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Annual North-American Power Symposium

作者： John Yonce Michael Walters Ganesh K. Venayagamoorthy Holcombe Department of Electrical and Computer Engineering Real-Time Power and Intelligent Systems Laboratory Clemson University Clemson SC USA

Large volumes of distributed energy resources (DERs), such as solar photovoltaic (PV) plants are integrated into the power distribution system due to increased awareness of climate change. These DERs introduce variable and uncertain generation sources due to changing weather conditions. This makes operations and controls challenging and complex. To better understand and manage the dynamic nature of solar PV power plants, digital twins (DTs) will be needed. DTs based on artificial intelligence (AI) methods can be applied to replicate the dynamics of PV plants. This study utilizes a popular paradigm of AI - neural networks to create a variety of data-driven DT (DD-DT) prediction models for a 1 MW solar PV plant located at Clemson University in South Carolina, USA. State-of-the-art internet of things (IoT) based real-time measurements are used to develop the DD-DTs. Typical results for short-term PV power prediction for DTs implemented using multilayer perceptron neural networks (MLPNNs) and Elman recurrent neural networks (ERNNs) are presented in this paper.

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Training Graph Neural Networks on Growing Stochastic Graphs

Training Graph Neural Networks on Growing Stochastic Graphs

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Juan Cerviño Luana Ruiz Alejandro Ribeiro Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia USA Computer Science & Artificial Intelligence Laboratory MIT Cambridge USA

Graph Neural Networks (GNNs) rely on graph convolutions to exploit meaningful patterns in networked data. Based on matrix multiplications, convolutions incur in high computational costs leading to scalability limitations in practice. To overcome these limitations, proposed methods rely on training GNNs in smaller number of nodes, and then transferring the GNN to larger graphs. Even though these methods are able to bound the difference between the output of the GNN with different number of nodes, they do not provide guarantees against the optimal GNN on the very large graph. In this paper, we propose to learn GNNs on very large graphs by leveraging the limit object of a sequence of growing graphs, the graphon. We propose to grow the size of the graph as we train, and we show that our proposed methodology – learning by transference – converges to a neighborhood of a first order stationary point on the graphon data. A numerical experiment validates our proposed approach.

关键词： Training Convolution Scalability Signal processing algorithms Graph neural networks Acoustics Computational efficiency

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Protecting Customer Privacy Through Distributed Energy Resource Anonymization

Protecting Customer Privacy Through Distributed Energy Resou...

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IEEE General Meeting Power& Energy Society

作者： Nicole Henderson Midrar Adham Robert B. Bass Tylor Slay Department of Electrical and Computer Engineering Portland State University Portland OR Distributed Systems Pacific Northwest National Laboratory Richland WA

Due to their stochastic nature, the increase of Renewable Energy Resources as primary sources of energy for power grids creates challenges regarding the reliability and resilience of the system. In order to combat these obstacles, expansion of Distributed Energy Resources (DERs) and their participation in grid services is necessary. Widespread participation requires prioritizing customer privacy and addressing concerns that may arise regarding communication between DERs and Grid Service Providers. Obtaining detailed information about customers’ power consumption can lead to privacy risks that may prevent users from willingly participating in services. Anonymization of individual data is one method of privacy protection that should be explored. This paper discusses the use of the IEEE 2030.5 [1] flow reservation resources to split the operating cycles of DER load profiles into unique phases. The splitting of phases increases anonymization of DERs by making it more difficult to determine the individual characteristics of each device. We discuss the results of applying this form of anonymization to a set of simulated DER load profiles and examine the effectiveness of the anonymization through the use of a linear Support Vector Machine classifier.

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Data-Driven Graph Construction of Power Flow Graphs for Electric Power Transmission Networks

Data-Driven Graph Construction of Power Flow Graphs for Elec...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Benjamin Poole Rajan Ratnakumar Dulip Tharaka Madurasinghe Christian Kümmerle Ganesh Kumar Venayagamoorthy Minwoo Lee Department of Computer Science University of North Carolina Charlotte USA Real-Time Power and Intelligent Systems Laboratory Clemson University USA Department of Electrical Electronic and Computer Engineering University of Pretoria South Africa

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

A comprehensive understanding of the topology of the electric power transmission network (EPTN) is essential for reliable and robust control of power systems. While existing research primarily relies on domain-specific methods, it lacks data-driven approaches that have proven effective in modeling the topology of complex systems. To address this gap, this paper explores the potential of data-driven methods for more accurate and adaptive solutions to uncover the true underlying topology of EPTNs. First, this paper examines Gaussian Graphical Models (GGM) to create an EPTN network graph (i.e., undirected simple graph). Second, to further refine and validate this estimated network graph, a physics-based, domain-specific refinement algorithm is proposed to prune false edges and construct the corresponding electric power flow network graph (i.e., directed multi-graph). The proposed method is tested using a synchrophasor dataset collected from a two-area, four-machine power system simulated on the real-time digital simulator (RTDS) platform. Experimental results show both the network and flow graphs can be reconstructed using various operating conditions and topologies with limited failure cases.

