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DOE OSTI2023

RX-ADS: Interpretable Anomaly Detection Using Adversarial ML for Electric Vehicle CAN Data

Recent year has brought considerable advancements in Electric Vehicles (EVs) and associated infrastructures/ communications. Intrusion Detection Systems (IDS) are widely deployed for anomaly detection in such critical infrastructures. This paper presents an Interpretable Anomaly Detection System (RX-ADS) for intrusion detection in CAN protocol communication in EVs. Contributions include: 1) window based feature extraction method; 2) deep Autoencoder based anomaly detection method; and 3) adversarial machine learning based explanation generation methodology. The presented approach was tested on two benchmark CAN datasets: OTIDS and Car Hacking. The anomaly detection performance of RX-ADS was compared against the state-of-the-art approaches on these datasets: HIDS and GIDS. The RX-ADS approach presented performance comparable to the HIDS approach (OTIDS dataset) and has outperformed HIDS and GIDS approaches (Car Hacking dataset). Further, the proposed approach was able to generate explanations for detected abnormal behaviors arising from various intrusions. Furthermore, these explanations were later validated by information used by domain experts to detect anomalies. Other advantages of RX-ADS include: 1) the method can be trained on unlabeled data; 2) explanations help experts in understanding anomalies and root course analysis, and also help with AI model debugging and diagnostics, ultimately improving user trust in AI systems.

42 ENGINEERING↗

DOE OSTI2023

A Mobile Edge Computing Framework for Traffic Optimization At Urban Intersections Through Cyber-Physical Integration

The stop-and-go traffic pattern on urban roads often results in excessive energy consumption because of unnecessary vehicle braking, idling, and accelerations. With the widespread and increased use of automobiles, this traffic pattern creates many negative impacts (e.g., delayed travel time, air pollution, and additional carbon emission) on the sustainability of our cities. Taking advantage of the recent emerging Internet of Things (IoT) and edge computing paradigms, we propose a mobile edge computing framework that integrates the capability of real-time vehicle-to-infrastructure communication and intelligent speed optimization algorithms into a mobile app to optimize individual vehicles' driving speed at signalized intersections. The optimization aims to mitigate the stop-and-go traffic pattern and its undesirable consequences in urban transportation systems. The framework consists of (1) a cyberinfrastructure-enabled dynamic messaging system for retrieving and delivering real-time traffic and signal phase and timing information from IoT-connected signal controllers and sensors, (2) a real-time speed optimization algorithm for generating intelligent speed advisory using vehicle's information (e.g., GPS and driving directions from mobile sensing) and corresponding signal and traffic information, and (3) an ad-hoc mobile computing environment that converts drivers' smartphones into edge devices to host the speed optimization algorithms for enabling intelligent advisory on the vehicle's driving speed within signalized corridors. The paper presents the design and implementation of the proposed framework. Finally, we demonstrate the feasibility, usefulness, and energy-saving benefits of our proposed framework and its prototyping mobile app on urban transportation systems through traffic simulation, real-vehicle laboratory experiments, an evaluative survey, and field communication tests. The simulation-based energy evaluation results show that the 100% usage of the mobile app can achieve 24% energy savings in the transportation system.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2022

Vulnerability of a VOC-Based Inverter Due to Noise Injection and Its Mitigation

Even though virtual oscillator control (VOC)-based inverters do not communicate with each other, they need to make local measurements for control. The impact of tampering with these measured or sensed signals on the performance of a VOC-based inverter and synchronization of multiple such inverters is an important but open-ended issue. As such, this letter explores the impact of intentional side-channel noise intrusion (SNI) on the synchronization of VOC-based communication-free self-synchronizing inverters (CFSIs). Two different scenarios are investigated via experimental and analytical studies using a half-bridge neutral point clamped (NPC) single-phase CFSI. Furthermore, they address the impact of SNI on the ability of a CFSI to ensure a stable 60-Hz limit cycle and on the parallel operation of two such CFSIs to ensure synchronism to a common 60-Hz load frequency.

42 ENGINEERING↗

DOE OSTI2021

Roles of Dynamic State Estimation in Power System Modeling, Monitoring and Operation

—Power system dynamic state estimation (DSE) remains an active research area. This is driven by the absence of accurate models, the increasing availability of fast-sampled, time synchronized measurements, and the advances in the capability, scalability, and affordability of computing and communications. This paper discusses the advantages of DSE as compared to static state estimation, and the implementation differences between the two, including the measurement configuration, modeling framework and support software features. The important roles of DSE are discussed from modeling, monitoring and operation aspects for today’s synchronous machine dominated systems and the future power electronics-interfaced generation systems. Several examples are presented to demonstrate the benefits of DSE on enhancing the operational robustness and resilience of 21st century power system through time critical applications. Future research directions are identified and discussed, paving the way for developing the next generation of energy management systems.

