pwc-archive/papers-with-abstracts huggingface dataset:
Data-driven Topology and Parameter Identification in Distribution Systems with limited Measurements
Authors: Steven de Jongh, Felicitas Mueller, Fabian Osterberg, Claudio A. Cañizares, Thomas Leibfried, Kankar Bhattacharya
Date: 2023-08-18 00:00:00
Tasks: Problem Decomposition, Time Series

This manuscript presents novel techniques for identifying the switch states, phase identification, and estimation of equipment parameters in multi-phase low voltage electrical grids, which is a major challenge in long-standing German low voltage grids that lack observability and are heavily impacted by modelling errors. The proposed methods are tailored for systems with a limited number of spatially distributed measuring devices, which measure voltage magnitudes at specific nodes and some line current magnitudes. The overall approach employs a problem decomposition strategy to divide the problem into smaller subproblems, which are addressed independently. The techniques for identifying switch states and system phases are based on heuristics and a binary optimization problem using correlation analysis of the measured time series. The estimation of equipment parameters is achieved through a data-driven regression approach and by an optimization problem, and the identification of cable types is solved using a Mixed-Integer Quadratic Programming solver. To validate the presented methods, a realistic grid is used and the presented techniques are evaluated for their resilience to data quality and time resolution, discussing the limitations of the proposed methods.
Towards Trustworthy NFC-based Sensor Readout for Battery Packs in Battery Management Systems
Authors: Fikret Basic, Martin Gaertner, Christian Steger
Date: 2023-08-18 00:00:00
Tasks: Management

In the last several years, wireless Battery Management Systems (BMS) have slowly become a topic of interest from both academia and industry. It came from a necessity derived from the increased production and use in different systems, including the electrical vehicles. Wireless communication allows for a more flexible and cost-efficient sensor installation in battery packs. However, many wireless technologies, such as those that use the 2.4 GHz frequency band, suffer from the interference limitations that need to be addressed. In this paper we present an alternative approach to communication in BMS that relies on the use of Near Field Communication (NFC) technology for battery sensor readouts. Due to a vital concern over the counterfeited battery pack products, security measures are also considered. To this end, we propose the use of an effective, and easy to integrate, authentication schema that is supported by the dedicated NFC devices. To test the usability of our design, a demonstrator using the targeted devices was implemented and evaluated.
Optimal Closed Loop Control of G2V/V2G Action Using Model Predictive Controller
Authors: Satya Vikram Pratap Singh, Siddharth Kamila, Prashanth Agnihotri
Date: 2023-08-17 00:00:00
Tasks: Model Predictive Control

This paper has developed a closed-loop control algorithm to operate the G2V/V2G action, tested under varying battery voltage conditions and load and source power differences. Under V2G action, to maintain total harmonic distortion under minimum level and grid frequency under the standard limit, a Model predictive controller (MPC) has been used to control the gate driver circuit of the inverter. The state space model of the plant has been created using the system identification toolbox, and the MPC Controller block has been designed using the Model Predictive Control Toolbox of MATLAB. The proposed methodology is tested using MATLAB/Simulink and OPAL-RT (OP4510) in a real-time environment. This methodology reduces %THD to less than 0.5%, improves waveform quality of grid voltage, inverter output voltage, grid current, and inverter output current to nearly 99%, and maintains the grid frequency in standard limit while in G2V/V2G action.
Fault Detection and Classification using Wavelet and ANN in DFIG and TCSC Connected Transmission Line
Authors: Satya Vikram Pratap Singh, Tanu Prasad, Siddharth Kamila, Prashant Agnihotri
Date: 2023-08-17 00:00:00
Tasks: Fault Detection

