pwc-archive/papers-with-abstracts huggingface dataset:
An Overview of Indian Spoken Language Recognition from Machine Learning Perspective
Authors: Spandan Dey, Md Sahidullah, Goutam Saha
Date: 2022-11-30 00:00:00
Tasks: Language Identification, Spoken language identification

Automatic spoken language identification (LID) is a very important research field in the era of multilingual voice-command-based human-computer interaction (HCI). A front-end LID module helps to improve the performance of many speech-based applications in the multilingual scenario. India is a populous country with diverse cultures and languages. The majority of the Indian population needs to use their respective native languages for verbal interaction with machines. Therefore, the development of efficient Indian spoken language recognition systems is useful for adapting smart technologies in every section of Indian society. The field of Indian LID has started gaining momentum in the last two decades, mainly due to the development of several standard multilingual speech corpora for the Indian languages. Even though significant research progress has already been made in this field, to the best of our knowledge, there are not many attempts to analytically review them collectively. In this work, we have conducted one of the very first attempts to present a comprehensive review of the Indian spoken language recognition research field. In-depth analysis has been presented to emphasize the unique challenges of low-resource and mutual influences for developing LID systems in the Indian contexts. Several essential aspects of the Indian LID research, such as the detailed description of the available speech corpora, the major research contributions, including the earlier attempts based on statistical modeling to the recent approaches based on different neural network architectures, and the future research trends are discussed. This review work will help assess the state of the present Indian LID research by any active researcher or any research enthusiasts from related fields.
Optimal Control Design for Operating a Hybrid PV Plant with Robust Power Reserves for Fast Frequency Regulation Services
Authors: Victor Paduani, Qi Xiao, Bei Xu, David Lubkeman, Ning Lu
Date: 2022-12-07 00:00:00

This paper presents an optimal control strategy for operating a solar hybrid system consisting of solar photovoltaic (PV) and a high-power, low-storage battery energy storage system (BESS). A state-space model of the hybrid PV plant is first derived, based on which an adaptive model predictive controller is designed. The controller's objective is to control the PV and BESS to follow power setpoints sent to the the hybrid system while maintaining desired power reserves and meeting system operational constraints. Furthermore, an extended Kalman filter (EKF) is implemented for estimating the battery SOC, and an error sensitivity is executed to assess its limitations. To validate the proposed strategy, detailed EMT models of the hybrid system are developed so that losses and control limits can be quantified accurately. Day-long simulations are performed in an OPAL-RT real-time simulator using second-by-second actual PV farm data as inputs. Results verify that the proposed method can follow power setpoints while maintaining power reserves in days of high irradiance intermittency even with a small BESS storage.
INTERPRETER -- Tool for non-technical losses detection
Authors: Hans Bludszuweit, Nurseda Yildirim Yurusen, Pablo López Pérez, Diego Martínez-López
Date: 2022-12-07 00:00:00

This article presents a tool for the detection of non-technical losses, which is being developed within the European INTERPRETER project. The tool employs a hybrid method based on feature detection from smart meter data and grid model analysis. This paper focuses on the grid model analysis, where voltage deviations between the grid model (digital twin) and real-world measurements at a low-voltage pilot site have been evaluated. Energy measurements from smart meters represent hourly mean power, while voltage measurements are instantaneous with uneven time intervals. Thus, measurements are not synchronous, which poses a major challenge for grid analysis. The proposed method focuses on daily mean, minimum, and maximum voltage and results show that deviations in daily minimum voltage are the most useful ones. A heatmap is developed, which helps the DSO expert to have a quick overview of all deviations of all meters in a certain time interval (1-day time step). A total of 6 locations have been identified where field inspections will be done.
Hierarchical Finite State Machines for Efficient Optimal Planning in Large-scale Systems
Authors: Elis Stefansson, Karl H. Johansson
Date: 2022-12-07 00:00:00

