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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A machine learning-based fast frequency response control for a VSC-HVDC system

An HVDC system can realize a very fast frequency response to the disturbed system under a contingency because its active power control is decoupled from the frequency deviation. However, most of existing HVDC frequency control strategies are coupled with system primary frequency control and secondary frequency control. Since the traditional system frequency control is dominated by the thermal generators, the advantage of the fast response of the HVDC system is not made fully used. The development of a frequency response estimation based on a machine learning algorithm provides another approach to improve the frequency response capability of the HVDC system. Different from other frequency deviation tracking strategies, a machine learning based HVDC frequency response control can directly increase the power flow of a HVDC system by estimation of the system generator or load lost. In this paper, a fast frequency response control using a HVDC system for a large power system disturbance based on the multivariate random forest regression (MRFR) algorithm is proposed. The simulation is carried out with an integrated power system model based on the North American interconnections. The simulation results indicate that the proposed MRFR based frequency response control can significantly improve the frequency low point during an event, while stabilizing the frequency in advance.

42 ENGINEERING↗
DOE OSTI2021

Graph Convolutional Network-Based Topology Embedded Deep Reinforcement Learning for Voltage Stability Control

Topological variations in power system is a common phenomenon and can impose significant challenges to traditional controllers of power system. Recent study revealed the strength of deep reinforcement learning (DRL) based approaches in power system preventive and corrective control. But topological variations are difficult to capture using classical fully connected neural network (FCN) model and has not been explicitly modeled in previous work. Hence, we develop a Graph Convolutional Network (GCN) based DRL framework to tackle topology changes in control design of power system. The GCN model exploits the graph structure of the power network and helps the DRL agent to embed the topology information during learning process. Our GCN based approach is evaluated using the IEEE-39 bus system and it outperforms the FCN-based DRL scheme in terms of training convergence and control performance considering grid topology changes.

Hossain, Ramij Raja↗
DOE OSTI2016

Short-Term Distribution System State Forecast Based on Optimal Synchrophasor Sensor Placement and Extreme Learning Machine

This paper proposes an approach for distribution system state forecasting, which aims to provide an accurate and high speed state forecasting with an optimal synchrophasor sensor placement (OSSP) based state estimator and an extreme learning machine (ELM) based forecaster. Specifically, considering the sensor installation cost and measurement error, an OSSP algorithm is proposed to reduce the number of synchrophasor sensor and keep the whole distribution system numerically and topologically observable. Then, the weighted least square (WLS) based system state estimator is used to produce the training data for the proposed forecaster. Traditionally, the artificial neural network (ANN) and support vector regression (SVR) are widely used in forecasting due to their nonlinear modeling capabilities. However, the ANN contains heavy computation load and the best parameters for SVR are difficult to obtain. In this paper, the ELM, which overcomes these drawbacks, is used to forecast the future system states with the historical system states. The proposed approach is effective and accurate based on the testing results.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

BOS Gas Detection Pipeline (Integrated System for Optical Hydrogen Detection Using Background Oriented Schlieren and Machine Learning) [SWR-26-007]

This software is the world's first integrated background oriented schlieren and machine learning-based leak detection system. The system provides real time visualization of gas leaks and machine learning interpenetration of leak severity. The software is supplemented by SWR-25-177, "gpu_piv (Graphics Processing Unit Accelerated Background Oriented Schlieren Algorithm", also developed by the National Laboratory of the Rockies. SEE DOECODE ID 182832.

Palin, Ian [National Laboratory of the Rockies (NL↗
DOE OSTI2021

BETO 2021 Peer Review - Biomethanation to Upgrade Biogas to Pipeline Grade Methane WBS 5.1.3.102

We are developing, innovating and de-risking a biomethanation process capable of megawatt-scale deployment that upgrades biogas waste streams to produce pipeline quality renewable natural gas (RNG). Biomethanation is a two-step process using a methanogenic microorganism to convert renewable hydrogen (H2) and waste carbon dioxide (CO2) to renewable methane (CH4) - the primary component in natural gas. Using biogenic CO2 from biogas sources like dairies, wastewater treatment plants, and landfills allows production of this drop-in direct replacement fuel to participate in the growing number of carbon markets; like California's Low Carbon Fuel Standard and the Federal Renewable Fuel Standard. The end-of-project goal is to demonstrate pipeline quality RNG production (> 95% CH4, < 4% H2, <1% CO2, < 0.2% O2 and < 4 parts per million hydrogen sulfide) using real biogas feedstocks. We will accomplish this goal by designing and building a pressurized (18 bar) mobile lab-scale (20L) bioreactor research platform, including integrated electrolyzer system, based on lessons learned from operating the 700L pilot system from Southern California Gas Company. In collaboration with Electrochaea, natural gas utilities and Argonne National lab, will provide the data to establish a preliminary range of carbon intensity to help accelerate the deployment of utility-scale H2 production and qualify the biomethanation pathway process for RNG production.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Machine Learning-Based PV Reserve Determination Strategy for Frequency Control on the WECC System: Preprint