This paper presents fault detection and classification using Wavelet and ANN based methods in a DFIG-based series compensated system. The state-of-the art methods include Wavelet transform, Fourier transform, and Wavelet-neuro fuzzy methods-based system for fault detection and classification. However, the accuracy of these state-of-the-art methods diminishes during variable conditions such as changes in wind speed, high impedance faults, and the changes in the series compensation level. Specifically, in Wavelet transform based methods, the threshold values need to be adapted based on the variable field conditions. To solve this problem, this paper has proposed a Wavelet-ANN based fault detection method where Wavelet is used as an identifier and ANN is used as a classifier for detecting various fault cases. This methodology is also effective under SSR condition. The proposed methodology is evaluated on various fault and non-fault cases generated on an IEEE first benchmark model under varying compensation levels from 20% to 55%, impedance faults, and wind velocity from 6m/sec to 10m/sec using MATLAB/Simulink, OPALRT(OP4510) manufactured real-time digital simulator environment, Arduino board I/O ports communicating with external PC in which ANN model dumped, using Arduino support package of MATLAB. The preliminary results are compared with the state-of-the-art fault detection method, where the proposed method shows robust performance under varying field conditions.
Enhancing accuracy of finite-dimensional models for lithium-ion batteries, observer design and experimental validation
Authors: Mira Khalil, Romain Postoyan, Stéphane Raël
Date: 2023-08-17 00:00:00
Tasks: Management

Accurate estimation of the internal states of lithium-ion batteries is key towards improving their management for safety, efficiency and longevity purposes. Various approaches exist in the literature in this context, among which designing an observer based on an electrochemical model of the battery dynamics. With this approach, the performance of the observer depends on the accuracy of the considered model. It appears that electrochemical models, and thus their associated observer, typically require to be of high dimension to generate accurate internal variables. In this work, we present a method to mitigate this limitation by correcting the lithium concentrations generated by a general class of finite-dimensional electrochemical models such that they asymptotically match those generated by the original partial differential equations (PDE) they are based on, for constant input currents. These corrections apply irrespectively of the order of the considered finite-dimensional model. The proposed correction leads to a new state space model for which we design observers, whose global, robust convergences are supported by a Lyapunov analysis. Both numerical and experimental validations are presented, which show the improvement of the accuracy of the state estimates as a result of the proposed corrections.
Remote State Estimation with Privacy Against Active Eavesdroppers
Authors: Matthew Crimson, Justin M. Kennedy, Daniel E. Quevedo
Date: 2023-08-17 00:00:00
Tasks: State Estimation

This paper considers a cyber-physical system under an active eavesdropping attack. A remote legitimate user estimates the state of a linear plant from the state information received from a sensor. Transmissions from the sensor occur via an insecure and unreliable network. An active eavesdropper may perform an attack during system operation. The eavesdropper intercepts transmissions from the sensor, whilst simultaneously sabotaging the data transfer from the sensor to the remote legitimate user to harm its estimation performance. To maintain state confidentiality, we propose an encoding scheme that is activated on the detection of an eavesdropper. Our scheme transmits noise based on a pseudo-random indicator, pre-arranged at the legitimate user and sensor. The transmission of noise harms the eavesdropper's performance, more than that of the legitimate user. Using the proposed encoding scheme, we impair the eavesdropper's expected estimation performance, whilst minimising expected performance degradation at the legitimate user. We explore the trade-off between state confidentiality and legitimate user performance degradation through selecting the probability that the sensor transmits noise. Under certain design choices, the trace of the expected estimation error covariance of the eavesdropper is greater than that of the legitimate user. Numerical examples are provided to illustrate the proposed encoding scheme.
Maximum Correntropy Ensemble Kalman Filter
Authors: Yangtianze Tao, Jiayi Kang, Stephen Shing-Toung Yau
Date: 2023-08-17 00:00:00