In this paper, we consider a planning problem for a hierarchical finite state machine (HFSM) and develop an algorithm for efficiently computing optimal plans between any two states. The algorithm consists of an offline and an online step. In the offline step, one computes exit costs for each machine in the HFSM. It needs to be done only once for a given HFSM, and it is shown to have time complexity scaling linearly with the number of machines in the HFSM. In the online step, one computes an optimal plan from an initial state to a goal state, by first reducing the HFSM (using the exit costs), computing an optimal trajectory for the reduced HFSM, and then expand this trajectory to an optimal plan for the original HFSM. The time complexity is near-linearly with the depth of the HFSM. It is argued that HFSMs arise naturally for large-scale control systems, exemplified by an application where a robot moves between houses to complete tasks. We compare our algorithm with Dijkstra's algorithm on HFSMs consisting of up to 2 million states, where our algorithm outperforms the latter, being several orders of magnitude faster.
Online Feedback Optimization of Compressor Stations with Model Adaptation using Gaussian Process Regression
Authors: M. Zagorowska, M. Degner, L. Ortmann, A. Ahmed, S. Bolognani, E. A. del Rio Chanona, M. Mercangoz
Date: 2022-12-07 00:00:00
Tasks: regression

Online Feedback Optimization is a method used to steer the operation of a process plant to its optimal operating point without explicitly solving a nonlinear constrained optimization problem. This is achieved by leveraging a linear plant model and feedback from measurements. However the presence of plant-model mismatch leads to suboptimal results when using this approach. Learning the plant-model mismatch enables Online Feedback Optimization to overcome this shortcoming. In this work we present a novel application of Online Feedback Optimization with online model adaptation using Gaussian Process regression. We demonstrate our approach with a realistic load sharing problem in a compressor station with parametric and structural plant-model mismatch. We assume imperfect knowledge of the compressor maps and design an Online Feedback Optimization controller that minimizes the compressor station power consumption. In the evaluated scenario, imperfect knowledge of the plant leads to a 5\% increase in power consumption compared to the case with perfect knowledge. We demonstrate that Online Feedback Optimization with model adaptation reduces this increase to only 0.8%, closely approximating the case of perfect knowledge of the plant, regardless of the type of mismatch.
Multi-Objective Model-Predictive Control for Dielectric Elastomer Wave Harvesters
Authors: Matthias K. Hoffmann, Lennart Heib, Gianluca Rizzello, Giacomo Moretti, Kathrin Flaßkamp
Date: 2022-12-07 00:00:00
Tasks: Model Predictive Control

This contribution deals with multi-objective model-predictive control (MPC) of a wave energy converter (WEC) device concept, which can harvest energy from sea waves using a dielectric elastomer generator (DEG) power take-off system. We aim to maximise the extracted energy through control while minimising the accumulated damage to the DEG. With reference to system operation in stochastic waves, we first generate ground truth solutions by solving an optimal control problem, and we analyse the MPC performance to determine a prediction horizon that trades off accuracy and efficiency for computation. Fixed weights in the MPC scheme can produce unpredictable costs for variable sea condition, meaning the average rate of cost accumulation can vary vastly. To steer this cost growth, we propose a heuristic to adapt the algorithm by changing the weighting of the cost functions using for fulfilling the long-time goal of accumulating a small enough damage in a fixed time. A simulated case-study is presented in order to evaluate the performance of the proposed MPC framework and the weight-adaptation algorithm. The proposed heuristic proves to be able to limit the amount of accumulated damage while remaining close to (or even improving) the energy yield obtained with a comparable fixed-weight MPC.
A Four-stage Heuristic Algorithm for Solving On-demand Meal Delivery Routing Problem
Authors: Lejun Zhou, Anke Ye, Simon Hu
Date: 2022-12-07 00:00:00