Zhao, Junbo↗

DOE OSTI2022

PowerNet: Multi-agent Deep Reinforcement Learning for Scalable Powergrid Control

This paper develops an efficient multi-agent deep reinforcement learning algorithm for cooperative controls in powergrids. Specifically, we consider the decentralized inverter-based secondary voltage control problem in distributed generators (DGs), which is first formulated as a cooperative multi-agent reinforcement learning (MARL) problem. We then propose a novel on-policy MARL algorithm, PowerNet, in which each agent (DG) learns a control policy based on (sub-)global reward but local states and encoded communication messages from its neighbors. Motivated by the fact that a local control from one agent has limited impact on agents distant from it, we exploit a novel spatial discount factor to reduce the effect from remote agents, to expedite the training process and improve scalability. Furthermore, a differentiable, learning-based communication protocol is employed to foster the collaborations among neighboring agents. In addition, to mitigate the effects of system uncertainty and random noise introduced during on-policy learning, we utilize an action smoothing factor to stabilize the policy execution. To facilitate training and evaluation, we develop PGSim, an efficient, high-fidelity powergrid simulation platform. Here, experimental results in two microgrid setups show that the developed PowerNet outperforms the conventional model-based control method, as well as several state-of-the-art MARL algorithms. The decentralized learning scheme and high sample efficiency also make it viable to large-scale power grids.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Machine Learning for Scalable and Optimal Load Shedding Under Power System Contingency

Prompt and effective corrective actions in response to unexpected contingencies are crucial for improving power system resilience and preventing cascading blackouts. The optimal load shedding (OLS) accounting for network limits has the potential to address the diverse system-wide impacts of contingency scenarios as compared to traditional local schemes. However, due to the fast cascading propagation of initial contingencies, real-time OLS solutions are challenging to attain in large systems with high computation and communication needs. In this paper, we propose a decentralized design that leverages offline training of a neural network (NN) model for individual load centers to autonomously construct the OLS solutions from locally available measurements. Our learning-for-OLS approach can greatly reduce the computation and communication needs during online emergency responses, thus preventing the cascading propagation of contingencies for enhanced power grid resilience. Numerical studies on both the IEEE 118-bus system and a synthetic Texas 2000-bus system have demonstrated the efficiency and effectiveness of our scalable OLS learning design for timely power system emergency operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

A Cyber-Physical Anomaly Detection for Wide-Area Protection Using Machine Learning

Wide-area protection scheme (WAPS) provides system-wide protection by detecting and mitigating small and large-scale disturbances that are difficult to resolve using local protection schemes. As this protection scheme is evolving from a substation-based distributed remedial action scheme (DRAS) to the control center-based centralized RAS (CRAS), it presents severe challenges to their cybersecurity because of its heavy reliance on an insecure grid communication, and its compromise would lead to system failure. This article presents an architecture and methodology for developing a cyber-physical anomaly detection system (CPADS) that utilizes synchrophasor measurements and properties of network packets to detect data integrity and communication failure attacks on measurement and control signals in CRAS. The proposed machine leaning-based methodology applies a rules-based approach to select relevant input features, utilizes variational mode decomposition (VMD) and decision tree (DT) algorithms to develop multiple classification models, and performs final event identification using a rules-based decision logic. Here, we have evaluated the proposed methodology of CPADS using the IEEE 39 bus system for several performance measures (accuracy, recall, precision, and F-measure) in a cyber-physical testbed environment. Furthermore, our experimental results reveal that the proposed algorithm (VMD-DT) of CPADS outperforms the existing machine learning classifiers during noisy and noise-free measurements while incurring an acceptable processing overhead.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Bayesian GAN-Based False Data Injection Attack Detection in Active Distribution Grids With DERs

Advancements in information and communication technologies have revolutionized monitoring and control capabilities within smart grids. However, it also brings new vulnerabilities to data acquisition systems and state estimation functions, which attackers can subtly tamper with the measurement data through compromising the communication network. Moreover, the high penetration of renewable energy sources with the inherited characteristics of uncertainty and variability further complicates the design of effective intrusion detection systems. In this paper, a Bayesian deep learning-based approach is developed to detect cyber attacks and maintain the security of smart grids. Our method specifically addresses the prevalent issue of imbalanced data in real power systems, which arises from the predominance of normal system operations over compromised or attacked states. Employing a novel Bayesian GAN-based technique, our approach successfully discriminates between secure and compromised measurement data, even in scenarios with significant data imbalance. Furthermore, the proposed method accommodates various practical application factors, ensuring accurate intrusion detection despite the presence of measurement noise. The feasibility and effectiveness of the proposed detection mechanism are validated by testing on IEEE 13-node and 123-node test systems. Simulation results and comparisons with literature methods demonstrate the superiority of proposed cybersecurity solutions.

