pwc-archive/papers-with-abstracts huggingface dataset:
A Nearly Optimal Chattering Reduction Method of Sliding Mode Control With an Application to a Two-wheeled Mobile Robot
Authors: Lei Guo, Han Zhao, Yuan Song
Date: 2021-10-25 00:00:00

The problem we focus on in this paper is to find a nearly optimal sliding mode controller of continuous-time nonlinear multiple-input multiple-output (MIMO) systems that can both reduce chattering and minimize the cost function, which is a measure of the performance index of dynamics systems. First, the deficiency of chattering in traditional SMC and the quasi-SMC method are analyzed in this paper. In quasi-SMC, the signum function of the traditional SMC is replaced with a continuous saturation function. Then, a chattering reduction algorithm based on integral reinforcement learning (IRL) is proposed. Under an initial sliding mode controller, the proposed method can learn the nearly optimal saturation function using policy iteration. To satisfy the requirement of the learned saturation function, we treat the problem of training the saturation function as the constraint of an optimization problem. The online neural network implementation of the proposed algorithm is presented based on symmetric radius basis functions and a regularized batch least-squares (BLS) algorithm to train the control law in this paper. Finally, two examples are simulated to verify the effectiveness of the proposed method. The second example is an application to a real-world dynamics model -- a two-wheeled variable structure robot.
Transient Synchronization Stability Analysis of Wind Farms with MMC-HVDC Integration Under Offshore AC Grid Fault
Authors: Yu Zhang, Chen Zhang, Renxin Yang, Jing Lyu, Li Liu, Xu Cai
Date: 2021-10-25 00:00:00

The MMC-HVDC connected offshore wind farms (OWFs) could suffer short circuit fault (SCF), whereas their transient stability is not well analysed. In this paper, the mechanism of the loss of synchronization (LOS) of this system is analysed considering the whole system state from the fault-on to the post-fault, and the discussion on fault type and fault clearance is addressed as well. A stability index is proposed to quantify the transient synchronization stability (TSS) of the system, which is capable to not only estimate whether the wind turbine generators (WTGs) be able to get resynchronized with the offshore MMC after the fault is cleared, but also to evaluate the performance of stability improving methods as well. Finally, a scenario of six cases is tested on the PSCAD/EMTDC simulation platform, where the performances of four existing stability improving methods are thoroughly compared via both numerical simulation and the proposed stability index.
Sampling-Based Robust Control of Autonomous Systems with Non-Gaussian Noise
Authors: Thom S. Badings, Alessandro Abate, Nils Jansen, David Parker, Hasan A. Poonawala, Marielle Stoelinga
Date: 2021-10-25 00:00:00

Controllers for autonomous systems that operate in safety-critical settings must account for stochastic disturbances. Such disturbances are often modelled as process noise, and common assumptions are that the underlying distributions are known and/or Gaussian. In practice, however, these assumptions may be unrealistic and can lead to poor approximations of the true noise distribution. We present a novel planning method that does not rely on any explicit representation of the noise distributions. In particular, we address the problem of computing a controller that provides probabilistic guarantees on safely reaching a target. First, we abstract the continuous system into a discrete-state model that captures noise by probabilistic transitions between states. As a key contribution, we adapt tools from the scenario approach to compute probably approximately correct (PAC) bounds on these transition probabilities, based on a finite number of samples of the noise. We capture these bounds in the transition probability intervals of a so-called interval Markov decision process (iMDP). This iMDP is robust against uncertainty in the transition probabilities, and the tightness of the probability intervals can be controlled through the number of samples. We use state-of-the-art verification techniques to provide guarantees on the iMDP, and compute a controller for which these guarantees carry over to the autonomous system. Realistic benchmarks show the practical applicability of our method, even when the iMDP has millions of states or transitions.
Improving Online Railway Deadlock Detection using a Partial Order Reduction
Authors: Bjørnar Luteberget
Date: 2021-10-25 00:00:00

