92 research outputs found

    L-Band System Engineering - Concepts of Use, Systems Performance Requirements, and Architecture

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    This document is being provided as part of ITT s NASA Glenn Research Center Aerospace Communication Systems Technical Support (ACSTS) contract NNC05CA85C, Task 7: New ATM Requirements-Future Communications, C-band and L-band Communications Standard Development. Task 7 was motivated by the five year technology assessment performed for the Federal Aviation Administration (FAA) under the joint FAA-EUROCONTROL cooperative research Action Plan (AP-17), also known as the Future Communications Study (FCS). It was based on direction provided by the FAA project-level agreement (PLA FY09_G1M.02-02v1) for "New ATM Requirements-Future Communications." Task 7 was separated into two distinct subtasks, each aligned with specific work elements and deliverable items. Subtask 7-1 addressed C-band airport surface data communications standards development, systems engineering, test bed development, and tests/demonstrations to establish operational capability for what is now referred to as the Aeronautical Mobile Airport Communications System (AeroMACS). Subtask 7-2, which is the subject of this report, focused on preliminary systems engineering and support of joint FAA/EUROCONTROL development and evaluation of a future L-band (960 to 1164 MHz) air/ground (A/G) communication system known as the L-band digital aeronautical communications system (L-DACS), which was defined during the FCS. The proposed L-DACS will be capable of providing ATM services in continental airspace in the 2020+ timeframe. Subtask 7-2 was performed in two phases. Phase I featured development of Concepts of Use, high level functional analyses, performance of initial L-band system safety and security risk assessments, and development of high level requirements and architectures. It also included the aforementioned support of joint L-DACS development and evaluation, including inputs to L-DACS design specifications. Phase II provided a refinement of the systems engineering activities performed during Phase I, along with continued joint FAA/EUROCONTROL L-DACS development and evaluation support

    Short-Term Load Demand Forecasting For Transnet Port Terminal (Tpt) In East London Using Artificial Neural Network

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    DissertationThe daily and weekly energy consumption patterns at the Transnet Port Terminal (TPT) in East London varies stochastically. This is as a result of the transient weather patterns that exist at the harbor. It has therefore become imperative to wisely manage this load in order to save electricity costs and for future infrastructure development. Hence the ongoing supply of electricity to port consumers requires an accurate and adequate short-term load forecast (STLF) for quality, quantity, and efficient management. Many researchers have recently proposed Artificial Neural Networks for short-term load prediction. However, most of the studies have not considered the quickly changing weather patterns that exist at the port. Therefore, the objective of this study is to establish a supervised short-term load prediction using ANN models, and to verify the effectiveness of such predictions by using the real load data from the TPT. The suggested system architecture uses open- loop training with real load and weather information, and then a closed-loop network is used to produce a prediction with the predicted load as its feedback data. Data collection points were set up in the ring network of the port by installing new power measuring meters, and weather data obtained from local meteorology offices in order to build a suitable alternative of localised data management (data base) for saving all data gathered. Hence, profiling of the load in the TPT was done and load forecasting was carried out, leading to improved load management strategies for the harbor terminal. ANN short-term load prediction (STLP) models were developed utilising its own performance to improve precision by essentially implementing a load feedback loop that is less reliant on external data. To ensure that the timeseries data recorded at the port were well modeled, the Nonlinear autoregressive exogenous model (NARX) for load prediction were developed using mean squared error (MSE) as a performance metric. Furthermore, to show the efficacy of the proposed model for STLP, the adaptive neuro-fuzzy inference system (ANFIS) was used with the same data for short-term predictions. The minimum mean squared errors obtained for both NARX and ANFIS models were 0.0010939 and 0.0032 respectively, indicating that the NARX model is more accurate during the forecast of departmental loads. The results of the predictions using the hourly timeseries indicated a close match between the forecasted and actual load demand at the port terminal. The effects of the load forecast could be used as a guide for implementing management plans for internal load, such as the generation of urgent electricity and the programme of implementation for demand-side management policies