Bayesian GAN↗

DOE OSTI2021

Stochastic Gradient-Based Distributed Bayesian Estimation in Cooperative Sensor Networks

Distributed Bayesian inference provides a full quantification of uncertainty offering numerous advantages over point estimates that autonomous sensor networks are able to exploit. However, fully-decentralized Bayesian inference often requires large communication overheads and low network latency, resources that are not typically available in practical applications. In this paper, we propose a decentralized Bayesian inference approach based on stochastic gradient Langevin dynamics, which produces full posterior distributions at each of the nodes with significantly lower communication overhead. We provide analytical results on convergence of the proposed distributed algorithm to the centralized posterior, under typical network constraints. Finally, we also provide extensive simulation results to demonstrate the validity of the proposed approach.

42 ENGINEERING↗

DOE OSTI2020

MPC-Based Local Voltage Control Strategy of DGs in Active Distribution Networks

The increasing penetration of distributed generators (DGs) exacerbates the voltage violations in active distribution networks (ADNs). The commonly used centralized voltage control may aggravate the burden of computation and communication. Local voltage control can realize fast response to frequent voltage fluctuations. However, effective local control for DGs still needs to be further investigated. In this paper, a model predictive control (MPC)-based local voltage control strategy of DGs is proposed to cope with voltage violation problems and enhance the adaptation to the DG volatility. DGs are regulated with local Q-V control curves to respond to voltage fluctuations rapidly. Based on the dynamic network partition, a unified control curve for DGs is set in the same area to realize efficient operation. To further improve the performance of voltage control, the parameter tuning of control curves is regularly conducted based on decentralized inter-area coordination with the rolling of the control horizon. Finally, case study is verified on the modified IEEE 33-node distribution system. The proposed local control strategy of DGs can effectively improve the voltage profile and reduce power losses of the whole network without high computation and communication burden.

active distribution network (ADN)↗

DOE OSTI2022

Multi-Segment Decentralized Control Strategies for Renewables-Rich Microgrids in Extreme Conditions

Microgrids provide a promising approach to accommodating various distributed energy resources (DERs), while requiring significant communication infrastructures that may be affected by extreme conditions such as natural disasters and cyber-attacks. In this paper, a fully decentralized control strategy without the need for communication is proposed for islanded microgrids with high renewables penetration. First, special multi-segment power/frequency characteristic curves are designed, so that different DERs can be automatically coordinated in a prioritized manner such as renewables first to maintain power balance while DER frequencies are regulated at their reference values. Second, piecewise linear served load versus frequency models are designed to prioritize loads according to their significance, so that only noncritical loads will be curtailed as needed while critical loads are supplied without any interruptions during power deficiency. The proposed strategy can effectively deal with various normal and extreme system conditions including 100% renewables penetration, loads and renewables variations, power deficiencies requiring load curtailments, disconnection of existing DERs, connection of new DERs as well as network sectionalization and reconfiguration. As a result, the proposed control strategy is validated in the real-time digital simulator (RTDS) model of the IIT Campus Microgrid to demonstrate its effectiveness in enhancing the resiliency of renewables-rich microgrids in extreme conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Advances in High-Power Wireless Charging Systems: Overview and Design Considerations

Wireless charging systems are foreseen as an effective solution to improve the convenience and safety of conventional conductive chargers. As this technology has matured, recent broad applications of wireless chargers to electrified transportation have indicated a trend toward higher power, power density, modularity, and scalability of designs. In this article, commercial systems and laboratory prototypes are reviewed, focusing mostly on the advances in high-power wireless charging systems. The recent endeavors in magnetic pad designs, compensation networks, power electronics converters, control strategies, and communication protocols are illustrated. Both stationary and dynamic (in-motion) wireless charging systems are discussed, and critical differences in their designs and applications are emphasized. On that basis, the comparisons among different solutions and design considerations are summarized to present the essential elements and technology roadmap that will be necessary to support large-scale deployment of high-power wireless charging systems. The review is concluded with the discussion of several fundamental challenges and prospects of high-power wireless power transfer (WPT) systems. Foreseen challenges include utilization of advanced materials, electric and electromagnetic field measurement and mitigation, customization, communications, power metering, and cybersecurity.