Although railway dispatching on large national networks is gradually becoming more computerized, there are still major obstacles to retrofitting (semi-)autonomous control systems. In addition to requiring extensive and detailed digitalization of infrastructure models and information systems, exact optimization for railway dispatching is computationally hard. Heuristic algorithms and manual overrides are likely to be required for semi-autonomous railway operations for the foreseeable future. In this context, being able to detect problems such as deadlocks can be a valuable part of a runtime verification system. If bound-for-deadlock situations are correctly recognized as early as possible, human operators will have more time to better plan for recovery operations. Deadlock detection may also be useful for verification in a feedback loop with a heuristic or semi-autonomous dispatching algorithm if the dispatching algorithm cannot itself guarantee a deadlock-free plan. We describe a SAT-based planning algorithm for online detection of bound-for-deadlock situations. The algorithm exploits parallel updates of train positions and a partial order reduction technique to significantly reduce the number of state transitions (and correspondingly, the sizes of the formulas) in the SAT instances needed to prove whether a deadlock situation is bound to happen in the future. Implementation source code and benchmark instances are supplied, and a direct comparison against another recent study demonstrates significant performance gains.
Quantitative Analysis of Demand Response Using Thermostatically Controlled Loads
Authors: Praveen Dhanasekar, Cunzhi Zhao, Xingpeng Li
Date: 2021-10-24 00:00:00
Tasks: Scheduling

The flexible power consumption feature of thermostatically controlled loads (TCLs) such as heating, ventilation, and air-conditioning (HVAC) systems makes them attractive targets for demand response (DR). TCLs possess a brief period where their power utilization can be altered without any significant impact on customer comfort level. This indicates TCLs are hidden potentials for providing ancillary services. This paper proposes a novel metric of demand response support time (DRST) for HVAC enabled demand response and a novel algorithm for the quantification of such HVAC-DR. The consumers' comfort will not be compromised with the proposed DRST-based HVAC-DR. Case studies demonstrate its benefits in terms of cost saving in microgrid day-ahead scheduling and reduction of forced load shedding during a grid-microgrid tie-line outage event. This illustrates the reserve potential benefits and the increase of microgrid reliability when DRST-based HVAC-DR is considered.
Data-driven estimation of system norms via impulse response
Authors: L. V. Fiorio, C. L. Remes, L. Campestrini, Y. R. de Novaes
Date: 2021-10-23 00:00:00

This paper proposes a method for estimating the norms of a system in a pure data-driven fashion based on their identified Impulse Response (IR) coefficients. The calculation of norms is briefly reviewed and the main expressions for the IR-based estimations are presented. As a case study, the $\mathcal{H}_{1}$, $\mathcal{H}_2$, and $\mathcal{H}_{\infty}$ norms of the sensitivity transfer function of five different discrete-time closed-loop systems are estimated for a Signal-to-Noise-Ratio (SNR) of 10 dB, achieving low percent error values if compared to the real value. To verify the influence of the noise amplitude, norms are estimated considering a wide range of SNR values, for a specific system, presenting low Mean Percent Error (MPE) if compared to the real norms. The proposed technique is also compared to an existing state-space-based method in terms of $\mathcal{H}_{\infty}$, through Monte Carlo, showing a reduction of approximately 48 % in the MPE for a wide range of SNR values.
pystorms: A simulation sandbox for the development and evaluation of stormwater control algorithms
Authors: Sara P. Rimer, Abhiram Mullapudi, Sara C. Troutman, Gregory Ewing, Benjamin D. Bowes, Aaron A. Akin, Jeffrey Sadler, Ruben Kertesz, Bryant McDonnell, Luis Montestruque, Jon Hathaway, Jonathan L. Goodall, Branko Kerkez
Date: 2021-10-23 00:00:00

