Asynchronous Integration of Real-Time Simulators for HIL-based Validation of Smart Grids

• URL: http://arxiv.org/abs/2309.07625v1
• Date: Thu, 14 Sep 2023 11:44:21 GMT
• Title: Asynchronous Integration of Real-Time Simulators for HIL-based Validation of Smart Grids
• Authors: Catalin Gavriluta, Georg Lauss, Thomas I. Strasser, Juan Montoya, Ron Brandl, Panos Kotsampopoulos
• Abstract summary: This paper explores the possibilities that are opened in terms of testing by the integration of a real-time simulator into co-simulation environments. Smart grid applications would typically include a relatively large number of physical devices, software components, as well as communication technology, all working hand in hand.
• Score: 0.08796261172196743
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As the landscape of devices that interact with the electrical grid expands, also the complexity of the scenarios that arise from these interactions increases. Validation methods and tools are typically domain specific and are designed to approach mainly component level testing. For this kind of applications, software and hardware-in-the-loop based simulations as well as lab experiments are all tools that allow testing with different degrees of accuracy at various stages in the development life-cycle. However, things are vastly different when analysing the tools and the methodology available for performing system-level validation. Until now there are no available well-defined approaches for testing complex use cases involving components from different domains. Smart grid applications would typically include a relatively large number of physical devices, software components, as well as communication technology, all working hand in hand. This paper explores the possibilities that are opened in terms of testing by the integration of a real-time simulator into co-simulation environments. Three practical implementations of such systems together with performance metrics are discussed. Two control-related examples are selected in order to show the capabilities of the proposed approach.

Related papers

• POGEMA: A Benchmark Platform for Cooperative Multi-Agent Navigation [76.67608003501479]
  We introduce and specify an evaluation protocol defining a range of domain-related metrics computed on the basics of the primary evaluation indicators. The results of such a comparison, which involves a variety of state-of-the-art MARL, search-based, and hybrid methods, are presented.
  arXiv  Detail & Related papers  (2024-07-20T16:37:21Z)
• LLM for Test Script Generation and Migration: Challenges, Capabilities, and Opportunities [8.504639288314063]
  Test script generation is a vital component of software testing, enabling efficient and reliable automation of repetitive test tasks. Existing generation approaches often encounter limitations, such as difficulties in accurately capturing and reproducing test scripts across diverse devices, platforms, and applications. This paper investigates the application of large language models (LLM) in the domain of mobile application test script generation.
  arXiv  Detail & Related papers  (2023-09-24T07:58:57Z)
• Towards Lightweight Data Integration using Multi-workflow Provenance and Data Observability [0.2517763905487249]
  Integrated data analysis plays a crucial role in scientific discovery, especially in the current AI era. We propose MIDA: an approach for lightweight runtime Multi-workflow Integrated Data Analysis. We show near-zero overhead running up to 100,000 tasks on 1,680 CPU cores on the Summit supercomputer.
  arXiv  Detail & Related papers  (2023-08-17T14:20:29Z)
• TestLab: An Intelligent Automated Software Testing Framework [0.0]
  TestLab is an automated software testing framework that attempts to gather a set of testing methods and automate them using Artificial Intelligence. The first two modules aim to identify vulnerabilities from different perspectives, while the third module enhances traditional automated software testing by automatically generating test cases.
  arXiv  Detail & Related papers  (2023-06-06T11:45:22Z)
• FluidLab: A Differentiable Environment for Benchmarking Complex Fluid Manipulation [80.63838153351804]
  We introduce FluidLab, a simulation environment with a diverse set of manipulation tasks involving complex fluid dynamics. At the heart of our platform is a fully differentiable physics simulator, providing GPU-accelerated simulations and gradient calculations. We propose several domain-specific optimization schemes coupled with differentiable physics.
  arXiv  Detail & Related papers  (2023-03-04T07:24:22Z)
• Realistic simulation of users for IT systems in cyber ranges [63.20765930558542]
  We instrument each machine by means of an external agent to generate user activity. This agent combines both deterministic and deep learning based methods to adapt to different environment. We also propose conditional text generation models to facilitate the creation of conversations and documents.
  arXiv  Detail & Related papers  (2021-11-23T10:53:29Z)
• Data Driven Testing of Cyber Physical Systems [12.93632948681342]
  We propose an approach to automatically generate fault-revealing test cases for CPS. Data collected from an application managing a smart building have been used to learn models of the environment.
  arXiv  Detail & Related papers  (2021-02-23T04:55:10Z)
• Test and Evaluation Framework for Multi-Agent Systems of Autonomous Intelligent Agents [0.0]
  We consider the challenges of developing a unifying test and evaluation framework for complex ensembles of cyber-physical systems with embedded artificial intelligence. We propose a framework that incorporates test and evaluation throughout not only the development life cycle, but continues into operation as the system learns and adapts.
  arXiv  Detail & Related papers  (2021-01-25T21:42:27Z)
• A User's Guide to Calibrating Robotics Simulators [54.85241102329546]
  This paper proposes a set of benchmarks and a framework for the study of various algorithms aimed to transfer models and policies learnt in simulation to the real world. We conduct experiments on a wide range of well known simulated environments to characterize and offer insights into the performance of different algorithms. Our analysis can be useful for practitioners working in this area and can help make informed choices about the behavior and main properties of sim-to-real algorithms.
  arXiv  Detail & Related papers  (2020-11-17T22:24:26Z)
• Integrated Benchmarking and Design for Reproducible and Accessible Evaluation of Robotic Agents [61.36681529571202]
  We describe a new concept for reproducible robotics research that integrates development and benchmarking. One of the central components of this setup is the Duckietown Autolab, a standardized setup that is itself relatively low-cost and reproducible. We validate the system by analyzing the repeatability of experiments conducted using the infrastructure and show that there is low variance across different robot hardware and across different remote labs.
  arXiv  Detail & Related papers  (2020-09-09T15:31:29Z)
• From Simulation to Real World Maneuver Execution using Deep Reinforcement Learning [69.23334811890919]
  Deep Reinforcement Learning has proved to be able to solve many control tasks in different fields, but the behavior of these systems is not always as expected when deployed in real-world scenarios. This is mainly due to the lack of domain adaptation between simulated and real-world data together with the absence of distinction between train and test datasets. We present a system based on multiple environments in which agents are trained simultaneously, evaluating the behavior of the model in different scenarios.
  arXiv  Detail & Related papers  (2020-05-13T14:22:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.