25 ENERGY STORAGE↗

DOE OSTI2023

A Photovoltaic MPPT Charge Controller Real-Time Testbed for Cybersecurity Applications

The increasing deployment of distributed energy resources (DER) over the last decade is a great ally to combat climate change and strengthen the grid during increasingly common extreme weather events. However, DER systems, combined with the ongoing transition to a digital power grid, also pose substantial cybersecurity threats. One of the most common communication protocols used in DER integration is the Distributed Network Protocol 3 (DNP3), which is known to have many security vulnerabilities. Thus, it is essential to investigate cyberattack behaviors and mitigation on power systems using DNP3. In this paper, we designed and implemented a cybersecurity testbed for a simulated photovoltaic (PV) maximum power point tracking (MPPT) charge controller. Our testbed uses an MPPT charge controller simulated on a Typhoon HIL602+ real-time simulator with a real DNP3 communication connection over TCP/IP, allowing for safe and efficient monitoring and manipulation of data traffic between the simulated hardware and supervisory control and data acquisition (SCADA) systems.

14 SOLAR ENERGY↗

DOE OSTI2024

Positioning Accuracy in a Concurrent Robot-CNC Hybrid Manufacturing System

Abstract Additive manufacturing (AM) has gained notoriety for offering advantages over traditional manufacturing methods, such as increased design complexity and flexibility. However, it has not found widespread use beyond rapid prototyping. One hindrance to the acceptance of AM processes in industry is the time and cost of fabrication per component. While metal AM by itself can be inexpensive, extra manufacturing steps in the form of subtractive manufacturing (SM) may need to be performed to reach final part tolerances, leading to hybrid additive-subtractive manufacturing (HASM) of a part, which increases time and cost. A potential area to reduce cost is through increasing the efficiency of the HASM process by conducting additive and subtractive manufacturing simultaneously. Usually, HASM is performed in a process where AM is completed in one machine or cell and transferred to another machine or cell for SM in a sequential assembly line process. This efficiency decreases part cost, but high aspect ratio parts or parts with internal geometry that require interleaved additive deposition and machining cannot be produced. One unexplored solution to simultaneous HASM that allows for interleaved operations is to operate the deposition head and machining spindle concurrently within the same machine envelope, known as concurrent HASM (CHASM). In this type of process, both AM and SM occur simultaneously on a batch of small parts or a single large part, maintaining a high efficiency without sacrificing the full range of complex geometries that AM allows for. A potential approach to the single-machine method could be to combine a robot and mill within the same envelope. A challenge to this approach, however, is control of both systems. Most machine controllers have limited external communication or, if a robot has been integrated, only offer movement of either the robot or mill at any given time. As a result, systems must pause either the AM or SM process to switch between them rather than working simultaneously. The present work investigates the positional accuracy of such a CHASM system comprised of a robotic arm and a 3-axis mill. Open-loop tests with limited communication between machines are performed on the system to verify positional error during concurrent robot-mill movements. Under certain conditions, it is demonstrated that position error can stay within 2 mm for the duration of a single layer; however, these tests show that, generally, the open-loop positioning performance of the system is inadequate for CHASM without part-specific hand-tuning of parameters. Based on these results, a set of requirements for successful robot-CNC CHASM is proposed for future integrations.

Goodwin, Jesse↗

DOE OSTI2021

Bridging the Gap between Analytical and Microbial Sciences in Microbiome Research

Metabolites from the microbiome influence human, animal, and environmental health, but the diversity and functional roles of these compounds have only begun to be elucidated. Comprehensively characterizing these molecules are significant challenges, as it requires expertise in analytical methods, such as mass spectrometry and nuclear magnetic resonance spectroscopy, skills that not many traditional microbiologists or microbial ecologists possess. This creates a gap between microbiome scientists that want to understand the role of microbial metabolites in microbiome systems and the skills required to generate and interpret complex metabolomics data sets. To bridge this gap, microbiome scientists should engage analytical chemists to best understand the underlying chemical principles of the data. Conversely, analytical scientists are encouraged to engage with microbiome scientists to better understand the biological questions being asked with metabolomics and to best communicate its intricacies. Better communication across the chemistry/biology disciplines will further reveal the “dark matter” within microbiomes that maintain healthy humans and environments.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Decoding the chemical language of Suillus fungi: genome mining and untargeted metabolomics uncover terpene chemical diversity