    Control and Optimization of Energy Storage in AC and DC Power Grids

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    Energy storage attracts attention nowadays due to the critical role it will play in the power generation and transportation sectors. Electric vehicles, as moving energy storage, are going to play a key role in the terrestrial transportation sector and help reduce greenhouse emissions. Bulk hybrid energy storage will play another critical role for feeding the new types of pulsed loads on ship power systems. However, to ensure the successful adoption of energy storage, there is a need to control and optimize the charging/discharging process, taking into consideration the customer preferences and the technical aspects. In this dissertation, novel control and optimization algorithms are developed and presented to address the various challenges that arise with the adoption of energy storage in the electricity and transportation sectors. Different decentralized control algorithms are proposed to manage the charging of a mass number of electric vehicles connected to different points of charging in the power distribution system. The different algorithms successfully satisfy the preferences of the customers without negatively impacting the technical constraints of the power grid. The developed algorithms were experimentally verified at the Energy Systems Research Laboratory at FIU. In addition to the charge control of electric vehicles, the optimal allocation and sizing of commercial parking lots are considered. A bi-layer Pareto multi-objective optimization problem is formulated to optimally allocate and size a commercial parking lot. The optimization formulation tries to maximize the profits of the parking lot investor, as well as minimize the losses and voltage deviations for the distribution system operator. Sensitivity analysis to show the effect of the different objectives on the selection of the optimal size and location is also performed. Furthermore, in this dissertation, energy management strategies of the onboard hybrid energy storage for a medium voltage direct current (MVDC) ship power system are developed. The objectives of the management strategies were to maintain the voltage of the MVDC bus, ensure proper power sharing, and ensure proper use of resources, where supercapacitors are used during the transient periods and batteries are used during the steady state periods. The management strategies were successfully validated through hardware in the loop simulation

    Technology Candidates for Air-to-Air and Air-to-Ground Data Exchange

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    Technology Candidates for Air-to-Air and Air-to-Ground Data Exchange is a two-year research effort to visualize the U. S. aviation industry at a point 50 years in the future, and to define potential communication solutions to meet those future data exchange needs. The research team, led by XCELAR, was tasked with identifying future National Airspace System (NAS) scenarios, determining requirements and functions (including gaps), investigating technical and business issues for air, ground, & air-to-ground interactions, and reporting on the results. The project was conducted under technical direction from NASA and in collaboration with XCELAR's partner, National Institute of Aerospace, and NASA technical representatives. Parallel efforts were initiated to define the information exchange functional needs of the future NAS, and specific communication link technologies to potentially serve those needs. Those efforts converged with the mapping of each identified future NAS function to potential enabling communication solutions; those solutions were then compared with, and ranked relative to, each other on a technical basis in a structured analysis process. The technical solutions emerging from that process were then assessed from a business case perspective to determine their viability from a real-world adoption and deployment standpoint. The results of that analysis produced a proposed set of future solutions and most promising candidate technologies. Gap analyses were conducted at two points in the process, the first examining technical factors, and the second as part of the business case analysis. In each case, no gaps or unmet needs were identified in applying the solutions evaluated to the requirements identified. The future communication solutions identified in the research comprise both specific link technologies and two enabling technologies that apply to most or all specific links. As a result, the research resulted in a new analysis approach, viewing the underlying architecture of ground-air and air-air communications as a whole, rather than as simple "link to function" paired solutions. For the business case analysis, a number of "reference architectures" were developed for both the future technologies and the current systems, based on three typical configurations of current aircraft. Current and future costs were assigned, and various comparisons made between the current and future architectures. In general, it was assumed that if a future architecture offers lower cost than the current typical architecture, while delivering equivalent or better performance, it is likely that the future solution will gain industry acceptance. Conversely, future architectures presenting higher costs than their current counterparts must present a compelling benefit case in other areas or risk a lack of industry acceptance. The business case analysis consistently indicated lower costs for the proposed future architectures, and in most cases, significantly so. The proposed future solutions were found to offer significantly greater functionality, flexibility, and growth potential over time, at lower cost, than current systems. This was true for overall, fleet-wide equipage for domestic and oceanic air carriers, as well as for single, General Aviation (GA) aircraft. The overall research results indicate that all identified requirements can be met by the proposed solutions with significant capacity for future growth. Results also illustrate that the majority of the future communication needs can be met using currently allocated aviation RF spectrum, if used in more effective ways than it is today. A combination of such optimized aviation-specific links and commercial communication systems meets all identified needs for the 50-year future and beyond, with the caveat that a new, overall function will be needed to manage all information exchange, individual links, security, cost, and other factors. This function was labeled "Delivery Manager" (DM) within this research. DM employs a distributed client/server architecture, for both airborne and ground communications architectures. Final research results included identifying the most promising candidate technologies for the future system, conclusions and recommendations, and identifying areas where further research should be considered