Frequency control from Photovoltaic (PV) plants has great potential to address the frequency response challenge of the power system with high renewable penetration. However, using model-based approaches to determine the optimal PV headroom reserve requires significant online computation and is intractable for an interconnection level system. This paper proposes a machine learning based strategy, that is suitable for real-time operation, to determine the optimal PV reserve for frequency control. The proposed machine learning algorithm is trained and tested on 1,987 offline simulations of a 60% renewable penetration Western Electricity Coordinating Council (WECC) system. Furthermore, the proposed reserve determination strategy is applied on a realistic one-day operation profile of the WECC system and demonstrates over 40% PV headroom saving compared to a conservative approach. It is evident that the proposed strategy can efficiently and effectively determine the optimal PV frequency control reserve for realistic interconnection systems.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2021

Direct Air Capture of CO2 (DAC) [Tech Snapshot]

Researchers at Los Alamos have extensive capabilities in design, testing and optimization of: (1) new capture materials based on membrane separation, amine and other solvent and sorbent separation, and porous materials; (2) biological capture methods, particularly algae and plants; (3) full-scale systems using science-based virtual learning; and (4) system performance under specific meteorological conditions, impacts of CO2 capture via atmospheric data and simulations, and availability of energy and storage capacity. The goal for Los Alamos is advancing our physical technologies and engineered systems approaches from the development phase to a minimal viable product in the energy sector for commercial purposes. Los Alamos is seeking strategic commercialization partners to work with us in advancing these capabilities and technologies for commercial viability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Barrier Function-based Reinforcement Learning for Emergency Control of Power Systems

Under voltage load shedding has been considered as a standard and effective measure to recover the voltage stability of the electric power grid under emergency and severe conditions. However, this scheme usually trips a massive amount of load which can be unnecessary and harmful to customers. Recently, deep reinforcement learning (RL) has been regarded and adopted as a promising approach that can significantly reduce the amount of load shedding. However, like most existing machine learning (ML)-based control techniques, RL control usually cannot guarantee the safety of the systems under control. In this paper, we introduce a novel safe RL method for load shedding of power systems, that can enhance the safe voltage recovery of the electric power grid after experiencing faults. Unlike standard RL method, the safe RL method integrates a Barrier function into the reward function. Consequently, the optimal control policy can render the power system to avoid the safety bounds. This method is general and can be applied to other safety-critical control problems. Numerical simulations on the 39-bus IEEE benchmark is performed to demonstrate the effectiveness of the proposed safe RL emergency control, as well as its adaptive capability to faults not seen in the training.

Vu, Thanh Long↗
DOE OSTI2024

An innovative heterogeneous transfer learning framework to enhance the scalability of deep reinforcement learning controllers in buildings with integrated energy systems

Deep Reinforcement Learning (DRL)-based control shows enhanced performance in the management of integrated energy systems when compared with Rule-Based Controllers (RBCs), but it still lacks scalability and generalisation due to the necessity of using tailored models for the training process. Transfer Learning (TL) is a potential solution to address this limitation. However, existing TL applications in building control have been mostly tested among buildings with similar features, not addressing the need to scale up advanced control in real-world scenarios with diverse energy systems. This paper assesses the performance of an online heterogeneous TL strategy, comparing it with RBC and offline and online DRL controllers in a simulation setup using EnergyPlus and Python. The study tests the transfer in both transductive and inductive settings of a DRL policy designed to manage a chiller coupled with a Thermal Energy Storage (TES). The control policy is pre-trained on a source building and transferred to various target buildings characterised by an integrated energy system including photovoltaic and battery energy storage systems, different building envelope features, occupancy schedule and boundary conditions (e.g., weather and price signal). The TL approach incorporates model slicing, imitation learning and fine-tuning to handle diverse state spaces and reward functions between source and target buildings. Results show that the proposed methodology leads to a reduction of 10% in electricity cost and between 10% and 40% in the mean value of the daily average temperature violation rate compared to RBC and online DRL controllers. Moreover, online TL maximises self-sufficiency and self-consumption by 9% and 11% with respect to RBC. Conversely, online TL achieves worse performance compared to offline DRL in either transductive or inductive settings. However, offline Deep Reinforcement Learning (DRL) agents should be trained at least for 15 episodes to reach the same level of performance as the online TL. Therefore, the proposed online TL methodology is effective, completely model-free and it can be directly implemented in real buildings with satisfying performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Data-driven reinforcement learning–based real-time energy management system for plug-in hybrid electric vehicles