Recent accessibility of affordable sensing technologies, microcontrollers, and wireless communication technology has made it possible for stormwater systems to be retrofitted with an assortment of sensors and actuators. These smart stormwater systems have enabled the real-time sensing of their surrounding environmental dynamics, and subsequently, provide the basis for autonomous and adaptive operational control strategies. Additionally, these systems allow for inexpensive and minimally-invasive stormwater control interventions (e.g. hydraulic valve operated by cellularly-connected actuator) in lieu of new construction. However promising this area of smart stormwater control, there still remain barriers -- for experts and novices alike -- to access a set of shared tools and methods for investigating, developing, and contributing to it. In an effort to make smart stormwater control research more methodical, efficient, and accessible, we present pystorms, a Python-based simulation sandbox that facilitates the quantitative evaluation and comparison of control strategies. The pystorms simulation sandbox comes with (i) a collection of real world-inspired stormwater control scenarios on which any number of control strategies can be applied and tested via (ii) an accompanying Python programming interface coupled with a stormwater simulator. For the first time, pystorms enables rigorous and efficient evaluation of smart stormwater control methodologies across a diverse set of watersheds with only a few lines of code. We present the details of pystorms here and demonstrate its usage by applying and evaluating two stormwater control strategies.
A Comprehensive Electric Vehicle Model for Vehicle-to-Grid Strategy Development
Authors: Fabian Rücker, Ilka Schoeneberger, Till Wilmschen, Ahmed Chahbaz, Philipp Dechent, Felix Hildenbrand, Elias Barbers, Matthias Kuipers, Jan Figgener, Dirk Uwe Sauer
Date: 2021-10-23 00:00:00

An electric vehicle model is developed to characterize the behavior of the Smart e.d. (2013) while driving, charging and providing vehicle-to-grid services. The battery model is an electro-thermal model with a dual polarization equivalent circuit electrical model coupled with a lumped thermal model with active liquid cooling. The aging trend of the EV's 50 Ah large format pouch cell with NMC chemistry is evaluated via accelerated aging tests in the laboratory. The EV model is completed with the measurement of the on-board charger efficiency and the charging control behavior via IEC 61851-1. Performance of the model is validated using laboratory pack tests, charging and driving field data. The RMSE of the cell voltage was between 18.49 mV and 67.17 mV per cell for the validation profiles. Cells stored at 100 % SOC and 40 $^{\circ}C$ reached end-of-life (80 % of initial capacity) after 431 days to 589 days. The end-of-life for a cell cycled with 80 % DOD around an SOC of 50 % is reached after 3634 equivalent full cycles which equates to a driving distance of over 420000 km. The full parameter set of the model is provided to serve as a resource for vehicle-to-grid strategy development.
Newtonian Mechanics Based Transient Stability PART III: Superimposed Machine
Authors: Songyan Wang, Jilai Yu, Aoife Foley, Jingrui Zhang
Date: 2021-10-23 00:00:00

This paper analyzes the mechanisms of the superimposed machine and also its inherit problems in TSA. Based on the global monitoring of the original system trajectory, the transient energy is mistakenly defined as the superimposition of the transient energy of all machines in the system. This "energy superimposition" directly causes the superimposed machine to become a pseudo machine without any equation of motion, and in this way the superimposed machine completely violates all the machine paradigms. The violations bring the two inherit defects in TSA: (i) the stability of the superimposed machine is unable to be characterized precisely, and (ii) the variance of the original system trajectory is unstable to be depicted clearly. The two defects are also reflected in the definitions of the superimposed-machine based transient stability concepts. In particular, the swing and the critical stability of the system are unable to be defined strictly, and the potential energy surface cannot be modeled precisely. Simulation results show that the problems of the pseudo superimposed-machine in TSA.
Family of Integrated Multi-Input Multi-Output DC-DC Power Converters
Authors: Bang Le-Huy Nguyen, Honnyong Cha, Tien-The Nguyen, Heung-Geun Kim
Date: 2021-10-23 00:00:00