    Power System Simulation, Control and Optimization

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    This Special Issue “Power System Simulation, Control and Optimization” offers valuable insights into the most recent research developments in these topics. The analysis, operation, and control of power systems are increasingly complex tasks that require advanced simulation models to analyze and control the effects of transformations concerning electricity grids today: Massive integration of renewable energies, progressive implementation of electric vehicles, development of intelligent networks, and progressive evolution of the applications of artificial intelligence

    Essays on Unit Commitment and Interregional Cooperation in Transmission Planning

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    One of the most challenging problems in the power industry is deciding which transmission lines to build. The process of answering this question leads to some very interesting and complex optimization problems. Answers to subsidiary questions about the detail of generator operations to simulate, generator siting, environmental regulations, political boundaries, and the ways in which these factors interact with each other, together inform the decision of building transmission lines. For example, carbon taxes may favor transmission expansion to areas with high levels of renewable energy, and consequently, fast ramp-rate generation may be desired to balance the variable nature of renewable energy sources. Transmission investment decisions can have far-reaching consequences for investors and a host of other entities connected to the electric grid. In addition to being expensive and time-consuming to build, these lines influence other transmission and generation investments, operations, and electricity prices. This work presents a series of essays on the transmission planning problem. There are two main themes: the effects of short-term operations, and the effects of political boundaries, on long-term transmission plans. Contributions of these essays include the following: 1. An alternative formulation of the Unit Commitment (UC) problem that solves faster than the standard UC formulation, and UC approximations that improve computational performance while maintaining high fidelity in the quality of the solution (reduction of binary variables and tightening of constraints). 2. Demonstrating how to bridge the gap between short-term (hours) operational models and long-term (years) transmission and generation co-optimization models, using an application of the U.S. Western Interconnection. 3. Demonstrating that short-term operational constraints have the potential to affect long-term transmission and generator investments. As an example, we find that, when operational constraints such as ramp-rates and minimum-run capacities are considered, transmission investment can sometimes act as a substitute to generation investments. 4. A novel formulation of the noncooperative regional transmission planning problem that shows how regional transmission operators acting in their own self-interest can negatively impact transmission investments. 5. Demonstrating that adjoining transmission operators can both benefit from cooperating with each other in the transmission planning process. Interestingly, we find that it is not enough to focus on seam lines connecting two regions. There are lines internal to each region that have interregional benefits and are identified only though a cooperative planning process. 6. Approximations of the non-cooperative transmission planning model that aid scale-up of this framework to large data-sets, further improving computational performance. Limitations of these models, practical issues involved, and future research directions are discussed in the concluding chapter. Together, these essays illuminate the effects of operational constraints and political boundaries on transmission planning, and encourage decision makers to consider them in their planning processes

    Compressed Air Energy Storage in Offshore Grids

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    Hourly Dispatching Wind-Solar Hybrid Power System with Battery-Supercapacitor Hybrid Energy Storage

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    This dissertation demonstrates a dispatching scheme of wind-solar hybrid power system (WSHPS) for a specific dispatching horizon for an entire day utilizing a hybrid energy storage system (HESS) configured by batteries and supercapacitors. Here, wind speed and solar irradiance are predicted one hour ahead of time using a multilayer perceptron Artificial Neural Network (ANN), which exhibits satisfactory performance with good convergence mapping between input and target output data. Furthermore, multiple state of charge (SOC) controllers as a function of energy storage system (ESS) SOC are developed to accurately estimate the grid reference power (PGrid,ref) for each dispatching period. A low pass filter (LPF) is employed to decouple the power between a battery and a supercapacitor (SC), and the cost optimization of the HESS is computed based on the time constant of the LPF through extensive simulations. Besides, the optimum value of depth of discharge for ESS considering both cycling and calendar expenses has been investigated to optimize the life cycle cost of the ESS, which is vital for minimizing the cost of a dispatchable wind-solar power scheme. Finally, the proposed ESS control algorithm is verified by conducting control hardware-in-the loop (CHIL) experiments in a real-time digital simulator (RTDS) platform

    Fuzzy Logic

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    The capability of Fuzzy Logic in the development of emerging technologies is introduced in this book. The book consists of sixteen chapters showing various applications in the field of Bioinformatics, Health, Security, Communications, Transportations, Financial Management, Energy and Environment Systems. This book is a major reference source for all those concerned with applied intelligent systems. The intended readers are researchers, engineers, medical practitioners, and graduate students interested in fuzzy logic systems
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