The Contact Zone: In Search of the Galician Theater of War in German Cinemas of the 1920s

https://scholarworks.uno.edu/hlw/1006/thumbnail.jp

Accelerating Dynamical System Simulations with Contracting and Physics-Projected Neural-Newton Solvers

Recent advances in deep learning have allowed neural networks (NNs) to successfully replace traditional numerical solvers in many applications, thus enabling impressive computing gains. One such application is time domain simulation, which is indispensable for the design, analysis and operation of many engineering systems. Simulating dynamical systems with implicit Newton-based solvers is a computationally heavy task, as it requires the solution of a parameterized system of differential and algebraic equations at each time step. A variety of NN-based methodologies have been shown to successfully approximate the trajectories computed by numerical solvers at a fraction of the time. However, few previous works have used NNs to model the numerical solver itself. For the express purpose of accelerating time domain simulation speeds, this paper proposes and explores two complementary alternatives for modeling numerical solvers. First, we use a NN to mimic the linear transformation provided by the inverse Jacobian in a single Newton step. Using this procedure, we evaluate and project the exact, physics-based residual error onto the NN mapping, thus leaving physics ``in the loop''. The resulting tool, termed the Physics-pRojected Neural-Newton Solver (PRoNNS), is able to achieve an extremely high degree of numerical accuracy at speeds which were observed to be up to 31% faster than a Newton-based solver. In the second approach, we model the Newton solver at the heart of an implicit Runge-Kutta integrator as a contracting map iteratively seeking a fixed point on a time domain trajectory. The associated recurrent NN simulation tool, termed the Contracting Neural-Newton Solver (CoNNS), is embedded with training constraints (via CVXPY Layers) which guarantee the mapping provided by the NN satisfies the Banach fixed-point theorem

PINNSim: A Simulator for Power System Dynamics based on Physics-Informed Neural Networks

The dynamic behaviour of a power system can be described by a system of differential-algebraic equations. Time-domain simulations are used to simulate the evolution of these dynamics. They often require the use of small time step sizes and therefore become computationally expensive. To accelerate these simulations, we propose a simulator -- PINNSim -- that allows to take significantly larger time steps. It is based on Physics-Informed Neural Networks (PINNs) for the solution of the dynamics of single components in the power system. To resolve their interaction we employ a scalable root-finding algorithm. We demonstrate PINNSim on a 9-bus system and show the increased time step size compared to a trapezoidal integration rule. We discuss key characteristics of PINNSim and important steps for developing PINNSim into a fully fledged simulator. As such, it could offer the opportunity for significantly increasing time step sizes and thereby accelerating time-domain simulations.Comment: submitted to the 23rd Power Systems Computation Conference (PSCC 2024

Learning without Data: Physics-Informed Neural Networks for Fast Time-Domain Simulation

In order to drastically reduce the heavy computational burden associated with time-domain simulations, this paper introduces a Physics-Informed Neural Network (PINN) to directly learn the solutions of power system dynamics. In contrast to the limitations of classical model order reduction approaches, commonly used to accelerate time-domain simulations, PINNs can universally approximate any continuous function with an arbitrary degree of accuracy. One of the novelties of this paper is that we avoid the need for any training data. We achieve this by incorporating the governing differential equations and an implicit Runge-Kutta (RK) integration scheme directly into the training process of the PINN; through this approach, PINNs can predict the trajectory of a dynamical power system at any discrete time step. The resulting Runge-Kutta-based physics-informed neural networks (RK-PINNs) can yield up to 100 times faster evaluations of the dynamics compared to standard time-domain simulations. We demonstrate the methodology on a single-machine infinite bus system governed by the swing equation. We show that RK-PINNs can accurately and quickly predict the solution trajectories.Comment: 6 pages, 6 figures, submitted to IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids 2021(SmartGridComm

Physics-Informed Neural Networks for Non-linear System Identification applied to Power System Dynamics

Varying power-infeed from converter-based generation units introduces great uncertainty on system parameters such as inertia and damping. As a consequence, system operators face increasing challenges in performing dynamic security assessment and taking real-time control actions. Exploiting the widespread deployment of phasor measurement units (PMUs) and aiming at developing a fast dynamic state and parameter estimation tool, this paper investigates the performance of Physics-Informed Neural Networks (PINN) for discovering the frequency dynamics of future power systems and monitoring the system inertia in real-time. PINNs have the potential to address challenges such as the stronger non-linearities of low-inertia systems, increased measurement noise, and limited availability of data. The estimator is demonstrated in several test cases using a 4-bus system, and compared with state of the art algorithms, such as the Unscented Kalman Filter (UKF), to assess its performance.Comment: 6 pages, 8 figures, submitted to 59th Conference on Decision and Contro

ISTmobil: Customer Needs Orientated, Individual Mobility Services in Suburban Areas (Individual Mobility as a Service)

January 2014, challenges concerning urban mobility worldwide and strategies for cities to shape urban mobility better are identified (Arthur D. Little, UITP (2014)). The study indicates that in most cities the potential of urban mobility measures is not achieved and most cities are still badly equipped to cope with the challenges ahead. According to the Arthur D. Little study developements toward individualization and sustainability will require mobility services portfolio extension as well as business model transformation (Arthur D. Little, UITP (2014)). So the developement of urban mobility systems that are able to respond to this challenges is one of the greatest challenges facing cities today. Connection ISTmobil and Urban Mobility Initially the concept of ISTmobil was developed for rural or sub-urban areas. These areas are often characterized by marginal public transport supply. In some cases inhabitants and visitors depend on the private car and have no other choice. One aspect which is linked to this problem is the high rate of motorization within the communities. Looking at the initial situation and the urbanisation trend, which forces people from rural areas to commute in urban areas, there is also a connection to the topic urban mobility. In order to strengthen the use of public transport systems and at the same time mitigating commuting flows of motorised individual transport in the cities the ISTmobil system was developed. Therefore a special offer for commuters, the so called „Pendlerabo“, was developed. The ISTmobil concept The underlying idea behind the concept ISTmobil is the creation of micro public transport systems for rural and suburban areas which are cross-linked to exisiting public transport supply. Moreover, it should be possible to travel in a region or in near future in the whole country with one card and one booking number. A „One-stop shop“ supply of complete mobility services would ease the usage of the mobility services. Another important aspect is to foster cooperations and increase coordination of the different mobility services, which leads to the pooling of regional transport providers

Capturing Power System Dynamics by Physics-Informed Neural Networks and Optimization

This paper proposes a tractable framework to determine key characteristics of non-linear dynamic systems by converting physics-informed neural networks to a mixed integer linear program. Our focus is on power system applications. Traditional methods in power systems require the use of a large number of simulations and other heuristics to determine parameters such as the critical clearing time, i.e. the maximum allowable time within which a disturbance must be cleared before the system moves to instability. The work proposed in this paper uses physics-informed neural networks to capture the power system dynamic behavior and, through an exact transformation, converts them to a tractable optimization problem which can be used to determine critical system indices. By converting neural networks to mixed integer linear programs, our framework also allows to adjust the conservativeness of the neural network output with respect to the existing stability boundaries. We demonstrate the performance of our methods on the non-linear dynamics of converter-based generation in response to voltage disturbances.Comment: 6 pages, 5 figures, submitted to the 60th IEEE conference on Decision and Control (CDC), 2021, Austin, Texas, US