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Auto-Steered Information-Decision Process for Electric Power System Asset Management

A NSF-funded DDDAS Project (www.dddas-isu.org)
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Project Description

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 and triples when including transformers and circuit breakers. Investment in new transmission equipment has significantly declined over the past 15 years. Some of the equipment is well beyond intended life, yet is operated under increasing stress, as load growth, new generation, and economically motivated transmission flows push equipment beyond nameplate limits. Transmission owners face mounting pressures from investors, and while maintenance needs exceed available resources, experienced engineers are lured to early retirement opportunities and are replaced by young engineers trained at universities having no power program. These issues played a significant role in the August 14, 2003 blackout in the eastern US interconnection. At the same time, economic operation, and ultimately electric energy price, is heavily influenced by transmission equipment availability, because transmission forced outages require utilization of more expensive generation. 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.

Our objective 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. We will 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.

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