Parallel Identification of Power System Dynamic Models Under Scheduling Constraints
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Engineering↗
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Results for “System Identification”
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A Data-Driven Framework for Power System Event Type Identification via Safe Semi-Supervised Techniques
Herein this paper investigates the use of phasor measurement unit (PMU) data with deep learning techniques to construct real-time event identification models for transmission networks. Increasing penetration of distributed energy resources represents a great opportunity to achieve decarbonization, as well as challenges in systematic situational awareness. When high-resolution PMU data and sufficient manually recorded event labels are available, the power event identification problem is defined as a statistical classification problem that can be solved by numerous cutting-edge classifiers. However, in real grids, collecting tremendous high-quality event labels is quite expensive. Utilities frequently have a large number of event records without in-depth details (i.e., unlabeled events). To bridge this gap, we propose a novel semi-supervised learning-based method to improve the performance of event classifiers trained with a limited number of labeled events by exploiting the information from massive unlabeled events. In other words, compared to existing data-driven methods, our method requires only a small portion of labeled data to achieve a similar level of accuracy. Meanwhile, this work discusses and addresses the performance degradation caused by class distribution mismatch between the training set and the real applications. Based on the proposed safe learning mechanism, our model does not directly use all unlabeled events during model training, but selectively uses them through a comprehensive evaluation procedure. Numerical studies on a sizable PMU dataset have been used to validate the performance of the proposed method.
42 ENGINEERING↗
Global Cylinder Identification and Monitoring System: Nonproliferation Concerns and Baseline Definition
With growing nuclear commerce and new safeguards initiatives, the international safeguards system continues to face ever-increasing demands to verify nuclear materials. Emerging proliferation threats from both state and non-state actors are also leading to increased concerns over the diversion of nuclear materials—for example, the loss of cylinders containing uranium hexafluoride (UF 6 ) during transport or the introduction of undeclared feed into enrichment plants. The International Atomic Energy Agency (IAEA) goal for detecting the diversion of a significant quantity (SQ) of natural or low-enriched uranium (LEU) is 1 year. If a proliferant state were able to obtain a cylinder containing natural or low-enriched UF 6 , it could produce a SQ of highly enriched uranium (HEU) (25 kilograms) in less than a year using a moderately sized enrichment facility.
White-Horton, Jessica L.↗
Consistent Principles for Particle Identification by PSD Systems
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Nuclear science and engineering, Mathematics and C↗
Physics-Informed Machine Learning-Aided System Space Discretization
Decision-making is the process of identifying and choosing alternatives based on an agreed-upon set of metrics and preferences established by the decision-maker. There are options to be considered during the decision-making process and each option offers a different trajectory and associated success profile in moving from a given system state to the desired system state. The decision-making process typically involves uncertainties associated with the current component and system states. In this sense, probabilistic risk assessment (PRA) can be an analytical method and tool for accomplishing the probabilistic aspect of the decision-making process. Dynamic PRA is an evolution of conventional PRA methodology in which driving forces on modeled plant elements and the element behaviors are explicitly modeled over time. In the recent past, risk assessment methodologies have evolved to address risk issues in a continuously evolving environment and a novel probabilistic dynamics framework in continuous time and state-space discretization forms has been proposed. While state-space discretization has shown its strength in both consequence and causal reasoning modes, several challenges, including the computational requirement and physically meaningful system state identification, exist. Conventional system space discretization has usually been done by either the equal width discretization method or a data-driven method. Those methods naturally possess challenges coming from the physical understanding of discretized system space (i.e., system state) and the trajectory moving from a given system state to another system state. The purpose of this paper is to present a physics-based and data-driven system state discretization method such that one can justify what the discretized system space implies and understand the state trajectory from the viewpoint of operational actions.
Kim, Junyung↗
Exploring Vital Area Identification using Systems-Theoretic Process Analysis.
Abstract not provided.