This paper explores a family of integrated multiport converters using three-switch which can provide single-input dual-output (SIDO) or dual-input single-output (DISO) with bidirectional power flow between any two ports. The concept can be extended to the n-switch converters to achieve more inputs and/or outputs. The proposed converters can be applied to interfacing sources, loads and storage elements having different voltage levels in applications such as dc nanogrids, electric vehicle, multiport power supplies, distributed generation systems. Various topological configurations of the integrated multiport n-switch converter are investigated. The operating principles and PWM control strategy of these converters are analyzed in detail. A universalized hardware prototype is built, experimental results are provided for verification.
Advanced Load Shedding for Integrated Power and Energy Systems
Authors: Bang L. H. Nguyen, Tuyen Vu, Colin Ogilvie, Harsha Ravindra, Mark Stanovich, Karl Schoder, Michael Steurer, Charalambos Konstantinou, Herbert Ginn, Christian Schegan
Date: 2021-10-23 00:00:00

This paper introduces an advanced load shedding algorithm to improve the operability performance of a medium voltage direct current (MVDC) integrated shipboard power and energy system. Outcomes are compared to a baseline algorithm while considering power generation contingency scenarios. The case study is conducted with a real-time, embedded algorithm implementation using a control hardware-in-the-loop (CHIL) setup.
Distributed Dynamic State Estimation for Microgrids
Authors: Bang L. H. Nguyen, Tuyen V. Vu, Thomas H. Ortmeyer, Tuan Ngo
Date: 2021-10-23 00:00:00
Tasks: State Estimation

Conventionally, the dynamic state estimation of variables in power networks is performed based on the forecasting-aided model of bus voltages. This approach is effective in the stiff grids at the transmission level, where the bus voltages are less sensitive to variations of the load. However, in microgrids, bus voltages can fluctuate significantly under load changes, the forecasting-aided model may not sufficiently accurate. To resolve this problem, this paper proposes a dynamic state estimation scheme for microgrids using the state-space model derived from differential equations of power networks. In the proposed scheme, the branch currents are the state variables, whereas the bus voltages become the inputs which can vary freely with loads. As a result, the entire microgrids system can be partitioned into local areas, where neighbor areas share the common inputs. The proposed estimation scheme then can be implemented in a distributed manner. A novel Kalman-based filtering method is derived to estimate both states and inputs simultaneously. Only information of common inputs is exchanged between neighboring estimators. Simulation results of the 13-bus Potsdam microgrid (New York State) are provided to prove the feasibility and performances of the proposed scheme.
Three-Layer Joint Distributionally Robust Chance-Constrained Framework for Optimal Day-Ahead Scheduling of E-mobility Ecosystem
Authors: Mahsa Bagheri Tookanlou, S. Ali Pourmousavi, Mousa Marzband
Date: 2021-10-23 00:00:00
Tasks: Scheduling

A high number of electric vehicles (EVs) in the transportation sector necessitates an advanced scheduling framework for e-mobility ecosystem operation as a whole in order to overcome range anxiety and create a viable business model for charging stations (CSs). The framework must account for the stochastic nature of all stakeholders' operations, including EV drivers, CSs, and retailers and their mutual interactions. In this paper, a three-layer joint distributionally robust chance-constrained (DRCC) model is proposed to plan grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operation in day-ahead for e-mobility ecosystems. The proposed stochastic model does not rely on a specific probability distribution for stochastic parameters. To solve the problem, an iterative process is proposed using joint DRCC formulation. To achieve computational traceability, the exact reformulation is implemented for double-sided and single-sided chance constraints (CCs). Furthermore, the impact of temporal correlation of uncertain PV generation on CSs operation is considered. A simulation study is carried out for an ecosystem of three retailers, nine CSs, and 600 EVs based on real data from San Francisco, the USA. The simulation results show the necessity and applicability of such a scheduling method for the e-mobility ecosystem in an uncertain environment, e.g., by reducing the number of unique EVs that failed to reach their destination from 272 to 61.
Joint Task Offloading and Resource Allocation for IoT Edge Computing with Sequential Task Dependency
Authors: Xuming An, Rongfei Fan, Han Hu, Ning Zhang, Saman Atapattu, Theodoros A. Tsiftsis
Date: 2021-10-23 00:00:00
Tasks: Edge-computing