In this article, a robust ensemble Kalman filter (EnKF) called MC-EnKF is proposed for nonlinear state-space model to deal with filtering problems with non-Gaussian observation noises. Our MC-EnKF is derived based on maximum correntropy criterion (MCC) with some technical approximations. Moreover, we propose an effective adaptive strategy for kernel bandwidth selection.Besides, the relations between the common EnKF and MC-EnKF are given, i.e., MC-EnKF will converge to the common EnKF when the kernel bandwidth tends to infinity. This justification provides a complementary understanding of the kernel bandwidth selection for MC-EnKF. In experiments, non-Gaussian observation noises significantly reduce the performance of the common EnKF for both linear and nonlinear systems, whereas our proposed MC-EnKF with a suitable kernel bandwidth maintains its good performance at only a marginal increase in computing cost, demonstrating its robustness and efficiency to non-Gaussian observation noises.
Control of Cross-Directional Systems using the Generalised Singular Value Decomposition
Authors: Idris Kempf, Paul J. Goulart, Stephen Duncan
Date: 2023-08-16 00:00:00

Diamond Light Source produces synchrotron radiation by accelerating electrons to relativistic speeds. In order to maximise the intensity of the radiation, vibrations of the electron beam are attenuated by a multi-input multi-output (MIMO) control system actuating hundreds of magnets at kilohertz rates. For future accelerator configurations, in which two separate arrays of magnets with different bandwidths are used in combination, standard accelerator control design methods based on the singular value decomposition (SVD) of the system gain matrix are not suitable. We therefore propose to use the generalised singular value decomposition (GSVD) to decouple a two-array cross-directional (CD) system into sets of two-input single-output (TISO) and single-input single-output (SISO) systems. We demonstrate that the two-array decomposition is linked to a single-array system, which is used to accommodate ill-conditioned systems and compensate for the non-orthogonality of the GSVD. The GSVD-based design is implemented and validated through real-world experiments at Diamond. Our approach provides a natural extension of single-array methods and has potential application in other CD systems, including paper making, steel rolling or battery manufacturing processes.
A hierarchy index for networks in the brain reveals a complex entangled organizational structure
Authors: Anand Pathak, Shakti N. Menon, Sitabhra Sinha
Date: 2023-08-17 00:00:00

Networks involved in information processing often have their nodes arranged hierarchically, with the majority of connections occurring in adjacent levels. However, despite being an intuitively appealing concept, the hierarchical organization of large networks, such as those in the brain, are difficult to identify, especially in absence of additional information beyond that provided by the connectome. In this paper, we propose a framework to uncover the hierarchical structure of a given network, that identifies the nodes occupying each level as well as the sequential order of the levels. It involves optimizing a metric that we use to quantify the extent of hierarchy present in a network. Applying this measure to various brain networks, ranging from the nervous system of the nematode Caenorhabditis elegans to the human connectome, we unexpectedly find that they exhibit a common network architectural motif intertwining hierarchy and modularity. This suggests that brain networks may have evolved to simultaneously exploit the functional advantages of these two types of organizations, viz., relatively independent modules performing distributed processing in parallel and a hierarchical structure that allows sequential pooling of these multiple processing streams. An intriguing possibility is that this property we report may be common to information processing networks in general.
Weak Identification with Many Instruments
Authors: Anna Mikusheva, Liyang Sun
Date: 2023-08-18 00:00:00

Linear instrumental variable regressions are widely used to estimate causal effects. Many instruments arise from the use of ``technical'' instruments and more recently from the empirical strategy of ``judge design''. This paper surveys and summarizes ideas from recent literature on estimation and statistical inferences with many instruments for a single endogenous regressor. We discuss how to assess the strength of the instruments and how to conduct weak identification-robust inference under heteroskedasticity. We establish new results for a jack-knifed version of the Lagrange Multiplier (LM) test statistic. Furthermore, we extend the weak-identification-robust tests to settings with both many exogenous regressors and many instruments. We propose a test that properly partials out many exogenous regressors while preserving the re-centering property of the jack-knife. The proposed tests have correct size and good power properties.
Closed-form approximations of moments and densities of continuous-time Markov models
Authors: Dennis Kristensen, Young Jun Lee, Antonio Mele
Date: 2023-08-17 00:00:00
Tasks: Form