Sandt, Emily↗
SU-E-T-261: Development of An Automated System to Detect Patient Identification and Positioning Errors Prior to Radiotherapy Treatment
Purpose: To develop a system that can automatically detect patient identification and positioning errors using 3D computed tomography (CT) setup images and kilovoltage CT (kVCT) planning images. Methods: Planning kVCT images were collected for head-and-neck (H&N), pelvis, and spine treatments with corresponding 3D cone-beam CT (CBCT) and megavoltage CT (MVCT) setup images from TrueBeam and TomoTherapy units, respectively. Patient identification errors were simulated by registering setup and planning images from different patients. Positioning errors were simulated by misaligning the setup image by 1cm to 5cm in the six anatomical directions for H&N and pelvis patients. Misalignments for spine treatments were simulated by registering the setup image to adjacent vertebral bodies on the planning kVCT. A body contour of the setup image was used as an initial mask for image comparison. Images were pre-processed by image filtering and air voxel thresholding, and image pairs were assessed using commonly-used image similarity metrics as well as custom -designed metrics. A linear discriminant analysis classifier was trained and tested on the datasets, and misclassification error (MCE), sensitivity, and specificity estimates were generated using 10-fold cross validation. Results: Our workflow produced MCE estimates of 0.7%, 1.7%, and 0% for H&N, pelvis, and spine TomoTherapy images, respectively. Sensitivities and specificities ranged from 98.0% to 100%. MCEs of 3.5%, 2.3%, and 2.1% were obtained for TrueBeam images of the above sites, respectively, with sensitivity and specificity estimates between 96.2% and 98.4%. MCEs for 1cm H&N/pelvis misalignments were 1.3/5.1% and 9.1/8.6% for TomoTherapy and TrueBeam images, respectively. 2cm MCE estimates were 0.4%/1.6% and 3.1/3.2%, respectively. Vertebral misalignment MCEs were 4.8% and 4.9% for TomoTherapy and TrueBeam images, respectively. Conclusion: Patient identification and gross misalignment errors can be robustly and automatically detected using 3D setup images of two imaging modalities across three commonly-treated anatomical sites.
60 APPLIED LIFE SCIENCES↗
Hypergames and Cyber-Physical Security for Control Systems
The identification of the Stuxnet worm in 2010 provided a highly publicized example of a cyber attack that physically damaged an industrial control system. This raised public awareness about the possibility of similar attacks against other industrial targets—including critical infrastructure. Here, we use hypergames to analyze how strategic perturbations of sensor readings and calibrated parameters can be used to manipulate a system that employs optimal control. Hypergames form an extension of game theory that enables us to model strategic interactions where the players may have significantly different perceptions of the game(s) they are playing. Past work with hypergames has focused on relatively simple interactions consisting of a small set of discrete choices for each player. Here, we apply single-stage hypergames to larger systems with continuous variables. We find that manipulating constraints can be a more effective attacker strategy than manipulating objective function parameters. Moreover, the attacker need not change the underlying system to carry out a successful attack—it may be sufficient to deceive the defender controlling the system. It is possible to scale our approach up to even larger systems, but this will depend on the characteristics of the system in question, and we identify several characteristics that will make those systems amenable to hypergame analysis.
97 MATHEMATICS AND COMPUTING↗
Automated identification of deformation twin systems in Mg WE43 from SEM DIC
In this study, the application of machine learning and computer vision approaches to microscale deformation data for the automated identification of deformation twinning systems, and their associated twin area, is introduced. Deformation data was obtained during in-situ SEM compression testing of a WE43 Mg alloy using digital image correlation (DIC) modified for use with electron microscopy. A 5.7 mm × 3.4 mm area of interest was analyzed, generating ~100 million data points of deformation. Experimental twin trace directions were determined by applying k-means clustering, morphological thinning, and Hough transforms to the deformation data. The identification of deformation twinning was achieved by consideration of the twin trace direction, its strain value, and its evolution. The deformation map was divided into areas corresponding to individual active twin systems, enabling the analysis of microstructure dependence of deformation behavior and twinning activity. The performance of the proposed twinning identification approach was evaluated by accuracy, precision, and sensitivity metrics. The effect of the tuning parameters used in the algorithm on the performance is also discussed.
36 MATERIALS SCIENCE↗
Trial-based calibration for audio-based identification, recognition, and detection system
The disclosed technologies include methods for generating a calibration model using data that is selected to match the conditions of a particular trial that involves an automated comparison of data samples, such as a comparison-based trial performed by an audio-based recognition, identification, or detection system. The disclosed technologies also include improved methods for selecting candidate data used to build the calibration model. The disclosed technologies further include methods for evaluating the performance of the calibration model and for rejecting a trial when not enough matched candidate data is available to build the calibration model. The disclosed technologies additionally include the use of regularization and automated data generation techniques to further improve the robustness of the calibration model.
