Power companies can benefit from the use of knowledge discovery methods and statistical machine learning for preventive maintenance. We introduce a general process for transforming historical electrical grid data into models that aim to predict the risk of failures for components and systems. These models can be used directly by power companies to assist with prioritization of maintenance and repair work. Specialized versions of this process are used to produce 1) feeder failure rankings, 2) cable, joint, terminator, and transformer rankings, 3) feeder Mean Time Between Failure (MTBF) estimates, and 4) manhole events vulnerability rankings. The process in its most general form can handle diverse, noisy, sources that are historical (static), semi-real-time, or realtime, incorporates state-of-the-art machine learning algorithms for prioritization (supervised ranking or MTBF), and includes an evaluation of results via cross-validation and blind test. Above and beyond the ranked lists and MTBF estimates are business management interfaces that allow the prediction capability to be integrated directly into corporate planning and decision support; such interfaces rely on several important properties of our general modeling approach: that machine learning features are meaningful to domain experts, that the processing of data is transparent, and that prediction results are accurate enough to support sound decision making. We discuss the challenges in working with historical electrical grid data that were not designed for predictive purposes. The “rawness” of these data contrasts with the accuracy of the statistical models that can be obtained from the process; these models are sufficiently accurate to assist in maintaining New York City’s electrical grid.

Approximate dynamic programming (ADP) driven adaptive stochastic control (ASC) for the Smart Grid holds the promise of providing the autonomous intelligence required to elevate the electric grid to efficiency and self-healing capabilities more comparable to the internet. To that end, we demonstrate the load and source control necessary to optimize management of distributed generation and storage within the Smart Grid.

Concern for the environment and energy security is changing the way we think about energy. Grid-enabled passenger vehicles, like electric vehicles (EV) and plug-in hybrid electric vehicles (PHEV) can help address environmental and energy issues. Automakers have recognized that electric drive vehicles are critical to the future of the industry. However, some challenges exist to greater adoption: the perception of cost, EV range, access to charging, potential impacts to the grid, and lack of public awareness about the availability and practicality of these vehicles. Although the current initial price for EV's is higher, their operating costs are lower. Policies that reduce the total cost of ownership of EVs and PHEVs, compared to conventional internal combustion engine (ICE) vehicles, will lead to faster market penetration. Greater access to charging infrastructure will also accelerate public adoption. Smart grid technology will optimize the vehicle integration with the grid, allowing intelligent and efficient use of energy. By coordinating efforts and using a systems perspective, the advantages of EVs and PHEVs can be achieved using the least resources. This paper analyzes these factors, their rate of acceleration and how they may synergistically align for the electrification of vehicles.

Ranking problems arise in a wide range of real world applications where an ordering on a set of examples is preferred to a classification model. These applications include collaborative filtering, information retrieval and ranking components of a system by susceptibility to failure. In this paper, we present an ongoing project to rank the underground primary feeders of New York City's electrical grid according to their susceptibility to outages. We describe our framework and the application of machine learning ranking methods, using scores from Support Vector Machines (SVM), RankBoost and Martingale Boosting. Finally, we present our experimental results and the lessons learned from this challenging real-world application.