ABSTRACT Ectomycorrhizal fungi establish mutually beneficial relationships with trees, trading nutrients for carbon. Suillus are ectomycorrhizal fungi that are critical to the health of boreal and temperate forest ecosystems. Comparative genomics has identified a high number of non-ribosomal peptide synthetase and terpene biosynthetic gene clusters (BGC) potentially involved in fungal competition and communication. However, the functionality of these BGCs is not known. This study employed co-culture techniques to activate BGC expression and then used metabolomics to investigate the diversity of metabolic products produced by three Suillus species ( Suillus hirtellus EM16, Suillus decipiens EM49, and Suillus cothurnatus VC1858), core members of the pine microbiome. After 28 days of growth on solid media, liquid chromatography–tandem mass spectrometry identified a diverse range of extracellular metabolites (exometabolites) along the interaction zone between Suillus co-cultures. Prenol lipids were among the most abundant chemical classes. Out of the 62 unique terpene BGCs predicted by genome mining, 41 putative prenol lipids (includes 37 putative terpenes) were identified across the three Suillus species using metabolomics. Notably, some terpenes were significantly more abundant in co-culture conditions. For example, we identified a metabolite matching to isomers isopimaric acid, sandaracopimaric acid, and abietic acid, which can be found in pine resin and play important roles in host defense mechanisms and Suillus spore germination. This research highlights the importance of combining genomics and metabolomics to advance our understanding of the chemical diversity underpinning fungal signaling and communication. IMPORTANCE Using a combination of genomics and metabolomics, this study’s findings offer new insights into the chemical diversity of Suillus fungi, which serve a critical role in forest ecosystems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Randomized Projection for Rank-Revealing Matrix Factorizations and Low-Rank Approximations

Rank-revealing matrix decompositions provide an essential tool in spectral analysis of matrices, including the Singular Value Decomposition (SVD) and related low-rank approximation techniques. QR with Column Pivoting (QRCP) is usually suitable for these purposes, but it can be much slower than the unpivoted QR algorithm. For large matrices, the difference in performance is due to increased communication between the processor and slow memory, which QRCP needs in order to choose pivots during decomposition. Our main algorithm, Randomized QR with Column Pivoting (RQRCP), uses randomized projection to make pivot decisions from a much smaller sample matrix, which we can construct to reside in a faster level of memory than the original matrix. This technique may be understood as trading vastly reduced communication for a controlled increase in uncertainty during the decision process. Furthermore, for rank-revealing purposes, the selection mechanism in RQRCP produces results that are the same quality as the standard algorithm, but with performance near that of unpivoted QR (often an order of magnitude faster for large matrices). Additionally, we also propose two formulas that facilitate further performance improvements. The first efficiently updates sample matrices to avoid computing new randomized projections. The second avoids large trailing updates during the decomposition in truncated low-rank approximations. Our truncated version of RQRCP also provides a key initial step in our truncated SVD approximation, TUXV. These advances open up a new performance domain for large matrix factorizations that will support efficient problem-solving techniques for challenging applications in science, engineering, and data analysis.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

A Shift Selection Strategy for Parallel Shift-invert Spectrum Slicing in Symmetric Self-consistent Eigenvalue Computation

The central importance of large-scale eigenvalue problems in scientific computation necessitates the development of massively parallel algorithms for their solution. Recent advances in dense numerical linear algebra have enabled the routine treatment of eigenvalue problems with dimensions on the order of hundreds of thousands on the world’s largest supercomputers. In cases where dense treatments are not feasible, Krylov subspace methods offer an attractive alternative due to the fact that they do not require storage of the problem matrices. However, demonstration of scalability of either of these classes of eigenvalue algorithms on computing architectures capable of expressing massive parallelism is non-trivial due to communication requirements and serial bottlenecks, respectively. In this work, we introduce the SISLICE method: a parallel shift-invert algorithm for the solution of the symmetric self-consistent field (SCF) eigenvalue problem. The SISLICE method drastically reduces the communication requirement of current parallel shift-invert eigenvalue algorithms through various shift selection and migration techniques based on density of states estimation and k-means clustering, respectively. This work demonstrates the robustness and parallel performance of the SISLICE method on a representative set of SCF eigenvalue problems and outlines research directions that will be explored in future work.

97 MATHEMATICS AND COMPUTING↗