Plug-in hybrid electric vehicles (PHEVs) show great promise in reducing transportation-related fossil fuel consumption and greenhouse gas emissions. Designing an efficient energy management system (EMS) for PHEVs to achieve better fuel economy has been an active research topic for decades. Most of the advanced systems rely either on a priori knowledge of future driving conditions to achieve the optimal but not real-time solution (e.g., using a dynamic programming strategy) or on only current driving situations to achieve a real-time but nonoptimal solution (e.g., rule-based strategy). This paper proposes a reinforcement learning–based real-time EMS for PHEVs to address the trade-off between real-time performance and optimal energy savings. The proposed model can optimize the power-split control in real time while learning the optimal decisions from historical driving cycles. Here, a case study on a real-world commute trip shows that about a 12% fuel saving can be achieved without considering charging opportunities; further, an 8% fuel saving can be achieved when charging opportunities are considered, compared with the standard binary mode control strategy.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Development of a Smart Alarm System for the CEBAF Injector

RadiaSoft and Jefferson Laboratory are working together to develop a machine-learning-based smart alarm system for the CEBAF injector. Because of the injector’s large number of parameters and possible fault scenarios, it is highly desirable to have an autonomous alarm system that can quickly identify and diagnose unusual machine states. We present our work on artificial neural networks designed to identify such undesirable machine states. In particular, we test both auto-encoders and inverse models as possible tools for differentiating between normal and abnormal states. These models are being developed using both supervised and unsupervised learning techniques, and are being trained using CEBAF injector data collected during dedicated machine studies as well as during regular operations. Lastly, we discuss tradeoffs between the two types of models.

Abell, D. T.↗
DOE OSTI2019

Automated control of microfluidic devices based on machine learning

A system is provided to automatically monitor and control the operation of a microfluidic device using machine learning technology. The system receives images of a channel of a microfluidic device collected by a camera during operation of the microfluidic device. Upon receiving an image, the system applies a classifier to the image to classify the operation of the microfluidic device as normal, in which no adjustment to the operation is needed, or as abnormal, in which an adjustment to the operation is needed. When an image is classified as normal, the system may make no adjustment to the microfluidic device. If, however, an image is classified as abnormal, the system may output an indication that the operation is abnormal, output an indication of a needed adjustment, or control the microfluidic device to make the needed adjustment.

42 ENGINEERING↗
DOE OSTI2021

Automated control of microfluidic devices based on machine learning

A system is provided to automatically monitor and control the operation of a microfluidic device using machine learning technology. The system receives images of a channel of a microfluidic device collected by a camera during operation of the microfluidic device. Upon receiving an image, the system applies a classifier to the image to classify the operation of the microfluidic device as normal, in which no adjustment to the operation is needed, or as abnormal, in which an adjustment to the operation is needed. When an image is classified as normal, the system may make no adjustment to the microfluidic device. If, however, an image is classified as abnormal, the system may output an indication that the operation is abnormal, output an indication of a needed adjustment, or control the microfluidic device to make the needed adjustment.

Giera, Brian↗
DOE OSTI2017

Machine Learning

The absence of a robust and unified theory of cyber dynamics presents challenges and opportunities for using machine learning based data-driven approaches to further the understanding of the behavior of such complex systems. Analysts can also use machine learning approaches to gain operational insights. In order to be operationally beneficial, cybersecurity machine learning based models need to have the ability to: (1) represent a real-world system, (2) infer system properties, and (3) learn and adapt based on expert knowledge and observations. Probabilistic models and Probabilistic graphical models provide these necessary properties and are further explored in this chapter. Bayesian Networks and Hidden Markov Models are introduced as an example of a widely used data driven classification/modeling strategy.

Anomaly detection↗