Co-simulation platform for interconnected power systems and communication networks based on PSS/E and OMNeT++

This paper proposes a co-simulator that combines OMNeT++ for communication systems with PSS/E for the electrical transmission network. The cosimulator applies an event-driven synchronization method that minimizes errors due to delays in the synchronization between both simulators. The synchronization method pauses the simulation of the power system at each communication event, while a supervisory module in PSS/E returns control to the event simulator if any condition from a pre-specified set is met. The proposed co-simulator is demonstrated on a protection system based on peer-to-peer communication and used to evaluate the effect of communication latency times on an online state estimator.This work was supported by the Spanish Agencia Estatal de Investigacion under grant PID2019-104449RB-I0

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Delay-Based Controller Design for Continuous-Time and Hybrid Applications

Motivated by the availability of different types of delays in embedded systems and biological circuits, the objective of this work is to study the benefits that delay can provide in simplifying the implementation of controllers for continuous-time systems. Given a continuous-time linear time-invariant (LTI) controller, we propose three methods to approximate this controller arbitrarily precisely by a simple controller composed of delay blocks, a few integrators and possibly a unity feedback. Different problems associated with the approximation procedures, such as finding the optimal number of delay blocks or studying the robustness of the designed controller with respect to delay values, are then investigated. We also study the design of an LTI continuous-time controller satisfying given control objectives whose delay-based implementation needs the least number of delay blocks. A direct application of this work is in the sampled-data control of a real-time embedded system, where the sampling frequency is relatively high and/or the output of the system is sampled irregularly. Based on our results on delay-based controller design, we propose a digital-control scheme that can implement every continuous-time stabilizing (LTI) controller. Unlike a typical sampled-data controller, the hybrid controller introduced here -— consisting of an ideal sampler, a digital controller, a number of modified second-order holds and possibly a unity feedback -— is robust to sampling jitter and can operate at arbitrarily high sampling frequencies without requiring expensive, high-precision computation

An investigation of techniques to assist with reliable specification and successful simulation of fire field modelling scenarios

Computational fluid dynamics (CFD) based Fire Field Modelling (FFM) codes offer powerful tools for fire safety engineers but their operation requires a high level of skill and an understanding of the mode of operation and limitations, in order to obtain meaningful results in complex scenarios. This problem is compounded by the fact that many FFM cases are barely stable and poor quality set-up can lead to solution failure. There are considerable dangers of misuse of FFM techniques if they are used without adequate knowledge of both the underlying fire science and the associated numerical modelling. CFD modelling can be difficult to set up effectively since there are a number of potential problems: it is not always clear what controls are needed for optimal solution performance, typically there will be no optimal static set of controls for the whole solution period to cover all stages of a complex simulation, there is the generic problem of requiring a high quality mesh - which cannot usually be ascertained until the mesh is actually used for the particular simulation for which it is intended and there are potential handling issues, e.g. for transitional events (and extremes of physical behaviour) which are likely to break the solution process. In order to tackle these key problems, the research described in this thesis has identified and investigated a methodology for analysing, applying and automating a CFD Expert user's knowledge to support various stages of the simulation process - including the key stages of creating a mesh and performing the simulation. This research has also indicated an approach for the control of a FFM CFD simulation which is analogous to the way that a FFM CFD Expert would approach the modelling of a previously unseen scenario. These investigations have led to the identification of a set of requirements and appropriate knowledge which have been instantiated as the, so called, Experiment Engine (EE). This prototype component (which has been built and tested within the SMARTFIRE FFM environment) is capable, both of emulating an Expert users' ability to produce a high quality and appropriate mesh for arbitrary scenarios, and is also able to automatically adjust a key control factor of the solution process. This research has demonstrated that it is possible to emulate an Experts' ability to analyse a series of simulation trials (starting from a simplified, coarse mesh test run) in order to improve subsequent modelling attempts and to improve the scenario specification and/or meshing solution in order to allow the software to recover from a complete solution failure. The research has also shown that it is possible to emulate an Expert user's ability to provide continual run-time control of a simulation and to provide significant benefits in terms of performance, overall reliability and accuracy of the results. The instantiation and testing of the Experiment Engine concept, on a chosen FFM environment - SMARTFIRE, has demonstrated significant performance and stability gains when compared to non Experiment Engine controlled simulations, for a range of complex "real world" fire scenarios. Preliminary tests have shown that the Experiment Engine controlled simulation was generally able to finish the simulations successfully without experiencing any difficulty, even for very complex scenarios, and that the run-time solution control adjustments, made to the time step size by both the Experiment Engine and by the Expert, showed similar trends and responses in reacting to the physical and/or numerical changes in the solution. This was also noticed for transitional events seen during the simulation. It has also been shown that the Experiment Engine (EE) controlled simulation demonstrates a saving of up to 40% of simulation sweeps for complex fire scenarios when compared with non-EE controlled simulations. Analysis of the results has demonstrated that the control technique, deployed by the EE, have no significant impact on the final solution results - hence, the Experiment Engine controlled simulations are able to produce physically sound results, which are almost identical to Expert controlled simulations. The research has investigated a number of new methods and algorithms (e.g. case categorisation, case recognition, block-wise mesh justification, local adaptive mesh refinements, etc.) that are combined into a novel approach to enhance the robustness, efficiency and the ease-of-use of the existing FFM software package. The instantiation of these methods as a prototype control system (within the target FFM environment - SMARTFIRE) has enhanced the software with a valuable tool-set and arguably will make the FFM techniques more accessible and reliable for novice users. The component based design and implementation of the Experiment Engine has proved to be highly robust and flexible. The Experiment Engine (EE) provides a bi­directional communication channel between the existing SMARTFIRE Case Specification Environment and the solution module (the CFD Engine). These key components can now communicate directly via status- and control- messages. In this way, it is possible to maintain the original Case Specification Environment and the CFD Engine processes completely independently. The two components interact with each other when the EE is operating. This componentization has enabled rapid prototyping and implementation of new development requirements (as well as the integration of other support techniques) as they have been identified

1992 NASA/ASEE Summer Faculty Fellowship Program

For the 28th consecutive year, a NASA/ASEE Summer Faculty Fellowship Program was conducted at the Marshall Space Flight Center (MSFC). The program was conducted by the University of Alabama and MSFC during the period June 1, 1992 through August 7, 1992. Operated under the auspices of the American Society for Engineering Education, the MSFC program, was well as those at other centers, was sponsored by the Office of Educational Affairs, NASA Headquarters, Washington, DC. The basic objectives of the programs, which are the 29th year of operation nationally, are (1) to further the professional knowledge of qualified engineering and science faculty members; (2) to stimulate and exchange ideas between participants and NASA; (3) to enrich and refresh the research and teaching activities of the participants' institutions; and (4) to contribute to the research objectives of the NASA centers

Aeronautical Engineering: A special bibliography with indexes, supplement 91, January 1978

This bibliography lists 359 reports, articles, and other documents introduced into the NASA scientific and technical information system in December 1977