This paper develops power series expansions of a general class of moment functions, including transition densities and option prices, of continuous-time Markov processes, including jump--diffusions. The proposed expansions extend the ones in Kristensen and Mele (2011) to cover general Markov processes. We demonstrate that the class of expansions nests the transition density and option price expansions developed in Yang, Chen, and Wan (2019) and Wan and Yang (2021) as special cases, thereby connecting seemingly different ideas in a unified framework. We show how the general expansion can be implemented for fully general jump--diffusion models. We provide a new theory for the validity of the expansions which shows that series expansions are not guaranteed to converge as more terms are added in general. Thus, these methods should be used with caution. At the same time, the numerical studies in this paper demonstrate good performance of the proposed implementation in practice when a small number of terms are included.
Linear Regression with Weak Exogeneity
Authors: Anna Mikusheva, Mikkel Sølvsten
Date: 2023-08-17 00:00:00
Tasks: regression, Time Series

This paper studies linear time series regressions with many regressors. Weak exogeneity is the most used identifying assumption in time series. Weak exogeneity requires the structural error to have zero conditional expectation given the present and past regressor values, allowing errors to correlate with future regressor realizations. We show that weak exogeneity in time series regressions with many controls may produce substantial biases and even render the least squares (OLS) estimator inconsistent. The bias arises in settings with many regressors because the normalized OLS design matrix remains asymptotically random and correlates with the regression error when only weak (but not strict) exogeneity holds. This bias's magnitude increases with the number of regressors and their average autocorrelation. To address this issue, we propose an innovative approach to bias correction that yields a new estimator with improved properties relative to OLS. We establish consistency and conditional asymptotic Gaussianity of this new estimator and provide a method for inference.
Wisdom of the Crowds or Ignorance of the Masses? A data-driven guide to WSB
Authors: Valentina Semenova, Dragos Gorduza, William Wildi, Xiaowen Dong, Stefan Zohren
Date: 2023-08-18 00:00:00

A trite yet fundamental question in economics is: What causes large asset price fluctuations? A tenfold rise in the price of GameStop equity, between the 22nd and 28th of January 2021, demonstrated that herding behaviour among retail investors is an important contributing factor. This paper presents a data-driven guide to the forum that started the hype -- WallStreetBets (WSB). Our initial experiments decompose the forum using a large language topic model and network tools. The topic model describes the evolution of the forum over time and shows the persistence of certain topics (such as the market / S\&P500 discussion), and the sporadic interest in others, such as COVID or crude oil. Network analysis allows us to decompose the landscape of retail investors into clusters based on their posting and discussion habits; several large, correlated asset discussion clusters emerge, surrounded by smaller, niche ones. A second set of experiments assesses the impact that WSB discussions have had on the market. We show that forum activity has a Granger-causal relationship with the returns of several assets, some of which are now commonly classified as `meme stocks', while others have gone under the radar. The paper extracts a set of short-term trade signals from posts and long-term (monthly and weekly) trade signals from forum dynamics, and considers their predictive power at different time horizons. In addition to the analysis, the paper presents the dataset, as well as an interactive dashboard, in order to promote further research.
Econometrics Modelling Approach to Examine the Effect of STEM Policy Changes on Asian Students Enrollment Decision in USA
Authors: Prathamesh Muzumdar, George Kurian, Ganga Prasad Basyal, Apoorva Muley
Date: 2023-08-17 00:00:00
Tasks: Econometrics, Experimental Design