Meal delivery services provided by platforms with integrated delivery systems are becoming increasingly popular. This paper adopts a rolling horizon approach to solve the meal delivery routing problem (MDRP). To improve delivery efficiency in scenarios with high delivery demand, multiple orders are allowed to be combined into one bundle with orders from different restaurants. Following this strategy, an optimization-based four-stage heuristic algorithm is developed to generate an optimal routing plan at each decision point. The algorithm first generates bundles according to orders' spatial and temporal distribution. Secondly, we find feasible bundle pairs. Then, routes for delivering any single bundle or multiple bundles are optimized, respectively. Finally, the routes are assigned to available couriers. In computational experiments using instances from open datasets, the system's performance is evaluated in respect of average click-to-door time and ready-to-pickup time. We demonstrate that this algorithm can effectively process real-time information and assign optimal routes to the couriers. By comparing the proposed method with existing the-state-of-the-art algorithms, the results indicate that our method can generate solutions with higher service quality and shorter distance.
Robo-Chargers: Optimal Operation and Planning of a Robotic Charging System to Alleviate Overstay
Authors: Yi Ju, Teng Zeng, Zaid Allybokus, Scott Moura
Date: 2022-12-07 00:00:00
Tasks: Scheduling

Charging infrastructure availability is a major concern for plug-in electric vehicle users. Nowadays, the limited public chargers are commonly occupied by vehicles which have already been fully charged. Such phenomenon, known as overstay, hinders other vehicles' accessibility to charging resources. In this paper, we analyze a charging facility innovation to tackle the challenge of overstay, leveraging the idea of Robo-chargers - automated chargers that can rotate in a charging station and proactively plug or unplug plug-in electric vehicles. We formalize an operation model for stations incorporating Fixed-chargers and Robo-chargers. Optimal scheduling can be solved with the recognition of the combinatorial nature of vehicle-charger assignments, charging dynamics, and customer waiting behaviors. Then, with operation model nested, we develop a planning model to guide economical investment on both types of chargers so that the total cost of ownership is minimized. In the planning phase, it further considers charging demand variances and service capacity requirements. In this paper, we provide systematic techno-economical methods to evaluate if introducing Robo-chargers is beneficial given a specific application scenario. Comprehensive sensitivity analysis based on real-world data highlights the advantages of Robo-chargers, especially in a scenario where overstay is severe. Validations also suggest the tractability of operation model and robustness of planning results for real-time application under reasonable model mismatches, uncertainties and disturbances.
Determination of Optimal Size and Number of Movable Energy Resources for Distribution System Resilience Enhancement
Authors: Mukesh Gautam, Eliza Hotchkiss, Mohammed Ben-Idris
Date: 2022-12-07 00:00:00

This paper proposes an approach based on graph theory and combinatorial enumeration for sizing of movable energy resources (MERs) to improve the resilience of the electric power supply. The proposed approach determines the size and number of MERs to be deployed in a distribution system to ensure the quickest possible recovery of the distribution system following an extreme event. The proposed approach starts by generating multiple line outage scenarios based on fragility curves of distribution lines. The generated scenarios are reduced using the k-means method. The distribution network is modeled as a graph where distribution network reconfiguration is performed for each reduced line outage scenario. The combinatorial enumeration technique is used to compute all combinations of total MER by size and number. The expected load curtailment (ELC) corresponding to each locational combination of MERs is determined. The minimum ELCs of all combinations of total MER are used to construct a minimum ELC matrix, which is later utilized to determine optimal size and number of MERs. The proposed approach is validated through a case study performed on a 33-node distribution test system.
Real-time rapid leakage estimation for deep space habitats using exponentially-weighted adaptively-refined search
Authors: Mahindra Rautela, Motahareh Mirfarah, Christian Silva, Shirley Dyke, Amin Maghareh, S. Gopalakrishnan
Date: 2022-12-06 00:00:00

