pwc-archive/papers-with-abstracts huggingface dataset:
Using Quantile Forecasts for Dynamic Equivalents of Active Distribution Grids under Uncertainty
Authors: Johanna Vorwerk, Thierry Zufferey, Petros Aristidou, Gabriela Hug
Date: 2022-07-08 00:00:00

While distribution networks (DNs) turn from consumers to active and responsive intelligent DNs, the question of how to represent them in large-scale transmission network (TN) studies is still under investigation. The standard approach that uses aggregated models for the inverter-interfaced generation and conventional load models introduces significant errors to the dynamic modeling that can lead to instabilities. This paper presents a new approach based on quantile forecasting to represent the uncertainty originating in DNs at the TN level. First, we aquire a required rich dataset employing Monte Carlo simulations of a DN. Then, we use machine learning (ML) algorithms to not only predict the most probable response but also intervals of potential responses with predefined confidence. These quantile methods represent the variance in DN responses at the TN level. The results indicate excellent performance for most ML techniques. The tuned quantile equivalents predict accurate bands for the current at the TN/DN-interface, and tests with unseen TN conditions indicate robustness. A final assessment that compares the MC trajectories against the predicted intervals highlights the potential of the proposed method.
Privacy Preservation by Local Design in Cooperative Networked Control Systems
Authors: Chao Yang, Wen Yang, Hongbo Shi
Date: 2022-07-08 00:00:00

In this paper, we study the privacy preservation problem in a cooperative networked control system working for the task of LQG control. The system consists of a user and a server: the user owns the state process, the server provides computation capability, and the user employs the server to compute control inputs for it. To enable the server's computation, the user needs to provide the measurements of the process states to the server. However, the user regards the states as privacy and wants the server to have deviated knowledge of the state estimates rather than the true value. Regarding that, we propose a novel local design of privacy preservation, which creates a deviation in the server's knowledge of the state estimates from the true value. Meanwhile, we propose an associated privacy metric to measure the deviation and analyze the privacy performance accordingly. Furthermore, we analyze the service loss in the LQG control performance caused by the privacy scheme. Finally, we study the performance trade-off between the privacy preservation and the LQG control, where the proposed optimization problems are solved by numerical methods efficiently.
Path Planning with Uncertainty for Aircraft Under Threat of Detection from Ground-Based Radar
Authors: Austin Costley, Greg Droge, Randall Christensen, Robert C. Leishman, James Swedeen
Date: 2022-07-08 00:00:00
Tasks: Position

Mission planners for aircraft operating under threat of detection by ground-based radar systems are concerned with the probability of detection. Current path planning methods for such scenarios consider the aircraft pose, radar position, and radar parameters to be deterministic and known. This paper presents a framework for incorporating uncertainty in these quantities into a radar detection model that is used by a path planner. The developed path planner evaluates the radar detection risk in the presence of uncertainties and uses linear covariance analysis to efficiently generate error budgets. The error budgets identify the contribution of each source of uncertainty (e.g., sensor measurement noise, radar position uncertainty) to the overall variability in the probability of detection. The framework is applied to a modified visibility graph path planner that uses the detection risk and its variability to calculate path adjustments, which maintain the detection risk below a specified threshold. The results show that the framework is effective at providing actionable information to the mission planner that improves the final planned path and reduces the detection risk.
ATC-Based Scenario Decomposition Algorithm for Optimal Power Flow of Distribution Networks Considering High Photovoltaic Penetration
Authors: Xiemin Mo, Tao Liu, Xue Lyu
Date: 2022-07-08 00:00:00
Tasks: Computational Efficiency