关键词： Robust control Network topology Data models Real-time systems Phasor measurement units Power systems Topology Power system reliability Reliability Load flow

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Efficient displacement convex optimization with particle gradient descent

arXiv

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arXiv 2023年

作者： Daneshmand, Hadi Lee, Jason D. Jin, Chi Laboratory for Information and Decision Systems MIT United States Department of Electrical Engineering and Computer Science Princeton Universit United States

Particle gradient descent, which uses particles to represent a probability measure and performs gradient descent on particles in parallel, is widely used to optimize functions of probability measures. This paper considers particle gradient descent with a finite number of particles and establishes its theoretical guarantees to optimize functions that are displacement convex in measures. Concretely, for Lipschitz displacement convex functions defined on probability over Rd, we prove that O(1/ϵ2) particles and O(d/ϵ4) computations are sufficient to find the ϵ-optimal solutions. We further provide improved complexity bounds for optimizing smooth displacement convex functions. We demonstrate the application of our results for function approximation with specific neural architectures with two-dimensional inputs. © 2023, CC BY-NC-ND.

关键词： Convex optimization

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Skeleton-based Action Recognition Using Two-stream Graph Convolutional Network with Pose Refinement 41

Skeleton-based Action Recognition Using Two-stream Graph Con...

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第41届中国控制会议

作者： Biao Zheng Luefeng Chen Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Department of Electrical and Computer Engineering University of Alberta Tokyo Institute of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the development of science and technology,graph convolutional network has made great progress in improving the accuracy of action ***,there still exists some deficiencies in current ***,the human skeleton point coordinates entering into the network are barely refined,which may cause large ***,the second-order information(the length and direction of bones),which can reflect action characteristics discriminatively,is rarely *** solve the above issues,a two stream graph convolutional network with pose refinement for skeleton based action recognition is ***,we use an adaptive block to to help improve the *** test our method on Kinetics dataset and the experiment show it can get better results than some recent methods,which plays a positive role in future research.

关键词： Graph convolutional network Pose refinement Skeleton based action recognition Adaptive block

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Comparison of Discrete Bipolar Transistors and MOSFETs for High-Speed Switching Application 44

Comparison of Discrete Bipolar Transistors and MOSFETs for H...

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44th International Convention on Information, Communication and Electronic Technology, MIPRO 2021

作者： Bogdanovic, F. Osrecki, Z. Zilak, J. Koricic, M. Suligoj, T. University of Zagreb Micro and Nano Electronics Laboratory Faculty of Electrical Engineering and Computing Department of Electronics Microelectronics Computing and Intelligent Systems Zagreb Croatia

ISBN: (纸本)9789532331011

Switching performance analysis in this paper presents how Horizontal Current Bipolar Transistor (HCBT) performs under switching mode of operation compared to other commercially available bipolar and MOS transistors, focusing mainly on power efficiency. Rudimentary measurement setup is devised, with target frequency spectrum for this analysis up to 100 MHz. Measurements were performed at frequencies of 1 MHz, 10 MHz and 100 MHz. Signal waveforms were measured at the input and output of analysed device. Dissipated power at each component of test circuit was calculated from the measurements to determine efficiency. The results show similar performance among RF bipolar junction transistors analysed in this paper. All bipolar transistors exhibit efficiency around 80% at 100 MHz, whereas the best performing MOSFET has n = 54.31%. The analysed MOSFETs show somewhat better performance comparing to bipolar transistors at lower frequencies, but with larger performance degradation with the frequency increase. This analysis shows potential of HCBT to be used in switching part of Envelope Tracking (ET), hybrid power supply modulation, pulse width modulation (PWM) or multilevel power supply switching to obtain higher efficiencies. Most of the efficiency reduction is caused by nonideal input signal. © 2021 Croatian Society MIPRO.

关键词： Efficiency

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Evaluation of Horizon of Viability Optimization Engine for Sustained Power to Critical Infrastructure

Evaluation of Horizon of Viability Optimization Engine for S...