McLaren, Mitchell Leigh↗
Data-driven Event Identification in the U.S. Power Systems Based on 2D-OLPP and RUSBoosting Trees
Accurate event identification is an essential part of situation awareness ability for power system operators. Therefore, this work proposes an integrated event identification algorithm for power systems. First, to obtain and filter suitable inputs for event identification, an event detection trigger based on the rate of change of frequency (RoCoF) is presented. Then, the wave arrival time difference-based triangulation method considering the anisotropy of wave propagation speed is utilized to estimate the location of the detected event. Next, the two-dimensional orthogonal locality preserving projection (2D-OLPP)-based method, which is suitable for multiple types of measured data, is employed to achieve higher effectiveness in extracting the event features compared with traditional one-dimensional projection and principle component analysis (PCA). Finally, the random undersampling boosted (RUSBoosted) trees-based classifier, which can mitigate the data sample imbalance issue, is utilized to identify the type of the detected event. Furthermore, the proposed approach is demonstrated using the actual measurement data of U.S. power systems from FNET/GridEye. Comparison results show that the proposed event identification algorithm can achieve better performance than existing approaches.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Pulsed Photofission Delayed Gamma Ray Detection for Nuclear Material Identification
Innovative systems with increased sensitivity and resolution are in great demand to detect diversion and to prevent misuse in support of nuclear materials management for the U.S. fuel cycle. Nuclear fission is the most important multiplicative process involved in non-destructive active interrogation. This process produces the most easily recognizable signature for nuclear materials. High-energy gamma rays can also excite a nucleus and cause fission through a process known as photofission. After photofission reactions, delayed signals are easily distinguishable from the interrogating radiation. Linac-based, advanced inspection techniques utilizing the fission signals after photofission have been extensively studied for homeland security applications. Previous research also showed that a unique delayed gamma ray energy spectrum exists for each fissionable isotope. Isotopic composition measurement methods based on delayed gamma ray spectroscopy will be the primary focus of this work.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Ensemble voting-based fault classification and location identification for a distribution system with microgrids using smart meter measurements
This study presents an ensemble learning approach for fault classification and location identification in a smart distribution network containing photovoltaics (PV)-based microgrid. Lack of available data points and the unbalanced nature of the distribution system make fault handling a challenging task for utilities. The proposed method uses event-driven voltage data from smart meters to classify and locate faults. The ensemble voting classifier is composed of three base learners; random forest, k-nearest neighbours, and artificial neural network. The fault location (FL) task has been formulated as a classification problem where the fault type is classified in the first step and based on the fault type, the faulty bus is identified. The method is tested on IEEE-123 bus system modified with added PV-based microgrid along with dynamic loading conditions and varying fault resistances from 0 to 20 Ω for both unbalanced and balanced fault types. A further sensitivity analysis has been done to test the robustness of the proposed method under various noise levels and data loss errors in the smart meter measurements. The ensemble method shows improved performance and robustness compared to some previously proposed FL methods. Finally, the proposed method has been experimentally validated on a real-time simulation-based testbed using a state-of-the-art digital real-time simulator, industry standard DNP3 communication protocol and a cpu-based control centre running the FL algorithm.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Measurement-based Coherency Identification and Aggregation for Power Systems
In power system model reduction, a high reduction ratio is often desired to handle much more complex power systems. The bottleneck of traditional methods lies in: Coherency identification methods are conservative. Some coherency generators are not detected when system topology or operating points change, because coherency identification depends on system topology or operating points. There are some solitary generators in external systems. These generators do not belong to any coherency group. However, sometimes these solitary generators have little impact on tie-line power flow, and it might be possible to ignore their dynamics in model reduction. But because they do not belong to any coherency group, existing reduction methods cannot handle them well. In order to overcome the first problem, a measurement-based online coherency identification method is presented in this paper. By analyzing post-fault trajectories measured by Phasor Measurement Units (PMUs), coherency generators are identified through principal component analysis. The method can track time-varying system topology and operating points. In order to address the second problem, this paper introduces sensitivity analysis into traditional reduction methods. The sensitivity of tie-line power flow against injected active power of external system generators is derived. Those generators having loose connection with tie-line power are identified through the sensitivity analysis, and their dynamics are ignored by replacing them with negative impedances. We test if the sensitivity, based on static power flow, provides good guidance to reduce the dynamic model. Case studies show that the proposed method can handle well these solitary generators and the reduction ratio can be enhanced through this method. Future work will include generalization of the sensitivity method.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Identification of open quantum systems from observable time traces
Estimating the parameters that dictate the dynamics of a quantum system is an important task for quantum information processing and quantum metrology, as well as fundamental physics. In our paper we develop a method for parameter estimation for Markovian open quantum systems using a temporal record of measurements on the system. Furthermore, the method is based on system realization theory and is a generalization of our previous work on identification of Hamiltonian parameters.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
IMoFi - Intelligent Model Fidelity: Physics-Based Data-Driven Grid Modeling to Accelerate Accurate PV Integration (Final Report)
This report summarizes the work performed under a project funded by U.S. DOE Solar Energy Technologies Office (SETO) to use grid edge measurements to calibrate distribution system models for improved planning and grid integration of solar PV. Several physics-based data-driven algorithms are developed to identify inaccuracies in models and to bring increased visibility into distribution system planning. This includes phase identification, secondary system topology and parameter estimation, meter-to-transformer pairing, medium-voltage reconfiguration detection, determination of regulator and capacitor settings, PV system detection, PV parameter and setting estimation, PV dynamic models, and improved load modeling. Each of the algorithms is tested using simulation data and demonstrated on real feeders with our utility partners. The final algorithms demonstrate the potential for future planning and operations of the electric power grid to be more automated and data-driven, with more granularity, higher accuracy, and more comprehensive visibility into the system.