This paper focuses on the analytical target cascading (ATC) based scenario decomposition method which applies to the stochastic OPF problem of distribution networks with high photovoltaic penetration. The original two-stage stochastic OPF model is decomposed into a master problem in the upper level and multiple subproblems in the lower level. This decomposition makes subproblems easier to be solved and can also effectively overcome the curse of dimensionality in the traditional scenario-based model. The global optimal solution can be obtained by only transferring some necessary coupling information between the upper and lower levels. Moreover, all the subproblems in the lower level can be solved in a parallel manner which improves the computational efficiency, in particular, for cases with a larger number of scenarios. Case studies on the IEEE 33-bus system and various larger systems verify the effectiveness and adaptability of the proposed algorithm.
Identification of Intraday False Data Injection Attack on DER Dispatch Signals
Authors: Jip Kim, Siddharth Bhela, James Anderson, Gil Zussman
Date: 2022-07-08 00:00:00
Tasks: regression

The urgent need for the decarbonization of power girds has accelerated the integration of renewable energy. Concurrently the increasing distributed energy resources (DER) and advanced metering infrastructures (AMI) have transformed the power grids into a more sophisticated cyber-physical system with numerous communication devices. While these transitions provide economic and environmental value, they also impose increased risk of cyber attacks and operational challenges. This paper investigates the vulnerability of the power grids with high renewable penetration against an intraday false data injection (FDI) attack on DER dispatch signals and proposes a kernel support vector regression (SVR) based detection model as a countermeasure. The intraday FDI attack scenario and the detection model are demonstrated in a numerical experiment using the HCE 187-bus test system.
Towards Optimal Integrated Planning of Electricity and Hydrogen Infrastructure for Large-Scale Renewable Energy Transport
Authors: Sleiman Mhanna, Isam Saedi, Guanchi Liu, Pierluigi Mancarella
Date: 2022-07-07 00:00:00

The imminent advent of large-scale green hydrogen (H2) production raises the central question of which of the two options, transporting "green" molecules, or transporting "green" electrons, is the most cost-effective one. This paper proposes a first-of-its-kind mathematical framework for the optimal integrated planning of electricity and H2 infrastructure for transporting large-scale variable renewable energy (VRE). In contrast to most existing works, this work incorporates essential nonlinearities such as voltage drops due to losses in high-voltage alternating current (HVAC) and high-voltage direct current (HVDC) transmission lines, losses in HVDC converter stations, reactive power flow, pressure drops in pipelines, and linepack, all of which play an important role in determining the optimal infrastructure investment decision. Capturing these nonlinearities requires casting the problem as a nonconvex mixed-integer nonlinear program (MINLP), whose complexity is further exacerbated by its large size due to the relatively high temporal resolution of RES forecasts. This work then leverages recent advancements in convex relaxations to instead solve a tractable alternative in the form of a mixed-integer quadratically constrained programming (MIQCP) problem. The impact of other fundamental factors such as transmission distance and RES capacity is also thoroughly analysed on a canonical two-node system. The integrated planning model is then demonstrated on a real-world case study involving renewable energy zones in Australia.
Droop-e: Exponential Droop as a Function of Power Output for Grid-Forming Inverters with Autonomous Power Sharing
Authors: Rick Wallace Kenyon, Amirhossein Sajadi, Bri-Mathias Hodge
Date: 2022-07-07 00:00:00

This paper presents the novel Droop-e grid-forming inverter control strategy, which establishes an active power-frequency relationship based on an exponential function of the inverter power dispatch. The advantages of this control strategy include an increased utilization of available headroom, mitigated system frequency dynamics, and a natural limiting behavior, all of which are directly compared to the hitherto standard static droop approach. First, the small signal stability of the Droop-e control is assessed on a 3-bus system and found stable across all possible inverter power dispatches. Then, time-domain simulations show improved frequency dynamics at lower power dispatches, and a limiting behavior at higher dispatches. Finally, a novel secondary control scheme is introduced that achieves power sharing following the primary Droop-e response to load perturbations, which is shown to be effective in time-domain simulations of the 3- and 9-bus systems; comparative simulations with a static 5% droop yields unacceptable frequency deviations, highlighting the superiority of the Droop-e control.
A Learn-and-Control Strategy for Jet-Based Additive Manufacturing
Authors: Uduak Inyang-Udoh, Alvin Chen, Sandipan Mishra
Date: 2022-07-07 00:00:00