Incorporating mobile edge computing (MEC) in the Internet of Things (IoT) enables resource-limited IoT devices to offload their computation tasks to a nearby edge server. In this paper, we investigate an IoT system assisted by the MEC technique with its computation task subjected to sequential task dependency, which is critical for video stream processing and other intelligent applications. To minimize energy consumption per IoT device while limiting task processing delay, task offloading strategy, communication resource, and computation resource are optimized jointly under both slow and fast fading channels. In slow fading channels, an optimization problem is formulated, which is mixed-integer and non-convex. To solve this challenging problem, we decompose it as a one-dimensional search of task offloading decision problem and a non-convex optimization problem with task offloading decision given. Through mathematical manipulations, the non-convex problem is transformed to be a convex one, which is shown to be solvable only with the simple Golden search method. In fast fading channels, optimal online policy depending on instant channel state is derived. In addition, it is proved that the derived policy will converge to the offline policy when channel coherence time is low, which can help to save extra computation complexity. Numerical results verify the correctness of our analysis and the effectiveness of our proposed strategies over existing methods.
Disturbance Bounds for Signal Temporal Logic Task Satisfaction: A Dynamics Perspective
Authors: Prithvi Akella, Aaron D. Ames
Date: 2021-10-22 00:00:00

This letter offers a novel approach to Test and Evaluation of pre-existing controllers from a control barrier function and dynamics perspective. More aptly, prior Test and Evaluation techniques tend to require apriori knowledge of a space of allowable disturbances. Our work, however, determines a two-norm disturbance-bound rejectable by a system's controller without requiring specific knowledge of these disturbances beforehand. The authors posit that determination of such a disturbance bound offers a better understanding of the robustness with which a given controller achieves a specified task - as motivated through a simple, linear-system example. Additionally, we show that our resulting disturbance bound is accurate through simulation of 1000 randomized trials in which a Segway-controller pair satisfies its specification despite randomized perturbations within our identified bound.
A Reinforcement Learning Approach to Parameter Selection for Distributed Optimal Power Flow
Authors: Sihan Zeng, Alyssa Kody, Youngdae Kim, Kibaek Kim, Daniel K. Molzahn
Date: 2021-10-22 00:00:00
Tasks: Distributed Optimization, Q-Learning, reinforcement-learning, Reinforcement Learning (RL)

With the increasing penetration of distributed energy resources, distributed optimization algorithms have attracted significant attention for power systems applications due to their potential for superior scalability, privacy, and robustness to a single point-of-failure. The Alternating Direction Method of Multipliers (ADMM) is a popular distributed optimization algorithm; however, its convergence performance is highly dependent on the selection of penalty parameters, which are usually chosen heuristically. In this work, we use reinforcement learning (RL) to develop an adaptive penalty parameter selection policy for the AC optimal power flow (ACOPF) problem solved via ADMM with the goal of minimizing the number of iterations until convergence. We train our RL policy using deep Q-learning, and show that this policy can result in significantly accelerated convergence (up to a 59% reduction in the number of iterations compared to existing, curvature-informed penalty parameter selection methods). Furthermore, we show that our RL policy demonstrates promise for generalizability, performing well under unseen loading schemes as well as under unseen losses of lines and generators (up to a 50% reduction in iterations). This work thus provides a proof-of-concept for using RL for parameter selection in ADMM for power systems applications.
Data-driven intrinsic localized mode detection and classification in one-dimensional crystal lattice model
Authors: Jānis Bajārs, Filips Kozirevs
Date: 2021-10-15 00:00:00
Tasks: Classification, Dimensionality Reduction

In this work we propose Support Vector Machine classification algorithms to classify onedimensional crystal lattice waves from locally sampled data. Different learning datasets of particle displacements, momenta and energy density values are considered. Efficiency of the classification algorithms is further improved by two dimensionality reduction techniques: Principal Component Analysis and Locally Linear Embedding. Robustness of classifiers is investigated and demonstrated. Developed algorithms are successfully applied to detect localized intrinsic modes in three numerical simulations considering a case of two localized stationary breather solutions, a single stationary breather solution in noisy background and two mobile breather collision.