All generated shapes satisfy the desired aerodynamic characteristics, demonstrating the success of the INN approach for inverse design of airfoils.

17 WIND ENERGY↗