In this paper, we develop a predictive geometry control framework for jet-based additive manufacturing (AM) based on a physics-guided recurrent neural network (RNN) model. Because of its physically interpretable architecture, the model's parameters are obtained by training the network through back propagation using input-output data from a small number of layers. Moreover, we demonstrate that the model can be dually expressed such that the layer droplet input pattern for (each layer of) the part to be fabricated now becomes the network parameter to be learned by back-propagation. This approach is applied for feedforward predictive control in which the network parameters are learned offline from previous data and the control input pattern for all layers to be printed is synthesized. Sufficient conditions for the predictive controller's stability are then shown. Furthermore, we design an algorithm for efficiently implementing feedback predictive control in which the network parameters and input patterns (for the receding horizon) are learned online with no added lead time for computation. The feedforward control scheme is shown experimentally to improve the RMS reference tracking error by more than 30% over the state of the art. We also experimentally demonstrate that process uncertainties are compensated by the online learning and feedback control.
Demographic noise in complex ecological communities
Authors: Ferran Larroya, Tobias Galla
Date: 2022-07-08 00:00:00

We introduce an individual-based model of a complex ecological community with random interactions. The model contains a large number of species, each with a finite population of individuals, subject to discrete reproduction and death events. The interaction coefficients determining the rates of these events is chosen from an ensemble of random matrices, and is kept fixed in time. The set-up is such that the model reduces to the known generalised Lotka-Volterra equations with random interaction coefficients in the limit of an infinite population for each species. Demographic noise in the individual-based model means that species which would survive in the Lotka-Volterra model can become extinct. These noise-driven extinctions are the focus of the paper. We find that, for increasing complexity of interactions, ecological communities generally become less prone to extinctions induced by demographic noise. An exception are systems composed entirely of predator-prey pairs. These systems are known to be stable in deterministic Lotka-Volterra models with random interactions, but, as we show, they are nevertheless particularly vulnerable to fluctuations.
Enhanced brain structure-function tethering in transmodal cortex revealed by high-frequency eigenmodes
Authors: Yaqian Yang, Zhiming Zheng, Longzhao Liu, Hongwei Zheng, Yi Zhen, Yi Zheng, Xin Wang, Shaoting Tang
Date: 2022-07-07 00:00:00
Tasks: Functional Connectivity

The brain's structural connectome supports signal propagation between neuronal elements, shaping diverse coactivation patterns that can be captured as functional connectivity. While the link between structure and function remains an ongoing challenge, the prevailing hypothesis is that the structure-function relationship may itself be gradually decoupled along a macroscale functional gradient spanning unimodal to transmodal regions. However, this hypothesis is strongly constrained by the underlying models which may neglect requisite signaling mechanisms. Here, we transform the structural connectome into a set of orthogonal eigenmodes governing frequency-specific diffusion patterns and show that regional structure-function relationships vary markedly under different signaling mechanisms. Specifically, low-frequency eigenmodes, which are considered sufficient to capture the essence of the functional network, contribute little to functional connectivity reconstruction in transmodal regions, resulting in structure-function decoupling along the unimodal-transmodal gradient. In contrast, high-frequency eigenmodes, which are usually on the periphery of attention due to their association with noisy and random dynamical patterns, contribute significantly to functional connectivity prediction in transmodal regions, inducing gradually convergent structure-function relationships from unimodal to transmodal regions. Although the information in high-frequency eigenmodes is weak and scattered, it effectively enhances the structure-function correspondence by 35% in unimodal regions and 56% in transmodal regions. Altogether, our findings suggest that the structure-function divergence in transmodal areas may not be an intrinsic property of brain organization, but can be narrowed through multiplexed and regionally specialized signaling mechanisms.
Large Bayesian VARs with Factor Stochastic Volatility: Identification, Order Invariance and Structural Analysis
Authors: Joshua Chan, Eric Eisenstat, Xuewen Yu
Date: 2022-07-08 00:00:00