The recent accelerated growth in space-related research and development activities makes the near-term need for long-term extraterrestrial habitats evident. Such habitats must operate under continuous disruptive conditions arising from extreme environments like meteoroid impacts, extreme temperature fluctuations, galactic cosmic rays, destructive dust, and seismic events. Loss of air or atmospheric leakage from a habitat poses safety challenges that demand proper attention. Such leakage may arise from micro-meteoroid impacts, crack growth, bolt/rivet loosening, and seal deterioration. In this paper, leakage estimation in deep space habitats is posed as an inverse problem. A forward pressure-based dynamical model is formulated for atmospheric leakage. Experiments are performed on a small-scaled pressure chamber where different leakage scenarios are emulated and corresponding pressure values are measured. An exponentially-weighted adaptively-refined search (EWARS) algorithm is developed and validated for the inverse problem of real-time leakage estimation. It is demonstrated that the proposed methodology can achieve real-time estimation and tracking of constant and variable leaks with accuracy.
Co-optimizing Behind-The-Meter Resources under Net Metering
Authors: Ahmed S. Alahmed, Lang Tong
Date: 2022-12-06 00:00:00

We consider the problem of co-optimizing behind-the-meter (BTM) storage and flexible demands with BTM stochastic renewable generation. Under a generalized net energy metering (NEM) policy-NEM X, we show that the optimal co-optimization policy schedules the flexible demands based on a load priority list that defers less prioritized loads to times when the BTM generation is abundant. This gives rise to the notion of a net-zero zone, which we quantify under different distributed energy resources (DER) compositions. We highlight the special case of inflexible demands that results in a storage policy that minimizes the imports and exports from and to the grid. Comparative statics are provided on the optimal co-optimization policy. Simulations using real residential data show the surplus gains of various customers under different DER compositions.
Design of Linear Passive Mixer-First Receivers for mmWave Digital Beamforming Arrays
Authors: Rawan Al Kubaisy, Sashank Krishnamurthy, Ali Niknejad
Date: 2022-12-06 00:00:00

A 25-40GHz passive mixer-first receiver using a novel architecture for digital beamforming arrays is proposed. The architecture uses a novel technique for impedance matching using the on-resistance of the mixers in the receiver and matching networks. The small switch resistance of the mixers can be matched to the antenna using matching networks. Several matching networks are discussed, including a tunable matching network for wideband applications. The design achieves a noise figure that is lower than 8dB, a conversion gain of 18dB, and an IIP3 of around +4dBm across the frequency range of 25-40GHz. A prototype chip is fabricated in 28nm bulk CMOS process.
Robust Switching Control of DC-DC Boost Converter for EV Charging Stations
Authors: Saif Ahmad, Ryan P. C. de Souza, Pauline Kergus, Zohra Kader, Stephane Caux
Date: 2022-12-06 00:00:00

In this work, the problem of switching control design for DC-DC boost converter is considered, in the case of operation under uncertain equilibrium condition arising due to perturbations in the input and load parameters. Assuming that these uncertain parameters are generated via a known linear exo-system, a parameter estimator is designed to update the equilibrium point for the switching controller in real-time. In order to mitigate the noise amplification problem associated with the designed parameter estimator, the estimation error injection term is filtered via a set of first-order filters to obtain the desired level of noise suppression in the final set of estimates. To demonstrate the efficiency of the developed scheme, a realistic application scenario of a DC charging station for electric vehicles is considered, with photovoltaic array as the source and a battery connected at the load side.
Analytical Uncertainty Propagation for Multi-Period Stochastic Optimal Power Flow
Authors: Rebecca Bauer, Tillmann Mühlpfordt, Nicole Ludwig, Veit Hagenmeyer
Date: 2022-12-06 00:00:00