14 SOLAR ENERGY↗
IMoFi (Intelligent Model Fidelity): Physics-Based Data-Driven Grid Modeling to Accelerate Accurate PV Integration Updated Accomplishments
This report summarizes the work performed under a project funded by U.S. DOE Solar Energy Technologies Office (SETO), including some updates from the previous report SAND2022-0215, to use grid edge measurements to calibrate distribution system models for improved planning and grid integration of solar PV. Several physics-based data-driven algorithms are developed to identify inaccuracies in models and to bring increased visibility into distribution system planning. This includes phase identification, secondary system topology and parameter estimation, meter-to-transformer pairing, medium-voltage reconfiguration detection, determination of regulator and capacitor settings, PV system detection, PV parameter and setting estimation, PV dynamic models, and improved load modeling. Each of the algorithms is tested using simulation data and demonstrated on real feeders with our utility partners. The final algorithms demonstrate the potential for future planning and operations of the electric power grid to be more automated and data-driven, with more granularity, higher accuracy, and more comprehensive visibility into the system.
14 SOLAR ENERGY↗
Consolidation and Centralization of Waste Operations Business Systems - 12319
This abstract provides a comprehensive plan supporting the continued development and integration of all waste operations and waste management business systems. These include existing systems such as ATMS (Automated Transportation Management System), RadCalc, RFITS (Radio Frequency Identification Transportation System) Programs as well as incorporating key components of existing government developed waste management systems and COTS (Computer Off The Shelf) applications in order to deliver a truly integrated waste tracking and management business system. Some of these existing systems to be integrated include IWTS at Idaho National Lab, WIMS at Sandia National Lab and others. The aggregation of data and consolidation into a single comprehensive business system delivers best practices in lifecycle waste management processes to be delivered across the Department of Energy facilities. This concept exists to reduce operational costs to the federal government by combining key business systems into a centralized enterprise application following the methodology that as contractors change, the tools they use to manage DOE's assets do not. IWITS is one efficient representation of a sound architecture currently supporting multiple DOE sites from a waste management solution. The integration of ATMS, RadCalc and RFITS and the concept like IWITS into a single solution for DOE contractors will result in significant savings and increased efficiencies for DOE. Building continuity and solving collective problems can only be achieved through mass collaboration, resulting in an online community that DOE contractors and subcontractors access common applications, allowing for the collection of business intelligence at an unprecedented level. This is a fundamental shift from a solely 'for profit' business model to a 'for purpose' business model. To the conventional-minded, putting values before profit is an unfamiliar and unnatural way for a contractor to operate - unless however; your objective is to build a strong, strategic alliance across the enterprise in order to execute an unprecedented change in waste management, transportation and logistical operations. The success of such an initiative can be achieved by creating a responsible framework by enabling key individuals to 'own' the sustainability of the program. This includes the strategic collaboration of responsible revolutionaries covering application developers, information owners and federal stakeholders to ensure compliance, security and risk management are 'baked' into the process and sustainability is fostered through continued innovation by both technology and application functionality. This ensures that working software can adapt to changing circumstances and is the principle measure of the success of the program. The consolidation of waste management business systems must be achieved in order to realize efficiencies in information technology portfolio management, data integrity, business intelligence and the lifecycle management of hazardous materials within the DOE enterprise architecture. By identifying best practices across the enterprise and aggregating computational and application development resources, you can provide a unified, holistic solution serviceable from a single location while being accessed from anywhere. The business impact of integrating and delivering a unified solution would reduce costs to the Department of Energy within the first year of deployment with increased savings annually. (author)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