Vector autoregressions (VARs) with multivariate stochastic volatility are widely used for structural analysis. Often the structural model identified through economically meaningful restrictions--e.g., sign restrictions--is supposed to be independent of how the dependent variables are ordered. But since the reduced-form model is not order invariant, results from the structural analysis depend on the order of the variables. We consider a VAR based on the factor stochastic volatility that is constructed to be order invariant. We show that the presence of multivariate stochastic volatility allows for statistical identification of the model. We further prove that, with a suitable set of sign restrictions, the corresponding structural model is point-identified. An additional appeal of the proposed approach is that it can easily handle a large number of dependent variables as well as sign restrictions. We demonstrate the methodology through a structural analysis in which we use a 20-variable VAR with sign restrictions to identify 5 structural shocks.
The welfare effects of nonlinear health dynamics
Authors: Chiara Dal Bianco, Andrea Moro
Date: 2022-07-08 00:00:00

We generate a continuous measure of health to estimate a non-parametric model of health dynamics, showing that adverse health shocks are highly persistent when suffered by people in poor health. Canonical models cannot account for this pattern. We incorporate this health dynamic into a life-cycle model of consumption, savings, and labor force participation. After estimating the model parameters, we simulate the effects of health shocks on economic outcomes. We find that bad health shocks have long-term adverse economic effects that are more extreme for those in poor health. Furthermore, bad health shocks also increase the disparity of asset accumulation among this group of people. A canonical model of health dynamics would not reveal these effects.
The Future of Traditional Fuel Vehicles (TFV) and New Energy Vehicles (NEV): Creative Destruction or Co-existence?
Authors: Zhaojia Huang, Liang Zhang, Tianhao Zhi
Date: 2022-07-08 00:00:00

There is a rapid development and commercialization of new Energy Vehicles (NEV) in recent years. Although traditional fuel vehicles (TFV) still occupy a majority share of the market, it is generally believed that NEV is more efficient, more environmental friendly, and has a greater potential of a Schumpeterian "creative destruction" that may lead to a paradigm shift in auto production and consumption. However, less is discussed regarding the potential environmental impact of NEV production and future uncertainty in R&D bottleneck of NEV technology and innovation. This paper aims to propose a modelling framework based on Lux (1995) that investigates the long-term dynamics of TFV and NEV, along with their associated environmental externality. We argue that environmental and technological policies will play a critical role in determining its future development. It is of vital importance to constantly monitor the potential environmental impact of both sectors and support the R&D of critical NEV technology, as well as curbing its negative externality in a preemptive manner.
Spatio-temporal error concealment in video by denoised temporal extrapolation refinement
Authors: Jürgen Seiler, Michael Schöberl, André Kaup
Date: 2022-07-08 00:00:00
Tasks: Denoising

In video communication, the concealment of distortions caused by transmission errors is important for allowing for a pleasant visual quality and for reducing error propagation. In this article, Denoised Temporal Extrapolation Refinement is introduced as a novel spatiotemporal error concealment algorithm. The algorithm operates in two steps. First, temporal error concealment is used for obtaining an initial estimate. Afterwards, a spatial denoising algorithm is used for reducing the imperfectness of the temporal extrapolation. For this, Non-Local Means denoising is used which is extended by a spiral scan processing order and is improved by an adaptation step for taking the preliminary temporal extrapolation into account. In doing so, a spatio-temporal error concealment results. By making use of the refinement, a visually noticeable average gain of 1 dB over pure temporal error concealment is possible. With this, the algorithm also is able to clearly outperform other spatio-temporal error concealment algorithms.
Content-Adaptive Motion Compensated Frequency Selective Extrapolation for Error Concealment in Video Communication
Authors: Jürgen Seiler, André Kaup
Date: 2022-07-08 00:00:00