The increase in renewable energy sources (RESs), like wind or solar power, results in growinguncertainty also in transmission grids. This affects grid stability through fluctuating energy supplyand an increased probability of overloaded lines. One key strategy to cope with this uncertainty isthe use of distributed energy storage systems (ESSs). In order to securely operate power systemscontaining renewables and use storage, optimization models are needed that both handle uncertaintyand apply ESSs. This paper introduces a compact dynamic stochastic chance-constrained DC optimalpower flow (CC-OPF) model, that minimizes generation costs and includes distributed ESSs. AssumingGaussian uncertainty, we use affine policies to obtain a tractable, analytically exact reformulation asa second-order cone problem (SOCP). We test the new model on five different IEEE networks withvarying sizes of 5, 39, 57, 118 and 300 nodes and include complexity analysis. The results showthat the model is computationally efficient and robust with respect to constraint violation risk. Thedistributed energy storage system leads to more stable operation with flattened generation profiles.Storage absorbed RES uncertainty, and reduced generation cost.
Cooperative Guidance Strategy for Active Defense Spacecraft with Imperfect Information via Deep Reinforcement Learning
Authors: Li Zhi, Haizhao Liang, Jinze Wu, Jianying Wang, Yu Zheng
Date: 2022-12-06 00:00:00
Tasks: Deep Reinforcement Learning

In this paper, an adaptive cooperative guidance strategy for the active protection of a target spacecraft trying to evade an interceptor was developed. The target spacecraft performs evasive maneuvers, launching an active defense vehicle to divert the interceptor. Instead of classical strategies, which are based on optimal control or differential game theory, the problem was solved by using the deep reinforcement learning method, and imperfect information was assumed for the interceptor maneuverability. To address the sparse reward problem, a universal reward design method and an increasingly difficult training approach were presented utilizing the shaping technique. Guidance law, reward function, and training approach were demonstrated through the learning process and Monte Carlo simulations. The application of the non-sparse reward function and increasingly difficult training approach accelerated the model convergence, alleviating the overfitting problem. Considering a standard optimal guidance law as a benchmark, the effectiveness, and the advantages, that guarantee the target spacecraft's escape and win rates in a multi-agent game, of the proposed guidance strategy were validated by the simulation results. The trained agent's adaptiveness to the interceptor maneuverability was superior to the optimal guidance law. Moreover, compared to the standard optimal guidance law, the proposed guidance strategy performed better with less prior knowledge.
Control of Grid-Forming VSCs: A Perspective of Adaptive Fast/Slow Internal Voltage Source
Authors: Heng Wu, Xiongfei Wang
Date: 2022-12-06 00:00:00

Grid-forming (GFM) capability requirements are increasingly imposed on grid-connected voltage-source converters (VSCs). Under large grid disturbances, GFM-VSCs need to remain stable while providing GFM services. Yet, such objectives, as pointed out in this paper, inherently lead to conflicting requirements on the dynamics of internal voltage source (IVS) of GFM-VSCs, i.e., the fast IVS dynamics is needed to avoid the loss of synchronism with the grid, whereas the slow IVS dynamics is preferred for maintaining GFM capability. To tackle this challenge, an adaptive fast/slow IVS control is proposed, which switches GFM-VSC between fast and slow IVS dynamics based on system needs. The proposed method enhances the transient stability of GFM-VSC, whilst maximizing its capability of providing GFM service. Further, the approach is robust to different grid strengths and different types of grid disturbances. The experimental results verify the theoretical findings and the effectiveness of the proposed control method.
The Curious Case of Integrator Reach Sets, Part I: Basic Theory
Authors: Shadi Haddad, Abhishek Halder
Date: 2021-02-23 00:00:00
Tasks: Benchmarking

This is the first of a two part paper investigating the geometry of the integrator reach sets, and the applications thereof. In this Part I, assuming box-valued input uncertainties, we establish that this compact convex reach set is semialgebraic, translated zonoid, and not a spectrahedron. We derive the parametric as well as the implicit representation of the boundary of this reach set. We also deduce the closed form formula for the volume and diameter of this set, and discuss their scaling with state dimension and time. We point out that these results may be utilized in benchmarking the performance of the reach set over-approximation algorithms.