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2022 IEEE Power and Energy Conference at Illinois, PECI 2022

作者： Khatana, Vivek Chakraborty, Soham Saraswat, Govind Lundstorm, Blake Salapaka, Murti V. University of Minnesota Department of Electrical and Computer Engineering United States Power Systems Engineering Center National Renewable Energy Laboratory GoldenCO80401 United States Enphase Energy AustinTX78758 United States

ISBN: (纸本)9781665402293

In the aftermath of increasingly frequent catastrophic events, a typical scenario is critical infrastructure (CI) units being supported by available backup sources with a weak power grid that can be intermittent or absent. Such a scenario is significantly challenging in the sense of providing reliable supply of power to CI units. In this article, an intelligent optimization scheme, termed a horizon of viability (HoV) engine, is developed to guarantee the viability of a sustained, reliable supply of power to the CI units over a time horizon. The proposed HoV engine generates a cost-optimal portfolio of the locally available generation sources and the loads over a time horizon using a mixed-integer convex programming problem. A controller-hardware-in-the-loop (CHIL) platform is developed to evaluate the control performance of the HoV engine. The experimental results corroborate the efficacy in maintaining the viability of the CI units after a grid interruption event. Further, the proposed HoV optimization scheme performs better than existing net-load management schemes in the literature. © 2022 IEEE.

关键词： Critical infrastructures

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A web-based Building Automation and Control Service 13

A web-based Building Automation and Control Service

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13th International Conference on Information, Intelligence, systems and Applications, IISA 2022

作者： Sarmas, Elissaios Dimitropoulos, Nikos Strompolas, Sofoklis Mylona, Zoi Marinakis, Vangelis Giannadakis, Athanasios Romaios, Alexandros Doukas, Haris National Technical University of Athens Decision Support Systems Laboratory School of Electrical and Computer Engineering Athens Greece Holistic Ike Athens Greece University of Peloponnese Mechanical Engineering Department Patra Greece Aeronautics University of Patras Department of Mechanical Engineering Patra Greece

ISBN: (数字)9781665463904

ISBN: (纸本)9781665463904

The reduction of the environmental impact in the building sector is necessary in achieving global sustainability. In this context, Building Automation and Control systems provide the opportunity for efficient monitoring and control facilities' subsystems, such as the heating and cooling system, the ventilation system, the hot water system, the lighting appliances among others, with the goal of improving thermal comfort as well as energy efficiency. This paper presents a Building Automation and Control system aiming at facilitating data-driven monitoring of complex, multi-storey facilities, by disagreggating total consumption of the different floors and rooms of the building and offering advanced insights and benchmarking indicators. The service is showcased with a use case application on a real building, where the benefits of the service for the energy manager are highlighted. © 2022 IEEE.

关键词： Energy efficiency

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Condition Monitoring with Incomplete Data: An Integrated Variational Autoencoder and Distance Metric Framework

Condition Monitoring with Incomplete Data: An Integrated Var...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Maryam Ahang Mostafa Abbasi Todd Charter Homayoun Najjaran Department of Electrical and Computer Engineering University of Victoria Victoria Canada Advanced Control and Intelligent Systems Laboratory (ACIS) University of Victoria Victoria Canada Department of Mechanical Engineering University of Victoria Victoria Canada

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Condition monitoring of industrial systems is crucial for ensuring safety and maintenance planning, yet notable challenges arise in real-world settings due to the limited or non-existent availability of fault samples. This paper introduces an innovative solution to this problem by proposing a new method for fault detection and condition monitoring for unseen data. Adopting an approach inspired by zero-shot learning, our method can identify faults and assign a relative health index to various operational conditions. Typically, we have plenty of data on normal operations, some data on compromised conditions, and very few (if any) samples of severe faults. We use a variational autoencoder to capture the probabilistic distribution of previously seen and new unseen conditions. The health status is determined by comparing each sample’s deviation from a normal operation reference distribution in the latent space. Faults are detected by establishing a threshold for the health indexes, allowing the model to identify severe, unseen faults with high accuracy, even amidst noise. We validate our approach using the run-to-failure IMS-bearing dataset and compare it with other methods. The health indexes generated by our model closely match the established descriptive model of bearing wear, attesting to the robustness and reliability of our method. These findings highlight the potential of our methodology in augmenting fault detection capabilities within industrial domains, thereby contributing to heightened safety protocols and optimized maintenance practices.

关键词： Condition monitoring Fault diagnosis Protocols Fault detection Zero shot learning Probabilistic logic Robustness Safety Maintenance Indexes

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