If digital video data is transmitted over unreliable channels such as the internet or wireless terminals, the risk of severe image distortion due to transmission errors is ubiquitous. To cope with this, error concealment can be applied on the distorted data at the receiver. In this contribution we propose a novel spatio-temporal error concealment algorithm, the Content-Adaptive Motion Compensated Frequency Selective Extrapolation. The algorithm operates in two stages, whereas at first the motion in a distorted sequence is estimated. After that, a model of the signal is generated for concealing the distortion. The novel algorithm is based on an already existent error concealment algorithm. But by adapting the model generation to the content of a sequence, the novel algorithm is able to exploit the remaining information, which is still available in the distorted sequence, more effectively compared to the original algorithm. In doing so, a visually noticeable gain of up to 0.51 dB PSNR compared to the underlying algorithm and more than 3 dB compared to other error concealment algorithms can be achieved.
Spatio-temporal prediction in video coding by spatially refined motion compensation
Authors: Jürgen Seiler, André Kaup
Date: 2022-07-08 00:00:00
Tasks: Decoder, Motion Compensation, Prediction

The purpose of this contribution is to introduce a new method of signal prediction in video coding. Unlike most existent prediction methods that either use temporal or use spatial correlations to generate the prediction signal, the proposed method uses spatial and temporal correlations at the same time. The spatio-temporal prediction is obtained by first performing motion compensation for a macroblock, followed by a refinement step that pays attention to the correlations between the macroblock and its surroundings. At the decoder, the refinement step can be performed in the same manner, thus no additional side information has to be transmitted. Implementation of the spatial refinement step into the H.264/AVC video codec leads to reduction in data rate of up to nearly 15% and increase in PSNR of up to 0.75 dB, compared to pure motion compensated prediction.
Innovative Cognitive Approaches for Joint Radar Clutter Classification and Multiple Target Detection in Heterogeneous Environments
Authors: Linjie Yan, Sudan Han, Chengpeng Hao, Danilo Orlando, Giuseppe Ricci
Date: 2022-07-08 00:00:00

The joint adaptive detection of multiple point-like targets in scenarios characterized by different clutter types is still an open problem in the radar community. In this paper, we provide a solution to this problem by devising detection architectures capable of classifying the range bins according to their clutter properties and detecting possible multiple targets whose positions and number are unknown. Remarkably, the information provided by the proposed architectures makes the system aware of the surrounding environment and can be exploited to enhance the entire detection and estimation performance of the system. At the design stage, we assume three different signal models and apply the latent variable model in conjunction with estimation procedures based upon the expectation-maximization algorithm. In addition, for some models, the maximization step cannot be computed in closed-form (at least to the best of authors' knowledge) and, hence, suitable approximations are pursued, whereas, in other cases, the maximization is exact. The performance of the proposed architectures is assessed over synthetic data and shows that they can be effective in heterogeneous scenarios providing an initial snapshot of the radar operating scenario.
Mobile MIMO Channel Prediction with ODE-RNN: a Physics-Inspired Adaptive Approach
Authors: Zhuoran Xiao, Zhaoyang Zhang, Zirui Chen, Zhaohui Yang, Richeng Jin
Date: 2022-07-08 00:00:00
Tasks: Prediction

Obtaining accurate channel state information (CSI) is crucial and challenging for multiple-input multiple-output (MIMO) wireless communication systems. Conventional channel estimation method cannot guarantee the accuracy of mobile CSI while requires high signaling overhead. Through exploring the intrinsic correlation among a set of historical CSI instances randomly obtained in a certain communication environment, channel prediction can significantly increase CSI accuracy and save signaling overhead. In this paper, we propose a novel channel prediction method based on ordinary differential equation (ODE)-recurrent neural network (RNN) for accurate and flexible mobile MIMO channel prediction. Differing from existing works using sequential network structures for exploring the numerical correlation between observed data, our proposed method tries to represent the implicit physics process of path responses changing by specially designed continuous learning network with ODE structure. Due to the targeted design of learning network, our proposed method fits the mathematics feature of CSI data better and enjoy higher network interpretability. Experimental results show that the proposed learning approach outperforms existing methods, especially for long time interval of the CSI sequence and large channel measurement error.