DDDAS-TMRP: Auto-Steered Information-Decision Processes for Electric System Asset Management

DDDAS-TMRP：电力系统资产管理的自动引导信息决策流程

• 批准号: 0540293
• 负责人: James McCalley
• 金额: --
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-01-01 至 2010-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0540293&HistoricalAwards=false
• 关键词: DDDAS; TMRP; Auto; Steered; Information

A rough estimate of the numbers of power transformers and circuit breakers comprising the US transmission system (138-765 kV) are 150,000 and 600,000, respectively; in addition, there are 254,000 miles of high voltage transmission lines. Total replacement value of the lines alone (excluding land) is conservatively estimated at over $100 billion dollars [1] and triples when including transformers and circuit breakers. Investment in new transmission equipment has significantly declined over the past 15 years. Maintaining acceptable electric transmission system reliability and delivering electric energy at low energy prices requires innovations in sensing, diagnostics, communications, data management, processing, algorithms, risk-assessment, decision-making (for operations, maintenance, and planning), and process coordination. Among these asset management problems, the data-driven electric power industry has made strides in sensing and diagnostics. Yet there has been less progress in communications, data management, information processing and associated algorithms, risk assessment methods, and decision-making paradigms, and progress has been almost nonexistent in process coordination; it is precisely in these areas that we propose our work. The objective of this project is to develop a hardware-software prototype capable of auto-steering the information-decision cycles inherent to managing operations, maintenance, and planning of the high-voltage electric power transmission systems. In pursuing this objective, we will implement and advance creative approaches to 5 main problems inherent to management of capital-intensive, geographically distributed physical assets: (a) Sensing and communications; (b) Integrating data;(c) Transforming condition measurements to reliability metrics; (d) Developing and linking multi-time scale stochastic decision algorithms; (e) Valuation of information and subsequent sensor deployment. The project will also build a prototype auto-steered information-decision process on top of a novel simulation environment representing the Iowa power system that is driven by, and drives, field installations. The immediate impact will be an increased ability and confidence therein to handle and use the massive data streams associated with owning and operating transmission equipment, resulting in better investment, maintenance, and operating decisions, and ultimately, more economic and more reliable electric delivery of electric power.

据粗略估计，美国输电系统（138-765 kV）的电力变压器和断路器数量分别为150，000和600，000;此外，还有254，000英里的高压输电线路。仅线路的总重置价值（不包括土地）保守估计超过1000亿美元[1]，如果包括变压器和断路器，则会增加两倍。在过去15年中，对新输电设备的投资大幅下降。保持可接受的输电系统可靠性并以低能源价格提供电能需要在传感，诊断，通信，数据管理，处理，算法，风险评估，决策（用于操作，维护和规划）和过程协调方面进行创新。在这些资产管理问题中，数据驱动的电力行业在传感和诊断方面取得了长足的进步。然而，在通信、数据管理、信息处理和相关算法、风险评估方法和决策模式方面的进展较少，在流程协调方面几乎没有进展;我们正是在这些领域提出我们的工作。 本项目的目标是开发一个硬件软件原型，能够自动转向固有的管理操作，维护和规划的高压电力传输系统的信息决策周期。在追求这一目标，我们将实施和推进创造性的方法来管理资本密集型，地理上分散的物理资产所固有的5个主要问题：（a）传感和通信;（B）整合数据;（c）将条件测量转换为可靠性指标;（d）开发和连接多时间尺度的随机决策算法;（e）信息和随后的传感器部署的估值。 该项目还将建立一个原型自动驾驶的信息决策过程上的一个新的模拟环境，代表爱荷华州电力系统，是由驱动器，驱动器，现场安装。直接影响将是提高处理和使用与拥有和操作传输设备相关的大量数据流的能力和信心，从而更好地投资，维护和运营决策，并最终实现更经济和更可靠的电力输送。