Academic research has shown significant interest in international student mobility, with previous literature primarily focusing on the migration industry from a political and public policy perspective. For many countries, international student mobility plays a crucial role in bolstering their economies through financial gains and attracting skilled immigrants. While previous studies have explored the determinants of mobility and country economic policies, only a few have examined the impact of policy changes on mobility trends. In this study, the researchers investigate the influence of immigration policy changes, particularly the optional practical training (OPT) extension on STEM programs, on Asian students' preference for enrolling in STEM majors at universities. The study utilizes observational data and employs a quasi-experimental design, analysing the information using the difference-in-difference technique. The findings of the research indicate that the implementation of the STEM extension policy in 2008 has a significant effect on Asian students' decisions to enroll in a STEM major. Additionally, the study highlights the noteworthy role of individual factors such as the specific STEM major, terminal degree pursued, and gender in influencing Asian students' enrollment decisions.
Decarbonizing the European energy system in the absence of Russian gas: Hydrogen uptake and carbon capture developments in the power, heat and industry sectors
Authors: Goran Durakovic, Hongyu Zhang, Brage Rugstad Knudsen, Asgeir Tomasgard, Pedro Crespo del Granado
Date: 2023-08-17 00:00:00

Hydrogen and carbon capture and storage are pivotal to decarbonize the European energy system in a broad range of pathway scenarios. Yet, their timely uptake in different sectors and distribution across countries are affected by supply options of renewable and fossil energy sources. Here, we analyze the decarbonization of the European energy system towards 2060, covering the power, heat, and industry sectors, and the change in use of hydrogen and carbon capture and storage in these sectors upon Europe's decoupling from Russian gas. The results indicate that the use of gas is significantly reduced in the power sector, instead being replaced by coal with carbon capture and storage, and with a further expansion of renewable generators. Coal coupled with carbon capture and storage is also used in the steel sector as an intermediary step when Russian gas is neglected, before being fully decarbonized with hydrogen. Hydrogen production mostly relies on natural gas with carbon capture and storage until natural gas is scarce and costly at which time green hydrogen production increases sharply. The disruption of Russian gas imports has significant consequences on the decarbonization pathways for Europe, with local energy sources and carbon capture and storage becoming even more important.
Entrepreneurial Higher Education Education, Knowledge and Wealth Creation
Authors: Rahmat Ullah, Rashid Aftab, Saeed Siyal, Kashif Zaheer
Date: 2023-08-17 00:00:00

This book presents detailed discussion on the role of higher education in terms of serving basic knowledge creation, teaching, and doing applied research for commercialization. The book presents an historical account on how this challenge was addressed earlier in education history, the cases of successful academic commercialization, the marriage between basic and applied science and how universities develop economies of the regions and countries. This book also discusses cultural and social challenges in research commercialization and pathways to break the status quo.
The Inflation Attention Threshold and Inflation Surges
Authors: Oliver Pfäuti
Date: 2023-08-16 00:00:00

At the outbreak of the recent inflation surge, the public's attention to inflation was low but increased quickly once inflation started to rise. In this paper, I quantify when and by how much the public's attention to inflation changes, and derive the macroeconomic implications of these attention changes. I estimate an attention threshold at an inflation rate of about $4%$, and that attention doubles when inflation exceeds this threshold. Adverse supply shocks become more inflationary in times of high attention, and the increase in people's attention to inflation in 2021 accounts for half of the subsequent supply-driven inflation. I develop a model accounting for the attention threshold and show that shocks that are usually short lived lead to a persistent surge in inflation if they induce an increase in people's attention. The attention threshold further lengthens the last mile of disinflation after an inflation surge, and leads to an asymmetry in the dynamics of inflation.
Endowments, patience types, and uniqueness in two-good HARA utility economies
Authors: Andrea Loi, Stefano Matta
Date: 2023-08-18 00:00:00

This paper establishes a link between endowments, patience types, and the parameters of the HARA Bernoulli utility function that ensure equilibrium uniqueness in an economy with two goods and two impatience types with additive separable preferences. We provide sufficient conditions that guarantee uniqueness of equilibrium for any possible value of $\gamma$ in the HARA utility function $\frac{\gamma}{1-\gamma}\left(b+\frac{a}{\gamma}x\right)^{1-\gamma}$. The analysis contributes to the literature on uniqueness in pure exchange economies with two-goods and two agent types and extends the result in [4].